Commit 66db25919d08dcea482610d14149b17414b3d693 - dynare

+1

-0

Makefile.am less more

18	18	EXTRA_DIST = \
19	19	matlab \
20	20	uncrustify.cfg \
	21	NEWS \
21	22	license.txt \
22	23	dynare.el \
23	24	windows/dynare.nsi \

+4

-2

Makefile.in less more

41	41	DIST_COMMON = $(am__configure_deps) $(srcdir)/Makefile.am \
42	42	$(srcdir)/Makefile.in $(top_srcdir)/configure \
43	43	$(top_srcdir)/matlab/dynare_version.m.in \
44		$(top_srcdir)/windows/dynare-version.nsi.in config.guess \
	44	$(top_srcdir)/windows/dynare-version.nsi.in NEWS config.guess \
45	45	config.sub depcomp install-sh missing ylwrap
46	46	ACLOCAL_M4 = $(top_srcdir)/aclocal.m4
47	47	am__aclocal_m4_deps = $(top_srcdir)/m4/ax_blas.m4 \

50	50	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
51	51	$(top_srcdir)/m4/ax_latex_class.m4 \
52	52	$(top_srcdir)/m4/ax_latex_test.m4 \
53		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	53	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
54	54	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
55	55	$(top_srcdir)/configure.ac
56	56	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

166	166	LN_S = @LN_S@
167	167	LTLIBOBJS = @LTLIBOBJS@
168	168	MAKEINFO = @MAKEINFO@
	169	MATLAB = @MATLAB@
169	170	MKDIR_P = @MKDIR_P@
170	171	OBJEXT = @OBJEXT@
171	172	OCTAVE = @OCTAVE@

251	252	EXTRA_DIST = \
252	253	matlab \
253	254	uncrustify.cfg \
	255	NEWS \
254	256	license.txt \
255	257	dynare.el \
256	258	windows/dynare.nsi \

+298

-0

NEWS less more

	0	Announcement for Dynare 4.2.4 (on 2011-12-02)
	1	=============================================
	2
	3	We are pleased to announce the release of Dynare 4.2.4.
	4
	5	This is a bugfix release. It comes only a few days after the previous release,
	6	because version 4.2.3 was affected by a critical bug (see below).
	7
	8	The Windows package for the new release is already available for download at
	9	the official Dynare website <http://www.dynare.org>. The Mac and Linux packages
	10	should follow soon.
	11
	12	All users are strongly encouraged to upgrade, especially those who have
	13	installed the buggy 4.2.3 release.
	14
	15	The new release is compatible with MATLAB versions ranging from 7.0 (R14) to
	16	7.13 (R2011b) and with GNU Octave versions ranging from 3.0 to 3.4.
	17
	18	Here is the list of the problems identified in version 4.2.3 and that have been
	19	fixed in version 4.2.4:
	20
	21	* Second order approximation was broken for most models, giving incorrect
	22	results (this problem only affects version 4.2.3, not previous versions)
	23
	24	* Bayesian priors with inverse gamma distribution and very small variances
	25	were giving incorrect results in some cases
	26
	27	* The `model_diagnostics' command was broken
	28
	29
	30	Announcement for Dynare 4.2.3 (on 2011-11-30)
	31	=============================================
	32
	33	We are pleased to announce the release of Dynare 4.2.3.
	34
	35	This is a bugfix release.
	36
	37	The Windows package is already available for download at the official
	38	Dynare website <http://www.dynare.org>. The Mac and Linux packages
	39	should follow soon.
	40
	41	All users are strongly encouraged to upgrade.
	42
	43	This release is compatible with MATLAB versions ranging from 7.0 (R14)
	44	to 7.13 (R2011b) and with GNU Octave versions ranging from 3.0 to 3.4.
	45
	46	Here is a non-exhaustive list of the problems identified in version 4.2.2 and
	47	that have been fixed in version 4.2.3:
	48
	49	* `steady_state_model' was broken for lags higher than 2
	50
	51	* `simult_.m' was not working correctly with `order=3' if `k_order_solver' had
	52	not been explicitly specified
	53
	54	* `stoch_simul' with `order=3' and without `periods' option was reporting
	55	dummy theoretical moments
	56
	57	* Under Octave, option `solve_algo=0' was causing crashes in `check' and
	58	`stoch_simul'
	59
	60	* Identification module was broken
	61
	62	* The test for singularity in the model reporting eigenvalues close to 0/0 was
	63	sometimes reporting false positives
	64
	65	* The `conditional_variance_decomposition' option was not working if one
	66	period index was 0. Now, Dynare reports an error if the periods are not
	67	strictly positive.
	68
	69	* Second order approximation was buggy if one variable was not present at the
	70	current period
	71
	72
	73	Announcement for Dynare 4.2.2 (on 2011-10-04)
	74	=============================================
	75
	76	We are pleased to announce the release of Dynare 4.2.2.
	77
	78	This is a bugfix release.
	79
	80	The Windows package is already available for download at the official
	81	Dynare website <http://www.dynare.org>. The Mac and Linux packages
	82	should follow soon.
	83
	84	All users are strongly encouraged to upgrade.
	85
	86	This release is compatible with MATLAB versions ranging from 7.0 (R14)
	87	to 7.13 (R2011b) and with GNU Octave versions ranging from 3.0 to 3.4.
	88
	89	Here is a list of the problems identified in version 4.2.1 and that have
	90	been fixed in version 4.2.2:
	91
	92	* The secondary rank test following the order test of the Blanchard and
	93	Kahn condition was faulty and almost never triggered
	94
	95	* The variance prior for BVAR “à la Sims” with only one lag was
	96	inconsistent. The solution implemented consists of adding one extra
	97	observation in the presample used to compute the prior; as a
	98	consequence, the numerical results for all estimations will be
	99	slightly different in future releases (thanks to Marek Jarociński for
	100	spotting this)
	101
	102	* The `conditional_forecast' command was buggy: it was always using the
	103	posterior mode, whatever the value of the `parameter_set' option
	104
	105	* `STEADY_STATE' was not working correctly with certain types of
	106	expressions (the priority of the addition and substraction operators
	107	was incorrectly handled)
	108
	109	* With the `block' option of `model', the preprocessor was failing on
	110	expressions of the form "a^b" (with no endogenous in "a" but an
	111	endogenous in "b")
	112
	113	* Some native MATLAB statements were not correctly passed on to MATLAB
	114	(e.g. x = { 'foo' 'bar' } )
	115
	116	* `external_function' was crashing in some circumstances
	117
	118	* The lambda parameter for HP filter was restricted to integer values
	119	for no good reason
	120
	121	* The `load_mh_file' option of `estimation' was crashing under Octave
	122	for Windows (MinGW version)
	123
	124	* Computation of steady state was failing on model contains auxiliary
	125	variables created by leads or lags larger than 2 or by of the
	126	`EXPECTATION' operator
	127
	128	* Compilation of MEX files for MATLAB was failing with GCC 4.6
	129
	130
	131	Announcement for Dynare 4.2.1 (on 2011-05-24)
	132	=============================================
	133
	134	We are pleased to announce the release of Dynare 4.2.1.
	135
	136	Many bugs have been fixed since the previous release. The reference
	137	manual has also been improved: new contents has been added at various
	138	places, the structure has been improved, an index of functions and
	139	variables has been added, the PDF/HTML rendering has been improved.
	140
	141	The Windows package is already available for download at the official
	142	Dynare website [1]. The Mac and Linux packages should follow soon.
	143
	144	All users are strongly encouraged to upgrade.
	145
	146	This release is compatible with MATLAB versions ranging from 7.0 (R14)
	147	to 7.12 (R2011a) and with GNU Octave versions ranging from 3.0 to 3.4.
	148
	149	Here is a list of the main bugfixes since version 4.2.0:
	150
	151	* The `STEADY_STATE' operator has been fixed
	152
	153	* Problems with MATLAB 7.3 (R2006b) and older have been fixed
	154
	155	* The `partial_information' option of `stoch_simul' has been fixed
	156
	157	* Option `conditional_variance_decomposition' of `stoch_simul' and
	158	`estimation' has been fixed
	159
	160	* Automatic detrending now works in conjunction with the `EXPECTATION'
	161	operator
	162
	163	* Percentage signs inside strings in MATLAB statements (like disp('%
	164	This is not a comment %')) now work
	165
	166	* Beta prior with a very small standard deviation now work even if you
	167	do not have the MATLAB Statistical toolbox
	168
	169	* External functions can now been used in assignment of model local
	170	variables
	171
	172	* `identification' command has been fixed
	173
	174	* Option `cova_compute' of `estimation' command has been fixed
	175
	176	* Random crashes with 3rd order approximation without `use_dll' option
	177	have been eliminated
	178
	179	[1] http://www.dynare.org
	180
	181
	182	Announcement for Dynare 4.2.0 (on 2011-02-15)
	183	=============================================
	184
	185	We are pleased to announce the release of Dynare 4.2.0.
	186
	187	This major release adds new features and fixes various bugs.
	188
	189	The Windows package is already available for download. The Mac and Linux
	190	packages should follow soon.
	191
	192	All users are strongly encouraged to upgrade.
	193
	194	This release is compatible with MATLAB versions ranging from 6.5 (R13) to 7.11
	195	(R2010b) and with GNU Octave versions 3.0.x and 3.2.x (support for GNU Octave
	196	3.4.x is not complete and will be added in the next minor release).
	197
	198	Here is the list of major user-visible changes:
	199
	200	* New solution algorithms:
	201
	202	- Pruning for second order simulations has been added, as described in Kim,
	203	Kim, Schaumburg and Sims (2008) [1,2]
	204
	205	- Models under partial information can be solved, as in Pearlman, Currie and
	206	Levine (1986) [3,4]
	207
	208	- New nonlinear solvers for faster deterministic simulations and steady state
	209	computation [5]
	210
	211	* Dynare can now use the power of multi-core computers or of a cluster of
	212	computer using parallelization [6]
	213
	214	* New features in the user interface:
	215
	216	- A steady state file can now be automatically generated, provided that the
	217	model can be solved analytically, and that the steady state as a function
	218	of the parameters is declared with the new "steady_state_model" command [7]
	219
	220	- For non-stationary models, Dynare is now able of automatically removing
	221	trends in all the equations: the user writes the equations in
	222	non-stationary form and declares the deflator of each variable. Then Dynare
	223	perform a check to determine if the proposed deflators are compatible with
	224	balanced growth path, and, if yes, then it computes the detrended equations
	225	[8]
	226
	227	- It is now possible to use arbitrary functions in the model block [9]
	228
	229	* Other minor changes to the user interface:
	230
	231	- New primitives allowed in model block: normpdf(), erf()
	232
	233	- New syntax for DSGE-VAR [10]
	234
	235	- Syntax of deterministic shocks has changed: after the values keyword,
	236	arbitrary expressions must be enclosed within parentheses (but numeric
	237	constants are still accepted as is)
	238
	239	* Various improvements:
	240
	241	- Third order simulations now work without the "USE_DLL" option:
	242	installing a C++ compiler is no longer necessary for 3rd order
	243
	244	- The HP filter works for empirical moments (previously it was only available
	245	for theoretical moments)
	246
	247	- "ramsey_policy" now displays the planner objective value function under
	248	Ramsey policy and stores it in "oo_.planner_objective_value"
	249
	250	- Estimation: if the "selected_variables_only" option is present, then the
	251	smoother will only be run on variables listed just after the estimation
	252	command
	253
	254	- Estimation: in the "shocks" block, it is now possible to calibrate
	255	measurement errors on endogenous variables (using the same keywords than
	256	for calibrating variance/covariance matrix of exogenous shocks)
	257
	258	- It is possibile to choose the parameter set for shock decomposition [11]
	259
	260	- The diffuse filter now works under Octave
	261
	262	- New option "console" on the Dynare command-line: use it when running Dynare
	263	from the console, it will replace graphical waitbars by text waitbars for
	264	long computations
	265
	266	- Steady option "solve_algo=0" (uses fsolve()) now works under Octave
	267
	268	* For Emacs users:
	269
	270	- New Dynare mode for Emacs editor (contributed by Yannick Kalantzis)
	271
	272	- Reference manual now available in Info format (distributed with
	273	Debian/Ubuntu packages)
	274
	275	* Miscellaneous:
	276
	277	- Deterministic models: leads and lags of two or more on endogenous
	278	variables are now substituted by auxiliary variables; exogenous variables
	279	are left as is [12]
	280
	281	[1] Kim, J., S. Kim, E. Schaumburg and C.A. Sims (2008), "Calculating and using
	282	second-order accurate solutions of discrete time dynamic equilibrium
	283	models", Journal of Economic Dynamics and Control, 32(11), 3397-3414
	284	[2] It is triggered by option "pruning" of "stoch_simul" (only 2nd order, not
	285	available at 3rd order)
	286	[3] Pearlman J., D. Currie and P. Levine (1986), "Rational expectations models
	287	with partial information", Economic Modelling, 3(2), 90-105
	288	[4] http://www.dynare.org/DynareWiki/PartialInformation
	289	[5] http://www.dynare.org/DynareWiki/FastDeterministicSimulationAndSteadyStateComputation
	290	[6] http://www.dynare.org/DynareWiki/ParallelDynare
	291	[7] See the entry for "steady_state_model" in the reference manual for more
	292	details and an example
	293	[8] http://www.dynare.org/DynareWiki/RemovingTrends
	294	[9] http://www.dynare.org/DynareWiki/ExternalFunctions
	295	[10] http://www.dynare.org/DynareWiki/DsgeVar
	296	[11] http://www.dynare.org/DynareWiki/ShockDecomposition
	297	[12] http://www.dynare.org/DynareWiki/AuxiliaryVariables

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aclocal.m4 less more

975	975	m4_include([m4/ax_latex_bibtex_test.m4])
976	976	m4_include([m4/ax_latex_class.m4])
977	977	m4_include([m4/ax_latex_test.m4])
	978	m4_include([m4/ax_matlab.m4])
978	979	m4_include([m4/ax_prog_ln_s.m4])
979	980	m4_include([m4/ax_pthread.m4])
980	981	m4_include([m4/ax_tex_test.m4])

+112

-10

configure less more

0	0	#! /bin/sh
1	1	# Guess values for system-dependent variables and create Makefiles.
2		# Generated by GNU Autoconf 2.68 for dynare 4.2.2.
	2	# Generated by GNU Autoconf 2.68 for dynare 4.2.4.
3	3	#
4	4	#
5	5	# Copyright (C) 1992, 1993, 1994, 1995, 1996, 1998, 1999, 2000, 2001,

556	556	# Identity of this package.
557	557	PACKAGE_NAME='dynare'
558	558	PACKAGE_TARNAME='dynare'
559		PACKAGE_VERSION='4.2.2'
560		PACKAGE_STRING='dynare 4.2.2'
	559	PACKAGE_VERSION='4.2.4'
	560	PACKAGE_STRING='dynare 4.2.4'
561	561	PACKAGE_BUGREPORT=''
562	562	PACKAGE_URL=''
563	563

607	607	ENABLE_ORG_EXPORT_TRUE
608	608	ENABLE_OCTAVE_FALSE
609	609	ENABLE_OCTAVE_TRUE
	610	HAVE_CMD_LINE_MATLAB_FALSE
	611	HAVE_CMD_LINE_MATLAB_TRUE
610	612	ENABLE_MATLAB_FALSE
611	613	ENABLE_MATLAB_TRUE
	614	MATLAB
612	615	subdirs
613	616	PTHREAD_CFLAGS
614	617	PTHREAD_LIBS

775	778	with_lapack
776	779	with_pthread
777	780	enable_matlab
	781	with_matlab
778	782	enable_octave
779	783	enable_org_export
780	784	'

1335	1339	# Omit some internal or obsolete options to make the list less imposing.
1336	1340	# This message is too long to be a string in the A/UX 3.1 sh.
1337	1341	cat <<_ACEOF
1338		\`configure' configures dynare 4.2.2 to adapt to many kinds of systems.
	1342	\`configure' configures dynare 4.2.4 to adapt to many kinds of systems.
1339	1343
1340	1344	Usage: $0 [OPTION]... [VAR=VALUE]...
1341	1345

1405	1409
1406	1410	if test -n "$ac_init_help"; then
1407	1411	case $ac_init_help in
1408		short \| recursive ) echo "Configuration of dynare 4.2.2:";;
	1412	short \| recursive ) echo "Configuration of dynare 4.2.4:";;
1409	1413	esac
1410	1414	cat <<\_ACEOF
1411	1415

1434	1438	--with-blas=<lib> use BLAS library <lib>
1435	1439	--with-lapack=<lib> use LAPACK library <lib>
1436	1440	--without-pthread don't use POSIX threads
	1441	--with-matlab=ARG check for MATLAB [[yes]]
1437	1442
1438	1443	Some influential environment variables:
1439	1444	CC C compiler command

1515	1520	test -n "$ac_init_help" && exit $ac_status
1516	1521	if $ac_init_version; then
1517	1522	cat <<\_ACEOF
1518		dynare configure 4.2.2
	1523	dynare configure 4.2.4
1519	1524	generated by GNU Autoconf 2.68
1520	1525
1521	1526	Copyright (C) 2010 Free Software Foundation, Inc.

2010	2015	This file contains any messages produced by compilers while
2011	2016	running configure, to aid debugging if configure makes a mistake.
2012	2017
2013		It was created by dynare $as_me 4.2.2, which was
	2018	It was created by dynare $as_me 4.2.4, which was
2014	2019	generated by GNU Autoconf 2.68. Invocation command line was
2015	2020
2016	2021	$ $0 $@

2826	2831
2827	2832	# Define the identity of the package.
2828	2833	PACKAGE='dynare'
2829		VERSION='4.2.2'
	2834	VERSION='4.2.4'
2830	2835
2831	2836
2832	2837	cat >>confdefs.h <<_ACEOF

4307	4312	# On mingw32, we don't want dynamic libgcc
4308	4313	# Note that static-libstdc++ is only supported since GCC 4.5 (but generates no error on older versions)
4309	4314	LDFLAGS="$LDFLAGS -static-libgcc -static-libstdc++"
	4315	have_windows="yes"
4310	4316	;;
4311	4317	cygwin)
4312	4318	{ $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: You are compiling for the Cygwin target. This means that the preprocessor will" >&5

4320	4326	# And by default, the AC_PROG_F77 will pick up g77 if it is present (even if gfortran is also here)
4321	4327	F77=gfortran
4322	4328	fi
	4329	have_windows="yes"
4323	4330	;;
4324	4331	esac
4325	4332

8444	8451
8445	8452	subdirs="$subdirs mex/build/matlab"
8446	8453
	8454
	8455	ax_enable_matlab=
	8456
	8457	# Check whether --with-matlab was given.
	8458	if test "${with_matlab+set}" = set; then :
	8459	withval=$with_matlab; case $withval in
	8460	yes \| no)
	8461	# Explicitly enable or disable MATLAB but determine
	8462	# MATLAB prefix automatically.
	8463	ax_enable_matlab=$withval
	8464	;;
	8465	*)
	8466	# Enable MATLAB and use ARG as the MATLAB prefix.
	8467	# ARG must be an existing directory.
	8468	ax_enable_matlab=yes
	8469	MATLAB=`cd "${withval-/}" > /dev/null 2>&1 && pwd`
	8470	if test -z "$MATLAB" ; then
	8471	as_fn_error $? "invalid value '$withval' for --with-matlab" "$LINENO" 5
	8472	fi
	8473	;;
	8474	esac
	8475	fi
	8476
	8477	{ $as_echo "$as_me:${as_lineno-$LINENO}: checking for MATLAB prefix" >&5
	8478	$as_echo_n "checking for MATLAB prefix... " >&6; }
	8479	if ${ax_cv_matlab+:} false; then :
	8480	$as_echo_n "(cached) " >&6
	8481	else
	8482	if test "${MATLAB+set}" = set ; then
	8483	ax_cv_matlab=`cd "${MATLAB-/}" > /dev/null 2>&1 && pwd`
	8484	else
	8485	ax_cv_matlab=
	8486	IFS=${IFS= } ; ax_ifs=$IFS ; IFS=:
	8487	for ax_dir in ${PATH-/opt/bin:/usr/local/bin:/usr/bin:/bin} ; do
	8488	if test -z "$ax_dir" ; then
	8489	ax_dir=.
	8490	fi
	8491	if test -x "$ax_dir/matlab" ; then
	8492	ax_dir=`echo "$ax_dir" \| sed 's,/bin$,,'`
	8493	# Directory sanity check.
	8494	ax_cv_matlab=`cd "${ax_dir-/}" > /dev/null 2>&1 && pwd`
	8495	if test -n "$ax_cv_matlab" ; then
	8496	break
	8497	fi
	8498	fi
	8499	done
	8500	IFS=$ax_ifs
	8501	fi
	8502	if test -z "$ax_cv_matlab" ; then
	8503	ax_cv_matlab="not found"
	8504	fi
	8505	fi
	8506	{ $as_echo "$as_me:${as_lineno-$LINENO}: result: $ax_cv_matlab" >&5
	8507	$as_echo "$ax_cv_matlab" >&6; }
	8508	if test "$ax_cv_matlab" = "not found" ; then
	8509	unset MATLAB
	8510	else
	8511	# Strip trailing dashes.
	8512	MATLAB=`echo "$ax_cv_matlab" \| sed 's,/*$,,'`
	8513	fi
	8514	{ $as_echo "$as_me:${as_lineno-$LINENO}: checking whether to enable MATLAB support" >&5
	8515	$as_echo_n "checking whether to enable MATLAB support... " >&6; }
	8516	if test x$ax_enable_matlab != xno ; then
	8517	if test "${MATLAB+set}" = set && test -d "$MATLAB/extern/include" ; then
	8518	ax_enable_matlab=yes
	8519	elif test x$ax_enable_matlab = x ; then
	8520	ax_enable_matlab=no
	8521	else
	8522	# Fail if MATLAB was explicitly enabled.
	8523	{ $as_echo "$as_me:${as_lineno-$LINENO}: result: failure" >&5
	8524	$as_echo "failure" >&6; }
	8525	as_fn_error $? "check your MATLAB setup" "$LINENO" 5
	8526	fi
	8527	fi
	8528	{ $as_echo "$as_me:${as_lineno-$LINENO}: result: $ax_enable_matlab" >&5
	8529	$as_echo "$ax_enable_matlab" >&6; }
	8530	if test x$ax_enable_matlab = xyes ; then
	8531
	8532	$as_echo "#define HAVE_MATLAB 1" >>confdefs.h
	8533
	8534	fi
	8535
	8536
8447	8537	fi
8448	8538	if test "x$enable_matlab" = "xyes"; then
8449	8539	ENABLE_MATLAB_TRUE=

8451	8541	else
8452	8542	ENABLE_MATLAB_TRUE='#'
8453	8543	ENABLE_MATLAB_FALSE=
	8544	fi
	8545
	8546	if test "x$ax_enable_matlab" = "xyes" -a "x$have_windows" = "x"; then
	8547	HAVE_CMD_LINE_MATLAB_TRUE=
	8548	HAVE_CMD_LINE_MATLAB_FALSE='#'
	8549	else
	8550	HAVE_CMD_LINE_MATLAB_TRUE='#'
	8551	HAVE_CMD_LINE_MATLAB_FALSE=
8454	8552	fi
8455	8553
8456	8554

8848	8946	as_fn_error $? "conditional \"ENABLE_MATLAB\" was never defined.
8849	8947	Usually this means the macro was only invoked conditionally." "$LINENO" 5
8850	8948	fi
	8949	if test -z "${HAVE_CMD_LINE_MATLAB_TRUE}" && test -z "${HAVE_CMD_LINE_MATLAB_FALSE}"; then
	8950	as_fn_error $? "conditional \"HAVE_CMD_LINE_MATLAB\" was never defined.
	8951	Usually this means the macro was only invoked conditionally." "$LINENO" 5
	8952	fi
8851	8953	if test -z "${ENABLE_OCTAVE_TRUE}" && test -z "${ENABLE_OCTAVE_FALSE}"; then
8852	8954	as_fn_error $? "conditional \"ENABLE_OCTAVE\" was never defined.
8853	8955	Usually this means the macro was only invoked conditionally." "$LINENO" 5

9265	9367	# report actual input values of CONFIG_FILES etc. instead of their
9266	9368	# values after options handling.
9267	9369	ac_log="
9268		This file was extended by dynare $as_me 4.2.2, which was
	9370	This file was extended by dynare $as_me 4.2.4, which was
9269	9371	generated by GNU Autoconf 2.68. Invocation command line was
9270	9372
9271	9373	CONFIG_FILES = $CONFIG_FILES

9322	9424	cat >>$CONFIG_STATUS <<_ACEOF \|\| ac_write_fail=1
9323	9425	ac_cs_config="`$as_echo "$ac_configure_args" \| sed 's/^ //; s/[\\""\`\$]/\\\\&/g'`"
9324	9426	ac_cs_version="\\
9325		dynare config.status 4.2.2
	9427	dynare config.status 4.2.4
9326	9428	configured by $0, generated by GNU Autoconf 2.68,
9327	9429	with options \\"\$ac_cs_config\\"
9328	9430

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17	17	dnl along with Dynare. If not, see <http://www.gnu.org/licenses/>.
18	18
19	19	AC_PREREQ([2.61])
20		AC_INIT([dynare], [4.2.2])
	20	AC_INIT([dynare], [4.2.4])
21	21	AC_CONFIG_SRCDIR([preprocessor/DynareMain.cc])
22	22	AM_INIT_AUTOMAKE([-Wall -Werror foreign])
23	23

30	30	# On mingw32, we don't want dynamic libgcc
31	31	# Note that static-libstdc++ is only supported since GCC 4.5 (but generates no error on older versions)
32	32	LDFLAGS="$LDFLAGS -static-libgcc -static-libstdc++"
	33	have_windows="yes"
33	34	;;
34	35	cygwin)
35	36	AC_MSG_WARN([You are compiling for the Cygwin target. This means that the preprocessor will])

40	41	# And by default, the AC_PROG_F77 will pick up g77 if it is present (even if gfortran is also here)
41	42	F77=gfortran
42	43	fi
	44	have_windows="yes"
43	45	;;
44	46	esac
45	47

195	197	AC_ARG_ENABLE([matlab], AS_HELP_STRING([--disable-matlab], [disable compilation of MEX files for MATLAB]), [], [enable_matlab=yes])
196	198	if test "x$enable_matlab" = "xyes"; then
197	199	AC_CONFIG_SUBDIRS([mex/build/matlab])
	200	AX_MATLAB
198	201	fi
199	202	AM_CONDITIONAL([ENABLE_MATLAB], [test "x$enable_matlab" = "xyes"])
	203	AM_CONDITIONAL([HAVE_CMD_LINE_MATLAB], [test "x$ax_enable_matlab" = "xyes" -a "x$have_windows" = "x"])
200	204
201	205	AC_ARG_ENABLE([octave], AS_HELP_STRING([--disable-octave], [disable compilation of MEX files for Octave]), [], [enable_octave=yes])
202	206	if test "x$enable_octave" = "xyes"; then

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45	45	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
46	46	$(top_srcdir)/m4/ax_latex_class.m4 \
47	47	$(top_srcdir)/m4/ax_latex_test.m4 \
48		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	48	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
49	49	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
50	50	$(top_srcdir)/configure.ac
51	51	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

181	181	LN_S = @LN_S@
182	182	LTLIBOBJS = @LTLIBOBJS@
183	183	MAKEINFO = @MAKEINFO@
	184	MATLAB = @MATLAB@
184	185	MKDIR_P = @MKDIR_P@
185	186	OBJEXT = @OBJEXT@
186	187	OCTAVE = @OCTAVE@

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doc/dr.tex less more

20	20	License, Version 1.3 or any later version published by the Free
21	21	Software Foundation; with no Invariant Sections, no Front-Cover
22	22	Texts, and no Back-Cover Texts. A copy of the license can be found
23		at: \url{http://www.gnu.org/licenses/fdl.txt} }
	23	at: \url{http://www.gnu.org/licenses/fdl.txt}
	24	\newline
	25	The author acknowledges funding through the Seventh Framework Programme
	26	for Research (FP7) of the European Commission's Socio-economic Sciences and
	27	Humanities (SSH) Program under grant agreement SSH-CT-2009-225149.}
24	28	}
25	29
26	30	\date{First version: December 2009 \hspace{1cm} This version: April 2011}

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272	272	researchers who devote part of their time to software
273	273	development. Currently the development team of Dynare is composed of
274	274	Stéphane Adjemian (Université du Maine, Gains and Cepremap), Houtan
275		Bastani (Cepremap), Michel Juillard (Banque de France), Frédéric
276		Karamé (Université d'Évry, Epee and Cepremap), Junior Maih (Norges
277		Bank), Ferhat Mihoubi (Université d'Évry, Epee and Cepremap), George
278		Perendia, Marco Ratto (JRC) and Sébastien Villemot (Cepremap and Paris
279		School of Economics). Financial support is provided by Cepremap,
280		Banque de France and DSGE-net (an international research network for
281		DSGE modeling). Increasingly, the developer base is expanding, as
282		tools developed by researchers outside of Cepremap are integrated into
283		Dynare.
	275	Bastani (Cepremap), Michel Juillard (Banque de France), Frédéric Karamé
	276	(Université d'Évry, Epee and Cepremap), Junior Maih (Norges Bank),
	277	Ferhat Mihoubi (Université d'Évry, Epee and Cepremap), George Perendia,
	278	Marco Ratto (JRC) and Sébastien Villemot (Cepremap and Paris School of
	279	Economics). Increasingly, the developer base is expanding, as tools
	280	developed by researchers outside of Cepremap are integrated into
	281	Dynare. Financial support is provided by Cepremap, Banque de France and
	282	DSGE-net (an international research network for DSGE modeling). The
	283	Dynare project also received funding through the Seventh Framework
	284	Programme for Research (FP7) of the European Commission's Socio-economic
	285	Sciences and Humanities (SSH) Program from October 2008 to September
	286	2011 under grant agreement SSH-CT-2009-225149.
284	287
285	288	Interaction between developers and users of Dynare is central to the
286	289	project. A @uref{http://www.dynare.org/phpBB3, web forum} is available

335	338	@node Software requirements
336	339	@section Software requirements
337	340
338		Packaged versions of Dynare are available for Windows XP/Vista,
	341	Packaged versions of Dynare are available for Windows XP/Vista/Seven,
339	342	@uref{http://www.debian.org,Debian GNU/Linux},
340	343	@uref{http://www.ubuntu.com/,Ubuntu} and Mac OS X Leopard/Snow
341	344	Leopard. Dynare should work on other systems, but some compilation
342	345	steps are necessary in that case.
343	346
344		In order to run Dynare, you need at least one of the following:
	347	In order to run Dynare, you need one of the following:
345	348
346	349	@itemize
347	350
348	351	@item
349		MATLAB version 7.0 (R14) or above; note that no toolbox is needed by
350		Dynare,
	352	MATLAB version 7.0 (R14) or above;
351	353
352	354	@item
353	355	GNU Octave version 3.0.0 or above.

355	357
356	358	Some installation instructions for GNU Octave can be found on the
357	359	@uref{http://www.dynare.org/DynareWiki/DynareOctave,Dynare Wiki}.
	360
	361	The following optional extensions are also useful to benefit from extra
	362	features, but are in no way required:
	363
	364	@itemize
	365
	366	@item
	367	If under MATLAB: the optimization toolbox, the statistics toolbox;
	368
	369	@item
	370	If under GNU Octave, the following
	371	@uref{http://octave.sourceforge.net/,Octave-Forge} package: optim.
	372
	373	@end itemize
358	374
359	375	If you plan to use the @code{use_dll} option of the @code{model}
360	376	command, you will need to install the necessary requirements for

2897	2913
2898	2914	@item conditional_variance_decomposition = [@var{INTEGER1} @var{INTEGER2} @dots{}]
2899	2915	Computes a conditional variance decomposition for the specified
2900		period(s). Conditional variances are given by
	2916	period(s). The periods must be strictly positive. Conditional variances are given by
2901	2917	@math{var(y_{t+k}\|t)}. For period 1, the conditional variance
2902	2918	decomposition provides the decomposition of the effects of shocks upon
2903	2919	impact. The results are stored in
2904		@var{oo_.conditional_variance_decomposition}
	2920	@code{oo_.conditional_variance_decomposition}
2905	2921	(@pxref{oo_.conditional_variance_decomposition}).
2906	2922
2907	2923	@item pruning
2908	2924	Discard higher order terms when iteratively computing simulations of
2909		the solution, as in @cite{Schaumburg and Sims (2008)}.
	2925	the solution, as in @cite{Kim, Kim, Schaumburg and Sims (2008)}.
2910	2926
2911	2927	@item partial_information
2912	2928	@anchor{partial_information}

3739	3755
3740	3756	@item optim = (@var{fmincon options})
3741	3757	Can be used to set options for @code{fmincon}, the optimizing function
3742		of MATLAB Optimizaiton toolbox. Use MATLAB's syntax for these
	3758	of MATLAB Optimization toolbox. Use MATLAB's syntax for these
3743	3759	options. Default:
3744	3760	@code{('display','iter','LargeScale','off','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6)}
3745	3761

3976	3992	Variable set by the @code{estimation} command, if it is used with the
3977	3993	@code{filtered_vars} option. Fields are of the form:
3978	3994	@example
3979		@code{oo_.FilteredVariables.@var{MOMENT_NAME}.@var{VARIABLE_NAME}}
	3995	@code{oo_.FilteredVariables.@var{VARIABLE_NAME}}
3980	3996	@end example
3981	3997	@end defvr
3982	3998

3992	4008	Variable set by the @code{estimation} command, if it is used with the
3993	4009	@code{smoother} option. Fields are of the form:
3994	4010	@example
3995		@code{oo_.SmoothedMeasurementErrors.@var{MOMENT_NAME}.@var{VARIABLE_NAME}}
	4011	@code{oo_.SmoothedMeasurementErrors.@var{VARIABLE_NAME}}
3996	4012	@end example
3997	4013	@end defvr
3998	4014

4000	4016	Variable set by the @code{estimation} command, if it is used with the
4001	4017	@code{smoother} option. Fields are of the form:
4002	4018	@example
4003		@code{oo_.SmoothedShocks.@var{MOMENT_NAME}.@var{VARIABLE_NAME}}
	4019	@code{oo_.SmoothedShocks.@var{VARIABLE_NAME}}
4004	4020	@end example
4005	4021	@end defvr
4006	4022

4008	4024	Variable set by the @code{estimation} command, if it is used with the
4009	4025	@code{smoother} option. Fields are of the form:
4010	4026	@example
4011		@code{oo_.SmoothedVariables.@var{MOMENT_NAME}.@var{VARIABLE_NAME}}
	4027	@code{oo_.SmoothedVariables.@var{VARIABLE_NAME}}
4012	4028	@end example
4013	4029	@end defvr
4014	4030

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41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

103	103	LN_S = @LN_S@
104	104	LTLIBOBJS = @LTLIBOBJS@
105	105	MAKEINFO = @MAKEINFO@
	106	MATLAB = @MATLAB@
106	107	MKDIR_P = @MKDIR_P@
107	108	OBJEXT = @OBJEXT@
108	109	OCTAVE = @OCTAVE@

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41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

103	103	LN_S = @LN_S@
104	104	LTLIBOBJS = @LTLIBOBJS@
105	105	MAKEINFO = @MAKEINFO@
	106	MATLAB = @MATLAB@
106	107	MKDIR_P = @MKDIR_P@
107	108	OBJEXT = @OBJEXT@
108	109	OCTAVE = @OCTAVE@

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41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

103	103	LN_S = @LN_S@
104	104	LTLIBOBJS = @LTLIBOBJS@
105	105	MAKEINFO = @MAKEINFO@
	106	MATLAB = @MATLAB@
106	107	MKDIR_P = @MKDIR_P@
107	108	OBJEXT = @OBJEXT@
108	109	OCTAVE = @OCTAVE@

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41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

103	103	LN_S = @LN_S@
104	104	LTLIBOBJS = @LTLIBOBJS@
105	105	MAKEINFO = @MAKEINFO@
	106	MATLAB = @MATLAB@
106	107	MKDIR_P = @MKDIR_P@
107	108	OBJEXT = @OBJEXT@
108	109	OCTAVE = @OCTAVE@

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0		@set UPDATED 30 September 2011
1		@set UPDATED-MONTH September 2011
2		@set EDITION 4.2.2
3		@set VERSION 4.2.2
	0	@set UPDATED 25 November 2011
	1	@set UPDATED-MONTH November 2011
	2	@set EDITION 4.2.4
	3	@set VERSION 4.2.4

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doc/userguide/Makefile.in less more

41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

103	103	LN_S = @LN_S@
104	104	LTLIBOBJS = @LTLIBOBJS@
105	105	MAKEINFO = @MAKEINFO@
	106	MATLAB = @MATLAB@
106	107	MKDIR_P = @MKDIR_P@
107	108	OBJEXT = @OBJEXT@
108	109	OCTAVE = @OCTAVE@

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doc/version.texi less more

0		@set UPDATED 30 September 2011
1		@set UPDATED-MONTH September 2011
2		@set EDITION 4.2.2
3		@set VERSION 4.2.2
	0	@set UPDATED 25 November 2011
	1	@set UPDATED-MONTH November 2011
	2	@set EDITION 4.2.4
	3	@set VERSION 4.2.4

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dynare++/Makefile.in less more

41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

143	143	LN_S = @LN_S@
144	144	LTLIBOBJS = @LTLIBOBJS@
145	145	MAKEINFO = @MAKEINFO@
	146	MATLAB = @MATLAB@
146	147	MKDIR_P = @MKDIR_P@
147	148	OBJEXT = @OBJEXT@
148	149	OCTAVE = @OCTAVE@

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dynare++/doc/Makefile.in less more

41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

103	103	LN_S = @LN_S@
104	104	LTLIBOBJS = @LTLIBOBJS@
105	105	MAKEINFO = @MAKEINFO@
	106	MATLAB = @MATLAB@
106	107	MKDIR_P = @MKDIR_P@
107	108	OBJEXT = @OBJEXT@
108	109	OCTAVE = @OCTAVE@

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dynare++/integ/Makefile.in less more

41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

143	143	LN_S = @LN_S@
144	144	LTLIBOBJS = @LTLIBOBJS@
145	145	MAKEINFO = @MAKEINFO@
	146	MATLAB = @MATLAB@
146	147	MKDIR_P = @MKDIR_P@
147	148	OBJEXT = @OBJEXT@
148	149	OCTAVE = @OCTAVE@

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dynare++/integ/cc/Makefile.in less more

42	42	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	43	$(top_srcdir)/m4/ax_latex_class.m4 \
44	44	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	45	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	46	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	47	$(top_srcdir)/configure.ac
48	48	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

130	130	LN_S = @LN_S@
131	131	LTLIBOBJS = @LTLIBOBJS@
132	132	MAKEINFO = @MAKEINFO@
	133	MATLAB = @MATLAB@
133	134	MKDIR_P = @MKDIR_P@
134	135	OBJEXT = @OBJEXT@
135	136	OCTAVE = @OCTAVE@

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dynare++/integ/src/Makefile.in less more

43	43	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
44	44	$(top_srcdir)/m4/ax_latex_class.m4 \
45	45	$(top_srcdir)/m4/ax_latex_test.m4 \
46		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	46	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
47	47	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
48	48	$(top_srcdir)/configure.ac
49	49	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

128	128	LN_S = @LN_S@
129	129	LTLIBOBJS = @LTLIBOBJS@
130	130	MAKEINFO = @MAKEINFO@
	131	MATLAB = @MATLAB@
131	132	MKDIR_P = @MKDIR_P@
132	133	OBJEXT = @OBJEXT@
133	134	OCTAVE = @OCTAVE@

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42	42	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	43	$(top_srcdir)/m4/ax_latex_class.m4 \
44	44	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	45	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	46	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	47	$(top_srcdir)/configure.ac
48	48	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

124	124	LN_S = @LN_S@
125	125	LTLIBOBJS = @LTLIBOBJS@
126	126	MAKEINFO = @MAKEINFO@
	127	MATLAB = @MATLAB@
127	128	MKDIR_P = @MKDIR_P@
128	129	OBJEXT = @OBJEXT@
129	130	OCTAVE = @OCTAVE@

+2

-1

dynare++/kord/Makefile.in less more

43	43	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
44	44	$(top_srcdir)/m4/ax_latex_class.m4 \
45	45	$(top_srcdir)/m4/ax_latex_test.m4 \
46		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	46	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
47	47	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
48	48	$(top_srcdir)/configure.ac
49	49	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

144	144	LN_S = @LN_S@
145	145	LTLIBOBJS = @LTLIBOBJS@
146	146	MAKEINFO = @MAKEINFO@
	147	MATLAB = @MATLAB@
147	148	MKDIR_P = @MKDIR_P@
148	149	OBJEXT = @OBJEXT@
149	150	OCTAVE = @OCTAVE@

+2

-1

dynare++/parser/cc/Makefile.in less more

42	42	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	43	$(top_srcdir)/m4/ax_latex_class.m4 \
44	44	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	45	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	46	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	47	$(top_srcdir)/configure.ac
48	48	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

144	144	LN_S = @LN_S@
145	145	LTLIBOBJS = @LTLIBOBJS@
146	146	MAKEINFO = @MAKEINFO@
	147	MATLAB = @MATLAB@
147	148	MKDIR_P = @MKDIR_P@
148	149	OBJEXT = @OBJEXT@
149	150	OCTAVE = @OCTAVE@

+2

-1

dynare++/src/Makefile.in less more

43	43	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
44	44	$(top_srcdir)/m4/ax_latex_class.m4 \
45	45	$(top_srcdir)/m4/ax_latex_test.m4 \
46		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	46	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
47	47	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
48	48	$(top_srcdir)/configure.ac
49	49	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

141	141	LN_S = @LN_S@
142	142	LTLIBOBJS = @LTLIBOBJS@
143	143	MAKEINFO = @MAKEINFO@
	144	MATLAB = @MATLAB@
144	145	MKDIR_P = @MKDIR_P@
145	146	OBJEXT = @OBJEXT@
146	147	OCTAVE = @OCTAVE@

+2

-1

dynare++/sylv/Makefile.in less more

41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

143	143	LN_S = @LN_S@
144	144	LTLIBOBJS = @LTLIBOBJS@
145	145	MAKEINFO = @MAKEINFO@
	146	MATLAB = @MATLAB@
146	147	MKDIR_P = @MKDIR_P@
147	148	OBJEXT = @OBJEXT@
148	149	OCTAVE = @OCTAVE@

+2

-1

dynare++/sylv/cc/Makefile.in less more

42	42	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	43	$(top_srcdir)/m4/ax_latex_class.m4 \
44	44	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	45	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	46	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	47	$(top_srcdir)/configure.ac
48	48	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

139	139	LN_S = @LN_S@
140	140	LTLIBOBJS = @LTLIBOBJS@
141	141	MAKEINFO = @MAKEINFO@
	142	MATLAB = @MATLAB@
142	143	MKDIR_P = @MKDIR_P@
143	144	OBJEXT = @OBJEXT@
144	145	OCTAVE = @OCTAVE@

+1

-1

dynare++/sylv/cc/SylvException.cpp less more

25	25	char mes[1500];
26	26	mes[0] = '\0';
27	27	printMessage(mes, 1499);
28		printf(mes);
	28	puts(mes);
29	29	}
30	30
31	31	int SylvException::printMessage(char* str, int maxlen) const

+2

-1

dynare++/sylv/testing/Makefile.in less more

42	42	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	43	$(top_srcdir)/m4/ax_latex_class.m4 \
44	44	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	45	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	46	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	47	$(top_srcdir)/configure.ac
48	48	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

125	125	LN_S = @LN_S@
126	126	LTLIBOBJS = @LTLIBOBJS@
127	127	MAKEINFO = @MAKEINFO@
	128	MATLAB = @MATLAB@
128	129	MKDIR_P = @MKDIR_P@
129	130	OBJEXT = @OBJEXT@
130	131	OCTAVE = @OCTAVE@

+2

-1

dynare++/tl/Makefile.in less more

41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

143	143	LN_S = @LN_S@
144	144	LTLIBOBJS = @LTLIBOBJS@
145	145	MAKEINFO = @MAKEINFO@
	146	MATLAB = @MATLAB@
146	147	MKDIR_P = @MKDIR_P@
147	148	OBJEXT = @OBJEXT@
148	149	OCTAVE = @OCTAVE@

+2

-1

dynare++/tl/cc/Makefile.in less more

42	42	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	43	$(top_srcdir)/m4/ax_latex_class.m4 \
44	44	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	45	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	46	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	47	$(top_srcdir)/configure.ac
48	48	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

137	137	LN_S = @LN_S@
138	138	LTLIBOBJS = @LTLIBOBJS@
139	139	MAKEINFO = @MAKEINFO@
	140	MATLAB = @MATLAB@
140	141	MKDIR_P = @MKDIR_P@
141	142	OBJEXT = @OBJEXT@
142	143	OCTAVE = @OCTAVE@

+2

-1

dynare++/tl/testing/Makefile.in less more

42	42	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	43	$(top_srcdir)/m4/ax_latex_class.m4 \
44	44	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	45	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	46	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	47	$(top_srcdir)/configure.ac
48	48	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

129	129	LN_S = @LN_S@
130	130	LTLIBOBJS = @LTLIBOBJS@
131	131	MAKEINFO = @MAKEINFO@
	132	MATLAB = @MATLAB@
132	133	MKDIR_P = @MKDIR_P@
133	134	OBJEXT = @OBJEXT@
134	135	OCTAVE = @OCTAVE@

+2

-1

dynare++/utils/cc/Makefile.in less more

42	42	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	43	$(top_srcdir)/m4/ax_latex_class.m4 \
44	44	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	45	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	46	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	47	$(top_srcdir)/configure.ac
48	48	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

126	126	LN_S = @LN_S@
127	127	LTLIBOBJS = @LTLIBOBJS@
128	128	MAKEINFO = @MAKEINFO@
	129	MATLAB = @MATLAB@
129	130	MKDIR_P = @MKDIR_P@
130	131	OBJEXT = @OBJEXT@
131	132	OCTAVE = @OCTAVE@

+5

-15

license.txt less more

5	5	Michel Juillard <michel.juillard@mjui.fr>
6	6	Ferhat Mihoubi <fmihoubi@univ-evry.fr>
7	7	George Perendia <george@perendia.orangehome.co.uk>
8		Marco Ratto <marco.ratto@jrc.it>
	8	Marco Ratto <marco.ratto@jrc.ec.europa.eu>
9	9	Sébastien Villemot <sebastien.villemot@ens.fr>
10	10	Source: http://www.dynare.org
11	11

127	127
128	128	Files: doc/dynare.texi doc/.tex doc/.svg doc/.dia doc/.pdf doc/*.bib
129	129	Copyright: 1996-2011 Dynare Team
130		License: GFDL-1.3+
	130	License: GFDL-NIV-1.3+
131	131	Permission is granted to copy, distribute and/or modify this document
132	132	under the terms of the GNU Free Documentation License, Version 1.3 or
133	133	any later version published by the Free Software Foundation; with no

137	137
138	138	Files: doc/userguide/.tex doc/userguide/.bib doc/userguide/*.pdf
139	139	Copyright: 2007-2011 Tommaso Mancini Griffoli
140		License: GFDL-1.3+
	140	License: GFDL-NIV-1.3+
141	141	Permission is granted to copy, distribute and/or modify this document
142	142	under the terms of the GNU Free Documentation License, Version 1.3 or
143	143	any later version published by the Free Software Foundation; with no

145	145	.
146	146	A copy of the license can be found at <http://www.gnu.org/licenses/fdl.txt>
147	147
148		Files: doc/dr.tex
149		Copyright: 2009,2011 Sébastien Villemot
150		License: GFDL-1.3+
151		Permission is granted to copy, distribute and/or modify this document
152		under the terms of the GNU Free Documentation License, Version 1.3 or
153		any later version published by the Free Software Foundation; with no
154		Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
155		.
156		A copy of the license can be found at <http://www.gnu.org/licenses/fdl.txt>
157
158		Files: doc/bvar_a_la_sims.tex
	148	Files: doc/dr.tex doc/bvar_a_la_sims.tex
159	149	Copyright: 2007-2011 Sébastien Villemot
160		License: GFDL-1.3+
	150	License: GFDL-NIV-1.3+
161	151	Permission is granted to copy, distribute and/or modify this document
162	152	under the terms of the GNU Free Documentation License, Version 1.3 or
163	153	any later version published by the Free Software Foundation; with no

+3

-5

m4/ax_mexopts.m4 less more

61	61	ax_mexopts_ok="yes"
62	62	;;
63	63	maci \| maci64)
64		SDKROOT='/Developer/SDKs/MacOSX10.5.sdk'
65		MACOSX_DEPLOYMENT_TARGET='10.5'
	64	SDKROOT='/Developer/SDKs/MacOSX10.6.sdk'
	65	MACOSX_DEPLOYMENT_TARGET='10.6'
66	66	if test "${MATLAB_ARCH}" = "maci"; then
67	67	ARCHS='i386'
68		MATLAB_FFLAGS=''
69	68	else
70	69	ARCHS='x86_64'
71		MATLAB_FFLAGS='-m64'
72	70	fi
73	71	MATLAB_DEFS="$MATLAB_DEFS -DNDEBUG"
74	72	MATLAB_CFLAGS="-fno-common -no-cpp-precomp -arch $ARCHS -isysroot $SDKROOT -mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -fexceptions -O2"
75	73	MATLAB_LDFLAGS="-L$MATLAB/bin/${MATLAB_ARCH} -Wl,-twolevel_namespace -undefined error -arch $ARCHS -Wl,-syslibroot,$SDKROOT -mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -bundle -Wl,-exported_symbols_list,\$(top_srcdir)/mexFunction-MacOSX.map"
76	74	MATLAB_LIBS="-lmx -lmex -lmat -lstdc++ -lmwlapack"
77	75	MATLAB_CXXFLAGS="-fno-common -no-cpp-precomp -fexceptions -arch $ARCHS -isysroot $SDKROOT -mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -O2"
78		MATLAB_FFLAGS="-fexceptions $MATLAB_FFLAGS -fbackslash"
	76	MATLAB_FFLAGS="-fexceptions -fbackslash -arch $ARCHS"
79	77	# Starting from MATLAB 7.5, BLAS and LAPACK are in distinct libraries
80	78	AX_COMPARE_VERSION([$MATLAB_VERSION], [ge], [7.5], [MATLAB_LIBS="${MATLAB_LIBS} -lmwblas"])
81	79	ax_mexopts_ok="yes"

+2

-2

matlab/DsgeLikelihood.m less more

125	125	%------------------------------------------------------------------------------
126	126	[T,R,SteadyState,info] = dynare_resolve('restrict');
127	127
128		if info(1) == 1 \|\| info(1) == 2 \|\| info(1) == 5
	128	if info(1) == 1 \|\| info(1) == 2 \|\| info(1) == 7 \|\| info(1) == 5
129	129	fval = bayestopt_.penalty+1;
130	130	cost_flag = 0;
131	131	return

270	270	% ------------------------------------------------------------------------------
271	271	lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
272	272	fval = (likelihood-lnprior);
273		options_.kalman_algo = kalman_algo;⏎
	273	options_.kalman_algo = kalman_algo;

+5

-0

matlab/conditional_variance_decomposition.m less more

31	31	%
32	32	% You should have received a copy of the GNU General Public License
33	33	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	34
	35	if any(Steps <= 0)
	36	error(['Conditional variance decomposition: All periods must be strictly ' ...
	37	'positive'])
	38	end
34	39
35	40	number_of_state_innovations = ...
36	41	StateSpaceModel.number_of_state_innovations;

+19

-0

matlab/distributions/ig1fun.m less more

	0	function err = ig1fun(nu,mu2,sigma2)
	1	% Copyright (C) 2011 Dynare Team
	2	%
	3	% This file is part of Dynare.
	4	%
	5	% Dynare is free software: you can redistribute it and/or modify
	6	% it under the terms of the GNU General Public License as published by
	7	% the Free Software Foundation, either version 3 of the License, or
	8	% (at your option) any later version.
	9	%
	10	% Dynare is distributed in the hope that it will be useful,
	11	% but WITHOUT ANY WARRANTY; without even the implied warranty of
	12	% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	13	% GNU General Public License for more details.
	14	%
	15	% You should have received a copy of the GNU General Public License
	16	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	17
	18	err = log(2mu2) - log((sigma2+mu2)(nu-2)) + 2*( gammaln(nu/2)-gammaln((nu-1)/2) );

+112

-41

matlab/distributions/inverse_gamma_specification.m less more

0		function [s,nu] = inverse_gamma_specification(mu,sigma,type)
	0	function [s,nu] = inverse_gamma_specification(mu,sigma,type,use_fzero_flag)
	1	% Computes the inverse Gamma hyperparameters from the prior mean and standard deviation.
1	2
2		% function [s,nu] = inverse_gamma_specification(mu,sigma,type)
3		% Specification of the inverse Gamma function parameters
4		% X ~ IG(s,nu)
5		%
6		% INPUTS
7		% mu: expectation
8		% sigma: standard deviation
9		% type=1: inverse Gamma 1
10		% type=2: inverse Gamma 2
	3	%@info:
	4	%! @deftypefn {Function File} {[@var{s}, @var{nu} ]=} colon (@var{mu}, @var{sigma}, @var{type}, @var{use_fzero_flag})
	5	%! @anchor{distributions/inverse_gamma_specification}
	6	%! @sp 1
	7	%! Computes the inverse Gamma (type 1 or 2) hyperparameters from the prior mean (@var{mu}) and standard deviation (@var{sigma}).
	8	%! @sp 2
	9	%! @strong{Inputs}
	10	%! @sp 1
	11	%! @table @ @var
	12	%! @item mu
	13	%! Double scalar, prior mean.
	14	%! @item sigma
	15	%! Positive double scalar, prior standard deviation.
	16	%! @item type
	17	%! Integer scalar equal to one or two, type of the Inverse-Gamma distribution.
	18	%! @item use_fzero_flag
	19	%! Integer scalar equal to 0 (default) or 1. Use (matlab/octave's implementation of) fzero to solve for @var{nu} if equal to 1, use
	20	%! dynare's implementation of the secant method otherwise.
	21	%! @end table
	22	%! @sp 1
	23	%! @strong{Outputs}
	24	%! @sp 1
	25	%! @table @ @var
	26	%! @item s
	27	%! Positive double scalar (greater than two), first hypermarameter of the Inverse-Gamma prior.
	28	%! @item nu
	29	%! Positive double scala, second hypermarameter of the Inverse-Gamma prior.
	30	%! @end table
	31	%! @sp 2
	32	%! @strong{This function is called by:}
	33	%! @sp 1
	34	%! @ref{set_prior}
	35	%! @sp 2
	36	%! @strong{This function calls:}
	37	%! @sp 2
	38	%! @strong{Remark:}
	39	%! The call to the matlab's implementation of the secant method is here for testing purpose and should not be used. This routine fails
	40	%! more often in finding an interval for nu containing a signe change because it expands the interval on both sides and eventually
	41	%! violates the condition nu>2.
	42	%!
	43	%! @end deftypefn
	44	%@eod:
11	45
12		% OUTPUTS
13		% s: shape parameter
14		% nu: scale parameter
15		%
16		% SPECIAL REQUIREMENTS
17		% none
18
19		% Copyright (C) 2003-2010 Dynare Team
	46	% Copyright (C) 2003-2011 Dynare Team
20	47	%
21	48	% This file is part of Dynare.
22	49	%

33	60	% You should have received a copy of the GNU General Public License
34	61	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
35	62
	63	check_solution_flag = 1;
	64
	65	if nargin==3
	66	use_fzero_flag = 0;
	67	end
	68
36	69	sigma2 = sigma^2;
37	70	mu2 = mu^2;
38	71
39		if type == 2; % Inverse Gamma 2
	72	if type == 2; % Inverse Gamma 2
40	73	nu = 2*(2+mu2/sigma2);
41	74	s = 2mu(1+mu2/sigma2);
42		elseif type == 1; % Inverse Gamma 1
43		if sigma2 < Inf;
	75	elseif type == 1; % Inverse Gamma 1
	76	if sigma2 < Inf
44	77	nu = sqrt(2*(2+mu2/sigma2));
45		nu2 = 2*nu;
46		nu1 = 2;
47		err = 2mu2gamma(nu/2)^2-(sigma2+mu2)(nu-2)gamma((nu-1)/2)^2;
48		while abs(nu2-nu1) > 1e-12
49		if err > 0
50		nu1 = nu;
51		if nu < nu2
52		nu = nu2;
	78	if use_fzero_flag
	79	nu = fzero(@(nu)ig1fun(nu,mu2,sigma2),nu);
	80	else
	81	nu2 = 2*nu;
	82	nu1 = 2;
	83	err = ig1fun(nu,mu2,sigma2);
	84	err2 = ig1fun(nu2,mu2,sigma2);
	85	if err2>0 % Too short interval.
	86	while nu2<500 % Shift the interval containing the root.
	87	nu1 = nu2;
	88	nu2 = nu2*1.01;
	89	err2 = ig1fun(nu2,mu2,sigma2);
	90	if err2<0
	91	break
	92	end
	93	end
	94	if err2>0
	95	error('inverse_gamma_specification:: Failed in finding an interval containing a sign change! You should check that the prior variance is not too small compared to the prior mean...');
	96	end
	97	end
	98	% Sove for nu using the secant method.
	99	while abs(nu2-nu1) > 1e-14
	100	if err > 0
	101	nu1 = nu;
	102	if nu < nu2
	103	nu = nu2;
	104	else
	105	nu = 2*nu;
	106	nu2 = nu;
	107	end
53	108	else
54		nu = 2*nu;
55	109	nu2 = nu;
56	110	end
57		else
58		nu2 = nu;
	111	nu = (nu1+nu2)/2;
	112	err = ig1fun(nu,mu2,sigma2);
59	113	end
60		nu = (nu1+nu2)/2;
61		err = 2mu2gamma(nu/2)^2-(sigma2+mu2)(nu-2)gamma((nu-1)/2)^2;
62	114	end
63	115	s = (sigma2+mu2)*(nu-2);
64		else;
	116	if check_solution_flag
	117	if abs(mu-sqrt(s/2)*gamma((nu-1)/2)/gamma(nu/2))>1e-9
	118	error('inverse_gamma_specification:: Failed in solving for the hyperparameters!');
	119	end
	120	if abs(sigma-sqrt(s/(nu-2)-mu^2))>1e-9
	121	error('inverse_gamma_specification:: Failed in solving for the hyperparameters!');
	122	end
	123	end
	124	else
65	125	nu = 2;
66	126	s = 2*mu2/pi;
67		end;
68		else;
	127	end
	128	else
69	129	s = -1;
70	130	nu = -1;
71		end;
	131	end
72	132
73		% 01/18/2004 MJ replaced fsolve with secant
74		% suppressed chck
75		% changed order of output parameters⏎
	133	%@test:1
	134	%$ [s0,nu0] = inverse_gamma_specification(.75,.2,1,0);
	135	%$ [s1,nu1] = inverse_gamma_specification(.75,.2,1,1);
	136	%$ [s3,nu3] = inverse_gamma_specification(.75,.1,1,0);
	137	%$ [s4,nu4] = inverse_gamma_specification(.75,.1,1,1);
	138	%$ % Check the results.
	139	%$ t(1) = dyn_assert(s0,s1,1e-6);
	140	%$ t(2) = dyn_assert(nu0,nu1,1e-6);
	141	%$ t(3) = isnan(s4);
	142	%$ t(4) = isnan(nu4);
	143	%$ t(5) = dyn_assert(s3,16.240907971002265,1e-6);;
	144	%$ t(6) = dyn_assert(nu3,30.368398202624046,1e-6);;
	145	%$ T = all(t);
	146	%@eof:1⏎

+49

-142

matlab/dr1.m less more

48	48	% You should have received a copy of the GNU General Public License
49	49	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
50	50
	51	lead_lag_incidence = M_.lead_lag_incidence;
	52
51	53	info = 0;
52	54
53	55	if M_.maximum_endo_lag == 0 && options_.order > 1

63	65
64	66	xlen = M_.maximum_endo_lead + M_.maximum_endo_lag + 1;
65	67	klen = M_.maximum_endo_lag + M_.maximum_endo_lead + 1;
66		iyv = M_.lead_lag_incidence';
	68	iyv = lead_lag_incidence';
67	69	iyv = iyv(:);
68	70	iyr0 = find(iyv) ;
69	71	it_ = M_.maximum_lag + 1 ;

139	141	klen = M_.maximum_lag + M_.maximum_lead + 1;
140	142	dr.ys = [oo_.steady_state;zeros(M_.exo_nbr,1);multbar];
141	143	oo_.steady_state = dr.ys;
	144	lead_lag_incidence = M_.lead_lag_incidence;
142	145	else
143	146	klen = M_.maximum_lag + M_.maximum_lead + 1;
144	147	iyv = M_.lead_lag_incidence';

206	209	nboth = dr.nboth;
207	210	order_var = dr.order_var;
208	211	nd = size(kstate,1);
209		nz = nnz(M_.lead_lag_incidence);
	212	nz = nnz(lead_lag_incidence);
210	213
211	214	sdyn = M_.endo_nbr - nstatic;
212	215
213		[junk,cols_b,cols_j] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+1, ...
	216	[junk,cols_b,cols_j] = find(lead_lag_incidence(M_.maximum_endo_lag+1, ...
214	217	order_var));
215	218	b = zeros(M_.endo_nbr,M_.endo_nbr);
216	219	b(:,cols_b) = jacobia_(:,cols_j);

255	258	info(2) = temp'*temp;
256	259	end
257	260	if options_.loglinear == 1
258		klags = find(M_.lead_lag_incidence(1,:));
	261	klags = find(lead_lag_incidence(1,:));
259	262	dr.ghx = repmat(1./dr.ys,1,size(dr.ghx,2)).dr.ghx. ...
260	263	repmat(dr.ys(klags),size(dr.ghx,1),1);
261	264	dr.ghu = repmat(1./dr.ys,1,size(dr.ghu,2)).*dr.ghu;

309	312	error('Problem with AIM solver - Try to remove the "aim_solver" option')
310	313	end
311	314	else % use original Dynare solver
312		k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_endo_lag+1),:);
	315	k1 = lead_lag_incidence(find([1:klen] ~= M_.maximum_endo_lag+1),:);
313	316	a = aa(:,nonzeros(k1'));
314	317	b(:,cols_b) = aa(:,cols_j);
315	318	b10 = b(1:nstatic,1:nstatic);

351	354	mexErrCheck('mjdgges', err);
352	355
353	356	if info1
354		info(1) = 2;
355		info(2) = info1;
	357	if info1 == -30
	358	info(1) = 7;
	359	else
	360	info(1) = 2;
	361	info(2) = info1;
	362	info(3) = size(e,2);
	363	end
356	364	return
357	365	end
358	366

366	374	% lambda = qz(A,B) won't return infinite eigenvalues
367	375	if ~exist('OCTAVE_VERSION')
368	376	dr.eigval = eig(e,d);
369		end
370		for i=1:nd
371		if abs(ss(i,i)) < 1e-6 && abs(tt(i,i)) < 1e-6
372		info(1) = 7;
373		end
374	377	end
375	378	return
376	379	end

472	475
473	476	%exogenous deterministic variables
474	477	if M_.exo_det_nbr > 0
475		f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
476		f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
	478	f1 = sparse(jacobia_(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
	479	f0 = sparse(jacobia_(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
477	480	fudet = sparse(jacobia_(:,nz+M_.exo_nbr+1:end));
478	481	M1 = inv(f0+[zeros(M_.endo_nbr,nstatic) f1*gx zeros(M_.endo_nbr,nyf-nboth)]);
479	482	M2 = M1*f1;

489	492	end
490	493
491	494	% Second order
492		%tempex = oo_.exo_simul ;
493
494		%hessian = real(hessext('ff1_',[z; oo_.exo_steady_state]))' ;
495		kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
496		if M_.maximum_endo_lag > 0
497		kk = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); kk];
498		end
499		kk = kk';
500		kk = find(kk(:));
501		nk = size(kk,1) + M_.exo_nbr + M_.exo_det_nbr;
502		k1 = M_.lead_lag_incidence(:,order_var);
503		k1 = k1';
504		k1 = k1(:);
505		k1 = k1(kk);
506		k2 = find(k1);
507		kk1(k1(k2)) = k2;
508		kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr+M_.exo_det_nbr];
509		kk = reshape([1:nk^2],nk,nk);
510		kk1 = kk(kk1,kk1);
511		%[junk,junk,hessian] = feval([M_.fname '_dynamic'],z, oo_.exo_steady_state);
512		hessian(:,kk1(:)) = hessian1;
	495	k1 = nonzeros(lead_lag_incidence(:,order_var)');
	496	kk = [k1; length(k1)+(1:M_.exo_nbr+M_.exo_det_nbr)'];
	497	nk = size(kk,1);
	498	kk1 = reshape([1:nk^2],nk,nk);
	499	kk1 = kk1(kk,kk);
	500	hessian = hessian1(:,kk1(:));
513	501	clear hessian1
514	502
515		%oo_.exo_simul = tempex ;
516		%clear tempex
517
518		n1 = 0;
519		n2 = np;
520	503	zx = zeros(np,np);
521	504	zu=zeros(np,M_.exo_nbr);
522		for i=2:M_.maximum_endo_lag+1
523		k1 = sum(kstate(:,2) == i);
524		zx(n1+1:n1+k1,n2-k1+1:n2)=eye(k1);
525		n1 = n1+k1;
526		n2 = n2-k1;
527		end
528		kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
	505	zx(1:np,:)=eye(np);
529	506	k0 = [1:M_.endo_nbr];
530	507	gx1 = dr.ghx;
531	508	hu = dr.ghu(nstatic+[1:npred],:);
532		zx = [zx; gx1];
533		zu = [zu; dr.ghu];
534		for i=1:M_.maximum_endo_lead
535		k1 = find(kk(i+1,k0) > 0);
536		zu = [zu; gx1(k1,1:npred)*hu];
537		gx1 = gx1(k1,:)*hx;
538		zx = [zx; gx1];
539		kk = kk(:,k0);
540		k0 = k1;
541		end
	509	k0 = find(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)');
	510	zx = [zx; gx1(k0,:)];
	511	zu = [zu; dr.ghu(k0,:)];
	512	k1 = find(lead_lag_incidence(M_.maximum_endo_lag+2,order_var)');
	513	zu = [zu; gx1(k1,:)*hu];
	514	zx = [zx; gx1(k1,:)*hx];
542	515	zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
543	516	zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
544	517	[nrzx,nczx] = size(zx);

549	522
550	523	%lhs
551	524	n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
552		A = zeros(n,n);
553		B = zeros(n,n);
554		A(1:M_.endo_nbr,1:M_.endo_nbr) = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
	525	A = zeros(M_.endo_nbr,M_.endo_nbr);
	526	B = zeros(M_.endo_nbr,M_.endo_nbr);
	527	A(:,k0) = jacobia_(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
555	528	% variables with the highest lead
556		k1 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead+1);
557		if M_.maximum_endo_lead > 1
558		k2 = find(kstate(:,2) == M_.maximum_endo_lag+M_.maximum_endo_lead);
559		[junk,junk,k3] = intersect(kstate(k1,1),kstate(k2,1));
560		else
561		k2 = [1:M_.endo_nbr];
562		k3 = kstate(k1,1);
563		end
	529	k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
564	530	% Jacobian with respect to the variables with the highest lead
565		B(1:M_.endo_nbr,end-length(k2)+k3) = jacobia_(:,kstate(k1,3)+M_.endo_nbr);
	531	fyp = jacobia_(:,kstate(k1,3)+nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)));
	532	B(:,nstatic+npred-dr.nboth+1:end) = fyp;
566	533	offset = M_.endo_nbr;
567		k0 = [1:M_.endo_nbr];
568	534	gx1 = dr.ghx;
569		for i=1:M_.maximum_endo_lead-1
570		k1 = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
571		[k2,junk,k3] = find(kstate(k1,3));
572		A(1:M_.endo_nbr,offset+k2) = jacobia_(:,k3+M_.endo_nbr);
573		n1 = length(k1);
574		A(offset+[1:n1],nstatic+[1:npred]) = -gx1(kstate(k1,1),1:npred);
575		gx1 = gx1*hx;
576		A(offset+[1:n1],offset+[1:n1]) = eye(n1);
577		n0 = length(k0);
578		E = eye(n0);
579		if i == 1
580		[junk,junk,k4]=intersect(kstate(k1,1),[1:M_.endo_nbr]);
581		else
582		[junk,junk,k4]=intersect(kstate(k1,1),kstate(k0,1));
583		end
584		i1 = offset-n0+n1;
585		B(offset+[1:n1],offset-n0+[1:n0]) = -E(k4,:);
586		k0 = k1;
587		offset = offset + n1;
588		end
589	535	[junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
590	536	A(1:M_.endo_nbr,nstatic+1:nstatic+npred)=...
591		A(1:M_.endo_nbr,nstatic+[1:npred])+jacobia_(:,k2)*gx1(k1,1:npred);
	537	A(1:M_.endo_nbr,nstatic+[1:npred])+fyp*gx1(k1,1:npred);
592	538	C = hx;
593	539	D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
594	540

599	545	%ghxu
600	546	%rhs
601	547	hu = dr.ghu(nstatic+1:nstatic+npred,:);
602		%kk = reshape([1:np*np],np,np);
603		%kk = kk(1:npred,1:npred);
604		%rhs = -hessiankron(zx,zu)-f1dr.ghxx(end-nyf+1:end,kk(:))*kron(hx(1:npred,:),hu(1:npred,:));
605
606	548	[err, rhs] = sparse_hessian_times_B_kronecker_C(hessian,zx,zu,options_.threads.kronecker.sparse_hessian_times_B_kronecker_C);
607	549	mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
608	550
609		nyf1 = sum(kstate(:,2) == M_.maximum_endo_lag+2);
610	551	hu1 = [hu;zeros(np-npred,M_.exo_nbr)];
611		%B1 = [B(1:M_.endo_nbr,:);zeros(size(A,1)-M_.endo_nbr,size(B,2))];
612	552	[nrhx,nchx] = size(hx);
613	553	[nrhu1,nchu1] = size(hu1);
614	554

623	563
624	564	%ghuu
625	565	%rhs
626		kk = reshape([1:np*np],np,np);
627		kk = kk(1:npred,1:npred);
628
629	566	[err, rhs] = sparse_hessian_times_B_kronecker_C(hessian,zu,options_.threads.kronecker.sparse_hessian_times_B_kronecker_C);
630	567	mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
631	568

638	575
639	576	dr.ghxx = dr.ghxx(1:M_.endo_nbr,:);
640	577	dr.ghxu = dr.ghxu(1:M_.endo_nbr,:);
641		dr.ghuu = dr.ghuu(1:M_.endo_nbr,:);
	578	rdr.ghuu = dr.ghuu(1:M_.endo_nbr,:);
642	579
643	580
644	581	% dr.ghs2

646	583	% reordering predetermined variables in diminishing lag order
647	584	O1 = zeros(M_.endo_nbr,nstatic);
648	585	O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-npred);
649		LHS = jacobia_(:,M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var));
	586	LHS = zeros(M_.endo_nbr,M_.endo_nbr);
	587	LHS(:,k0) = jacobia_(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
650	588	RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
651	589	kk = find(kstate(:,2) == M_.maximum_endo_lag+2);
652	590	gu = dr.ghu;

654	592	Gu = [dr.ghu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr)];
655	593	Guu = [dr.ghuu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr*M_.exo_nbr)];
656	594	E = eye(M_.endo_nbr);
657		M_.lead_lag_incidenceordered = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_endo_lag+1:end,order_var)),1));
658		if M_.maximum_endo_lag > 0
659		M_.lead_lag_incidenceordered = [cumsum(M_.lead_lag_incidence(1:M_.maximum_endo_lag,order_var),1); M_.lead_lag_incidenceordered];
660		end
661		M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered';
662		M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered(:);
663		k1 = find(M_.lead_lag_incidenceordered);
664		M_.lead_lag_incidenceordered(k1) = [1:length(k1)]';
665		M_.lead_lag_incidenceordered =reshape(M_.lead_lag_incidenceordered,M_.endo_nbr,M_.maximum_endo_lag+M_.maximum_endo_lead+1)';
666	595	kh = reshape([1:nk^2],nk,nk);
667	596	kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
668	597	E1 = [eye(npred); zeros(kp-npred,npred)];
669	598	H = E1;
670	599	hxx = dr.ghxx(nstatic+[1:npred],:);
671		for i=1:M_.maximum_endo_lead
672		for j=i:M_.maximum_endo_lead
673		[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+j+1,order_var));
674		[junk,k3a,k3] = ...
675		find(M_.lead_lag_incidenceordered(M_.maximum_endo_lag+j+1,:));
676		nk3a = length(k3a);
677		[err, B1] = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k3a,:),options_.threads.kronecker.sparse_hessian_times_B_kronecker_C);
678		mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
679		RHS = RHS + jacobia_(:,k2)*guu(k2a,:)+B1;
680		end
681		% LHS
682		[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+i+1,order_var));
683		LHS = LHS + jacobia_(:,k2)(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)H O2(k2a,:)]);
684
685		if i == M_.maximum_endo_lead
686		break
687		end
688
689		kk = find(kstate(:,2) == M_.maximum_endo_lag+i+1);
690		gu = dr.ghx*Gu;
691		[nrGu,ncGu] = size(Gu);
692		[err, G1] = A_times_B_kronecker_C(dr.ghxx,Gu,options_.threads.kronecker.A_times_B_kronecker_C);
693		mexErrCheck('A_times_B_kronecker_C', err);
694		[err, G2] = A_times_B_kronecker_C(hxx,Gu,options_.threads.kronecker.A_times_B_kronecker_C);
695		mexErrCheck('A_times_B_kronecker_C', err);
696		guu = dr.ghx*Guu+G1;
697		Gu = hx*Gu;
698		Guu = hx*Guu;
699		Guu(end-npred+1:end,:) = Guu(end-npred+1:end,:) + G2;
700		H = E1 + hx*H;
701		end
	600	[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
	601	k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:dr.nsfwrd)';
	602	[err, B1] = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k2a,:),options_.threads.kronecker.sparse_hessian_times_B_kronecker_C);
	603	mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
	604	RHS = RHS + jacobia_(:,k2)*guu(k2a,:)+B1;
	605
	606	% LHS
	607	LHS = LHS + jacobia_(:,k2)(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)H O2(k2a,:)]);
	608
702	609	RHS = RHS*M_.Sigma_e(:);
703	610	dr.fuu = RHS;
704	611	%RHS = -RHS-dr.fbias;

+13

-19

matlab/draw_prior_density.m less more

44	44	density = @(x,a,b,aa,bb) betapdf((x-aa)/(bb-aa), a, b)/(bb-aa);
45	45	infbound = betainv(truncprior,p6(indx),p7(indx))*(p4(indx)-p3(indx))+p3(indx);
46	46	supbound = betainv(1-truncprior,p6(indx),p7(indx))*(p4(indx)-p3(indx))+p3(indx);
47		stepsize = (supbound-infbound)/steps;
48		abscissa = infbound:stepsize:supbound;
	47	abscissa = linspace(infbound,supbound,steps);
49	48	dens = density(abscissa,p6(indx),p7(indx),p3(indx),p4(indx));
50	49	case 2% Generalized Gamma prior
51	50	density = @(x,a,b,c) gampdf(x-c,a,b);

60	59	rethrow(lasterror)
61	60	end
62	61	end
63		stepsize = (supbound-infbound)/steps;
64		abscissa = infbound:stepsize:supbound;
	62	abscissa = linspace(infbound,supbound,steps);
65	63	dens = density(abscissa,p6(indx),p7(indx),p3(indx));
66	64	case 3% Gaussian prior
67	65	infbound = norminv(truncprior,p6(indx),p7(indx));
68	66	supbound = norminv(1-truncprior,p6(indx),p7(indx));
69		stepsize = (supbound-infbound)/steps;
70		abscissa = infbound:stepsize:supbound;
	67	abscissa = linspace(infbound,supbound,steps);
71	68	dens = normpdf(abscissa,p6(indx),p7(indx));
72	69	case 4% Inverse-gamma of type 1 prior
73	70	try

81	78	rethrow(lasterror)
82	79	end
83	80	end
84		stepsize = (supbound-infbound)/steps;
85		abscissa = infbound:stepsize:supbound;
	81	abscissa = linspace(infbound,supbound,steps);
86	82	dens = exp(lpdfig1(abscissa-p3(indx),p6(indx),p7(indx)));
87	83	case 5% Uniform prior
88	84	infbound = p6(indx);
89	85	supbound = p7(indx);
90		stepsize = (supbound-infbound)/steps;
91		abscissa = infbound:stepsize:supbound;
	86	abscissa = linspace(infbound,supbound,steps);
92	87	dens = ones(1, steps) / (supbound-infbound);
93	88	case 6% Inverse-gamma of type 2 prior
94	89	try

102	97	rethrow(lasterror)
103	98	end
104	99	end
105		stepsize = (supbound-infbound)/steps ;
106		abscissa = infbound:stepsize:supbound;
	100	abscissa = linspace(infbound,supbound,steps);
107	101	dens = exp(lpdfig2(abscissa-p3(indx),p6(indx),p7(indx)));
108	102	otherwise
109	103	error(sprintf('draw_prior_density: unknown distribution shape (index %d, type %d)', indx, pshape(indx)));
110	104	end
111	105
112		k = [1:length(dens)];
113	106	if pshape(indx) ~= 5
114	107	[junk,k1] = max(dens);
115	108	if k1 == 1 \|\| k1 == length(dens)
116		k = find(dens < 10);
	109	k = find(dens > 10);
	110	dens(k) = NaN;
117	111	end
118	112	end
119		binf = abscissa(k(1));
120		bsup = abscissa(k(length(k)));
121		x = abscissa(k);
122		f = dens(k);
	113	binf = abscissa(1);
	114	bsup = abscissa(end);
	115	x = abscissa;
	116	f = dens;
123	117	f(find(x<bayestopt_.lb(indx)))=0;
124		f(find(x>bayestopt_.ub(indx)))=0;⏎
	118	f(find(x>bayestopt_.ub(indx)))=0;

+62

-0

matlab/dyn_waitbar.m less more

	0	function h = dyn_waitbar(prctdone, varargin)
	1	% h = dyn_waitbar(prctdone, varargin)
	2	% adaptive waitbar, producing console mode waitbars with
	3	% octave and when console_mode=1
	4
	5	%
	6	% Copyright (C) 2011 Dynare Team
	7	%
	8	% This file is part of Dynare.
	9	%
	10	% Dynare is free software: you can redistribute it and/or modify
	11	% it under the terms of the GNU General Public License as published by
	12	% the Free Software Foundation, either version 3 of the License, or
	13	% (at your option) any later version.
	14	%
	15	% Dynare is distributed in the hope that it will be useful,
	16	% but WITHOUT ANY WARRANTY; without even the implied warranty of
	17	% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	18	% GNU General Public License for more details.
	19	%
	20	% You should have received a copy of the GNU General Public License
	21	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	22	global options_
	23	persistent running_text newString
	24
	25	if prctdone==0,
	26	init=1;
	27	else
	28	init=0;
	29	end
	30
	31	if exist('OCTAVE_VERSION') \|\| options_.console_mode,
	32
	33	if nargout,
	34	h=[];
	35	end
	36
	37	if init,
	38	diary off;
	39	running_text = varargin{1};
	40	newString='';
	41	return;
	42	elseif nargin>2,
	43	running_text = varargin{2};
	44	end
	45
	46	if exist('OCTAVE_VERSION'),
	47	printf([running_text,' %3.f%% done\r'], prctdone*100);
	48	else
	49	s0=repmat('\b',1,length(newString));
	50	newString=sprintf([running_text,' %3.f%% done'], prctdone*100);
	51	fprintf([s0,'%s'],newString);
	52	end
	53
	54	else
	55	if nargout,
	56	h = waitbar(prctdone,varargin{:});
	57	else
	58	waitbar(prctdone,varargin{:});
	59	end
	60	end
	61

+31

-0

matlab/dyn_waitbar_close.m less more

	0	function dyn_waitbar_close(h)
	1	% h = dyn_waitbar_close(h)
	2	% adaptive close waitbar, compatible with
	3	% octave and when console_mode=1
	4
	5	%
	6	% Copyright (C) 2011 Dynare Team
	7	%
	8	% This file is part of Dynare.
	9	%
	10	% Dynare is free software: you can redistribute it and/or modify
	11	% it under the terms of the GNU General Public License as published by
	12	% the Free Software Foundation, either version 3 of the License, or
	13	% (at your option) any later version.
	14	%
	15	% Dynare is distributed in the hope that it will be useful,
	16	% but WITHOUT ANY WARRANTY; without even the implied warranty of
	17	% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	18	% GNU General Public License for more details.
	19	%
	20	% You should have received a copy of the GNU General Public License
	21	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	22	global options_
	23
	24	if exist('OCTAVE_VERSION') \|\| options_.console_mode,
	25	clear dyn_waitbar;
	26	diary on,
	27	fprintf('\n');
	28	else
	29	close(h),
	30	end

+22

-1

matlab/dynare_config.m less more

51	51	addpath([dynareroot '/missing/rows_columns'])
52	52	% Replacement for vec() (inexistent under MATLAB)
53	53	addpath([dynareroot '/missing/vec'])
54		if ~license('test', 'statistics_toolbox')
	54	if isempty(license('inuse','statistics_toolbox'))
55	55	% Replacements for functions of the stats toolbox
56	56	addpath([dynareroot '/missing/stats/'])
57	57	end

115	115	if exist(mexpath, 'dir')
116	116	addpath(mexpath)
117	117	end
	118	end
	119	end
	120
	121	if strcmp(computer, 'MACI')
	122	if matlab_ver_less_than('7.5')
	123	mexpath = [dynareroot '../mex/matlab/osx32-7.4'];
	124	if exist(mexpath, 'dir')
	125	addpath(mexpath)
	126	end
	127	else
	128	mexpath = [dynareroot '../mex/matlab/osx32-7.5-7.13'];
	129	if exist(mexpath, 'dir')
	130	addpath(mexpath)
	131	end
	132	end
	133	end
	134
	135	if strcmp(computer, 'MACI64')
	136	mexpath = [dynareroot '../mex/matlab/osx64'];
	137	if exist(mexpath, 'dir')
	138	addpath(mexpath)
118	139	end
119	140	end
120	141

+43

-75

matlab/dynare_estimation_1.m less more

944	944	fprintf(fidTeX,' \n');
945	945	end
946	946	if nbplt == 1
947		hh = figure('Name','Smoothed shocks');
	947	if options_.nograph
	948	hh = figure('Name','Smoothed shocks','Visible','off');
	949	else
	950	hh = figure('Name','Smoothed shocks');
	951	end
948	952	NAMES = [];
949	953	if options_.TeX, TeXNAMES = []; end
950	954	for i=1:M_.exo_nbr

980	984	eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(1)]);
981	985	saveas(hh,[M_.fname '_SmoothedShocks' int2str(1) '.fig']);
982	986	end
983		if options_.nograph, close(hh), end
984	987	if options_.TeX
985	988	fprintf(fidTeX,'\\begin{figure}[H]\n');
986	989	for jj = 1:M_.exo_nbr

997	1000	end
998	1001	else
999	1002	for plt = 1:nbplt-1
1000		hh = figure('Name','Smoothed shocks');
	1003	if options_.nograph
	1004	hh = figure('Name','Smoothed shocks','Visible','off');
	1005	else
	1006	hh = figure('Name','Smoothed shocks');
	1007	end
1001	1008	set(0,'CurrentFigure',hh)
1002	1009	NAMES = [];
1003	1010	if options_.TeX, TeXNAMES = []; end

1035	1042	eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(plt)]);
1036	1043	saveas(hh,[M_.fname '_SmoothedShocks' int2str(plt) '.fig']);
1037	1044	end
1038		if options_.nograph, close(hh), end
1039	1045	if options_.TeX
1040	1046	fprintf(fidTeX,'\\begin{figure}[H]\n');
1041	1047	for jj = 1:nstar

1049	1055	fprintf(fidTeX,'\n');
1050	1056	end
1051	1057	end
1052		hh = figure('Name','Smoothed shocks');
	1058	if options_.nograph
	1059	hh = figure('Name','Smoothed shocks','Visible','off');
	1060	else
	1061	hh = figure('Name','Smoothed shocks');
	1062	end
1053	1063	set(0,'CurrentFigure',hh)
1054	1064	NAMES = [];
1055	1065	if options_.TeX, TeXNAMES = []; end

1091	1101	eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(nbplt)]);
1092	1102	saveas(hh,[M_.fname '_SmoothedShocks' int2str(nbplt) '.fig']);
1093	1103	end
1094		if options_.nograph, close(hh), end
1095	1104	if options_.TeX
1096	1105	fprintf(fidTeX,'\\begin{figure}[H]\n');
1097	1106	for jj = 1:size(NAMES,1);

1127	1136	number_of_plots_to_draw = 0;
1128	1137	index = [];
1129	1138	for i=1:n_varobs
1130		if max(abs(measurement_error(10:end))) > 0.000000001
	1139	if max(abs(measurement_error)) > 0.000000001
1131	1140	number_of_plots_to_draw = number_of_plots_to_draw + 1;
1132	1141	index = cat(1,index,i);
1133	1142	end

1142	1151	fprintf(fidTeX,' \n');
1143	1152	end
1144	1153	if nbplt == 1
1145		hh = figure('Name','Smoothed observation errors');
	1154	if options_.nograph
	1155	hh = figure('Name','Smoothed observation errors','Visible','off');
	1156	else
	1157	hh = figure('Name','Smoothed observation errors');
	1158	end
1146	1159	set(0,'CurrentFigure',hh)
1147	1160	NAMES = [];
1148	1161	if options_.TeX, TeXNAMES = []; end

1178	1191	eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(1)]);
1179	1192	saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(1) '.fig']);
1180	1193	end
1181		if options_.nograph, close(hh), end
1182	1194	if options_.TeX
1183	1195	fprintf(fidTeX,'\\begin{figure}[H]\n');
1184	1196	for jj = 1:number_of_plots_to_draw

1194	1206	fclose(fidTeX);
1195	1207	end
1196	1208	else
1197		for plt = 1:nbplt-1
1198		hh = figure('Name','Smoothed observation errors');
	1209	for plt = 1:nbplt
	1210	if options_.nograph
	1211	hh = figure('Name','Smoothed observation errors','Visible','off');
	1212	else
	1213	hh = figure('Name','Smoothed observation errors');
	1214	end
1199	1215	set(0,'CurrentFigure',hh)
1200	1216	NAMES = [];
1201	1217	if options_.TeX, TeXNAMES = []; end
1202		for i=1:nstar
	1218	for i=1:min(nstar,number_of_plots_to_draw-(nbplt-1)*nstar)
1203	1219	k = (plt-1)*nstar+i;
1204	1220	subplot(nr,nc,i);
1205	1221	plot([1 gend],[0 0],'-r','linewidth',.5)

1232	1248	eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(plt)]);
1233	1249	saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(plt) '.fig']);
1234	1250	end
1235		if options_.nograph, close(hh), end
1236	1251	if options_.TeX
1237	1252	fprintf(fidTeX,'\\begin{figure}[H]\n');
1238	1253	for jj = 1:nstar

1246	1261	fprintf(fidTeX,'\n');
1247	1262	end
1248	1263	end
1249		hh = figure('Name','Smoothed observation errors');
1250		set(0,'CurrentFigure',hh)
1251		NAMES = [];
1252		if options_.TeX, TeXNAMES = []; end
1253		for i=1:number_of_plots_to_draw-(nbplt-1)*nstar
1254		k = (nbplt-1)*nstar+i;
1255		if lr ~= 0
1256		subplot(lr,lc,i);
1257		else
1258		subplot(nr,nc,i);
1259		end
1260		plot([1 gend],[0 0],'-r','linewidth',0.5)
1261		hold on
1262		plot(1:gend,measurement_error(index(k),:),'-k','linewidth',1)
1263		hold off
1264		name = deblank(options_.varobs(index(k),:));
1265		if isempty(NAMES)
1266		NAMES = name;
1267		else
1268		NAMES = char(NAMES,name);
1269		end
1270		if ~isempty(options_.XTick)
1271		set(gca,'XTick',options_.XTick)
1272		set(gca,'XTickLabel',options_.XTickLabel)
1273		end
1274		if options_.TeX
1275		idx = strmatch(options_.varobs(index(k)),M_.endo_names,'exact');
1276		texname = M_.endo_names_tex(idx,:);
1277		if isempty(TeXNAMES)
1278		TeXNAMES = ['$ ' deblank(texname) ' $'];
1279		else
1280		TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
1281		end
1282		end
1283		title(name,'Interpreter','none');
1284		end
1285		eval(['print -depsc2 ' M_.fname '_SmoothedObservationErrors' int2str(nbplt) '.eps']);
1286		if ~exist('OCTAVE_VERSION')
1287		eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(nbplt)]);
1288		saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(nbplt) '.fig']);
1289		end
1290		if options_.nograph, close(hh), end
1291		if options_.TeX
1292		fprintf(fidTeX,'\\begin{figure}[H]\n');
1293		for jj = 1:size(NAMES,1);
1294		fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
1295		end
1296		fprintf(fidTeX,'\\centering \n');
1297		fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedObservedErrors%s}\n',M_.fname,int2str(nbplt));
1298		fprintf(fidTeX,'\\caption{Smoothed observed errors.}');
1299		fprintf(fidTeX,'\\label{Fig:SmoothedObservedErrors:%s}\n',int2str(nbplt));
1300		fprintf(fidTeX,'\\end{figure}\n');
1301		fprintf(fidTeX,'\n');
1302		fprintf(fidTeX,'%% End of TeX file.\n');
1303		fclose(fidTeX);
1304		end
1305	1264	end
1306	1265	end
1307	1266	%%

1315	1274	fprintf(fidTeX,' \n');
1316	1275	end
1317	1276	if nbplt == 1
1318		hh = figure('Name','Historical and smoothed variables');
	1277	if options_.nograph
	1278	hh = figure('Name','Historical and smoothed variables','Visible','off');
	1279	else
	1280	hh = figure('Name','Historical and smoothed variables');
	1281	end
1319	1282	NAMES = [];
1320	1283	if options_.TeX, TeXNAMES = []; end
1321	1284	for i=1:n_varobs

1351	1314	eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(1)]);
1352	1315	saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(1) '.fig']);
1353	1316	end
1354		if options_.nograph, close(hh), end
1355	1317	if options_.TeX
1356	1318	fprintf(fidTeX,'\\begin{figure}[H]\n');
1357	1319	for jj = 1:n_varobs

1368	1330	end
1369	1331	else
1370	1332	for plt = 1:nbplt-1
1371		hh = figure('Name','Historical and smoothed variables');
	1333	if options_.nograph
	1334	hh = figure('Name','Historical and smoothed variables','Visible','off');
	1335	else
	1336	hh = figure('Name','Historical and smoothed variables');
	1337	end
1372	1338	set(0,'CurrentFigure',hh)
1373	1339	NAMES = [];
1374	1340	if options_.TeX, TeXNAMES = []; end

1406	1372	eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)]);
1407	1373	saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(plt) '.fig']);
1408	1374	end
1409		if options_.nograph, close(hh), end
1410	1375	if options_.TeX
1411	1376	fprintf(fidTeX,'\\begin{figure}[H]\n');
1412	1377	for jj = 1:nstar

1420	1385	fprintf(fidTeX,'\n');
1421	1386	end
1422	1387	end
1423		hh = figure('Name','Historical and smoothed variables');
	1388	if options_.nograph
	1389	hh = figure('Name','Historical and smoothed variables','Visible','off');
	1390	else
	1391	hh = figure('Name','Historical and smoothed variables');
	1392	end
1424	1393	set(0,'CurrentFigure',hh)
1425	1394	NAMES = [];
1426	1395	if options_.TeX, TeXNAMES = []; end

1462	1431	eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(nbplt)]);
1463	1432	saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(nbplt) '.fig']);
1464	1433	end
1465		if options_.nograph, close(hh), end
1466	1434	if options_.TeX
1467	1435	fprintf(fidTeX,'\\begin{figure}[H]\n');
1468	1436	for jj = 1:size(NAMES,1);

+11

-12

matlab/dynare_estimation_init.m less more

60	60	end
61	61
62	62	%% Set the order of approximation to one (if needed).
63		if options_.order > 1
64		if ~exist('particle','dir')
65		disp('This version of Dynare cannot estimate non linearized models!')
66		disp('Set "order" equal to 1.')
67		disp(' ')
68		options_.order = 1;
69		end
70		end
71
72		%% Set options_.lik_init equal to 3 if diffuse filter is used.
73		if (options_.diffuse_filter==1) && (options_.lik_init==1)
	63	if options_.order > 1 && ~isfield(options_,'nonlinear_filter')
	64	disp('This version of Dynare cannot estimate non linearized models!')
	65	disp('Set "order" equal to 1.')
	66	disp(' ')
	67	options_.order = 1;
	68	end
	69
	70	% Set options_.lik_init equal to 3 if diffuse filter is used or
	71	% kalman_algo refers to a diffuse filter algorithm.
	72	if (options_.diffuse_filter==1) \|\| (options_.kalman_algo > 2)
74	73	options_.lik_init = 3;
75	74	end
76	75

124	123	[xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
125	124	if any(bayestopt_.pshape > 0)
126	125	% Plot prior densities.
127		if options_.plot_priors
	126	if ~options_.nograph && options_.plot_priors
128	127	plot_priors(bayestopt_,M_,options_)
129	128	end
130	129	% Set prior bounds

+50

-32

matlab/dynare_identification.m less more

162	162	run_index = 0;
163	163
164	164	if SampleSize > 1,
165		h = waitbar(0,'Monte Carlo identification checks ...');
	165	h = dyn_waitbar(0,'Monte Carlo identification checks ...');
166	166	end
167	167	[I,J]=find(M_.lead_lag_incidence');
168	168

408	408	end
409	409
410	410	if SampleSize > 1,
411		waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
	411	dyn_waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
412	412	end
413	413	end
414	414	end

416	416
417	417
418	418	if SampleSize > 1,
419		close(h),
	419	dyn_waitbar_close(h),
420	420	normTAU=std(TAU')';
421	421	normLRE=std(LRE')';
422	422	normGAM=std(GAM')';

449	449	idemoments0=idemoments;
450	450	idelre0 = idelre;
451	451	iteration = 0;
452		h = waitbar(0,'Monte Carlo identification checks ...');
	452	h = dyn_waitbar(0,'Monte Carlo identification checks ...');
453	453	for file_index=1:length(identFiles)
454	454	load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
455	455	for index=1:size(stoH,3),

470	470	identification_checks(stoH(:,:,index)./normH(:,ones(nparam,1)), ...
471	471	stoJJ(:,:,index)./normJ(:,ones(nparam,1)), ...
472	472	stoLRE(:,:,index)./normLRE(:,ones(size(stoLRE,2),1)));
473		waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
	473	dyn_waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
474	474	end
475	475	end
476		close(h);
	476	dyn_waitbar_close(h);
477	477	save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idemodel', 'idemoments', 'idelre', '-append')
478	478	end
479	479	iteration = 0;
480		h = waitbar(0,'Monte Carlo identification checks ...');
	480	h = dyn_waitbar(0,'Monte Carlo identification checks ...');
481	481	for file_index=1:length(identFiles)
482	482	load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
483	483	for index=1:size(stoH,3),

491	491	FOCR(iteration,:) = ((GAM(:,iteration)./GAM(:,1)-1)./GAM(:,1))'*stoJJ(:,:,index);
492	492	FOCHR(iteration,:) = ((TAU(:,iteration)./TAU(:,1)-1)./TAU(:,1))'*stoH(:,:,index);
493	493
494		waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
495		end
496		end
497		close(h);
	494	dyn_waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
	495	end
	496	end
	497	dyn_waitbar_close(h);
498	498	end
499	499
500	500	% if SampleSize>1,

577	577	for ip=1:nparam,
578	578	text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
579	579	end
580		legend('relative to param value','relative to prior std','Location','Best')
	580	legend('relative to param value','relative to prior std',0)
581	581	title('Identification strength in the moments (log-scale)')
582	582
583	583	subplot(212)

604	604	text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
605	605	end
606	606	title('Sensitivity in the moments')
	607	eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ide_strength.eps']);
	608	if ~exist('OCTAVE_VERSION'),
	609	saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ide_strength'])
	610	eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ide_strength']);
	611	end
607	612
608	613	if advanced,
609	614	figure('Name','Identification in the model'),

656	661	end
657	662	title('Sensitivity in the model')
658	663
659		if options_.nograph, close(gcf); end
	664	eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_sensitivity.eps']);
	665	if ~exist('OCTAVE_VERSION'),
	666	saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_sensitivity'])
	667	eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_sensitivity']);
	668	end
	669	close(gcf);
660	670
661	671	% identificaton patterns
662	672	for j=1:size(cosnJ,2),

684	694	ax=colormap;
685	695	ax(1,:)=[0.9 0.9 0.9];
686	696	colormap(ax);
687		saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_collinearity_', int2str(j)])
688		eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
689		eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
690		if options_.nograph, close(gcf); end
	697	eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j) '.eps']);
	698	if ~exist('OCTAVE_VERSION'),
	699	saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_collinearity_', int2str(j)])
	700	eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
	701	end
	702	close(gcf);
691	703	end
692	704	disp('')
693	705	[U,S,V]=svd(siJ./normJ(:,ones(nparam,1)),0);

728	740	title(['Singular value ',num2str(Stit)])
729	741	end
730	742	figure(f1);
731		saveas(f1,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_1'])
732		eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
733		eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
	743	eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1.eps']);
	744	if ~exist('OCTAVE_VERSION'),
	745	saveas(f1,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_1'])
	746	eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
	747	end
734	748	if nparam>4,
735	749	figure(f2),
736		saveas(f2,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_2'])
737		eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
738		eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
	750	eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2.eps']);
	751	if ~exist('OCTAVE_VERSION'),
	752	saveas(f2,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_2'])
	753	eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
	754	end
739	755	end
740	756
741	757	if SampleSize>1,
742		options_.nograph=1;
743	758	figure('Name','Condition Number'),
744	759	subplot(221)
745	760	hist(log10(idemodel.cond))

750	765	subplot(223)
751	766	hist(log10(idelre.cond))
752	767	title('log10 of Condition number in the LRE model')
753		saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND'])
754		eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
755		eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
756		if options_.nograph, close(gcf); end
	768	eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND.eps']);
	769	if ~exist('OCTAVE_VERSION'),
	770	saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND'])
	771	eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
	772	end
	773	close(gcf)
757	774	ncut=floor(SampleSize/10*9);
758		[~,is]=sort(idelre.cond);
	775	[dum,is]=sort(idelre.cond);
759	776	[proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberLRE', 1, [], IdentifDirectoryName);
760		[~,is]=sort(idemodel.cond);
	777	[dum,is]=sort(idemodel.cond);
761	778	[proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberModel', 1, [], IdentifDirectoryName);
762		[~,is]=sort(idemoments.cond);
	779	[dum,is]=sort(idemoments.cond);
763	780	[proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments', 1, [], IdentifDirectoryName);
764	781	% [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
765	782	for j=1:nparam,

768	785	% if ~isempty(ibeh) && ~isempty(inonbeh)
769	786	% [proba, dproba] = stab_map_1(pdraws, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
770	787	% end
771		[~,is]=sort(idemoments.Mco(j,:));
	788	[dum,is]=sort(idemoments.Mco(j,:));
	789	options_.nograph=1;
772	790	[proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
773	791	end
774	792	end

+1

-1

matlab/dynare_solve.m less more

37	37	options_ = set_default_option(options_,'solve_algo',2);
38	38	info = 0;
39	39	if options_.solve_algo == 0
40		if ~exist('OCTAVE_VERSION') && ~license('test', 'optimization_toolbox')
	40	if ~exist('OCTAVE_VERSION') && isempty(license('inuse','optimization_toolbox'))
41	41	error('You can''t use solve_algo=0 since you don''t have MATLAB''s Optimization Toolbox')
42	42	end
43	43	options=optimset('fsolve');

+1

-1

matlab/forcst.m less more

80	80
81	81	int_width = zeros(horizon,M_.endo_nbr);
82	82	for i=1:nvar
83		int_width(:,i) = fact*sqrt(var_yf(:,i));
	83	int_width(:,i) = -fact*sqrt(var_yf(:,i));
84	84	end
85	85
86	86	yf = yf(ivar,:);

+2

-1

matlab/global_initialization.m less more

27	27	% You should have received a copy of the GNU General Public License
28	28	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
29	29
30		global oo_ M_ options_ estim_params_
	30	global oo_ M_ options_ estim_params_ bayestopt_
31	31
32	32	estim_params_ = [];
	33	bayestopt_ = [];
33	34
34	35	options_.console_mode = 0;
35	36

+1

-1

matlab/identification_checks.m less more

69	69	eH(ind1,length(find(vnorm(H)<eps))+1:end) = e1; % non-zero eigenvectors
70	70	eH(find(vnorm(H)<eps),1:length(find(vnorm(H)<eps)))=eye(length(find(vnorm(H)<eps)));
71	71	condH = cond(H1);
72		rankH = rank(HH./norm(HH),1.e-10);
	72	rankH = rank(H./norm(H),1.e-10);
73	73	rankHH = rankH;
74	74
75	75	ind2 = find(vnorm(JJ)>=eps); % take non-zero columns

+1

-1

matlab/missing_DiffuseKalmanSmootherH3_Z.m less more

278	278	end
279	279	end
280	280
281		epsilon_hat = Y - Z*alphahat;
	281	epsilonhat = Y - Z*alphahat;

+1

-0

matlab/model_diagnostics.m less more

84	84	M_.fname,...
85	85	oo_.exo_steady_state,...
86	86	oo_.exo_det_steady_state,...
	87	M_.params,...
87	88	options_.bytecode);
88	89	else
89	90	error([M_.fname '_steadystate.m doesn''t match the model']);

+2

-3

matlab/mr_gstep.m less more

70	70	end
71	71	xh1=x;
72	72	end
73		save gstep.mat
	73	save gstep.mat x h1 f0
74	74	end
75	75
76		save gstep.mat
	76	save gstep.mat x h1 f0
77	77
78	78
79

+3

-8

matlab/mr_hessian.m less more

5	5	%
6	6	% adapted from Michel Juillard original rutine hessian.m
7	7	%
8		% func = name of the function: func must give two outputs:
	8	% func = function handle. The function must give two outputs:
9	9	% - the log-likelihood AND the single contributions at times t=1,...,T
10	10	% of the log-likelihood to compute outer product gradient
11	11	% x = parameter values

147	147	h1(i)=h10;
148	148	i=0;
149	149	end
150		save hess.mat
151	150	end
152	151
153	152	h_1=h1;

181	180	xh_1(i)=x(i);
182	181	xh_1(j)=x(j);
183	182	j=j+1;
184		save hess.mat
185	183	end
186	184	i=i+1;
187	185	end

205	203	A=diag(2.*h1); % rescaling matrix
206	204	% igg=inv(hh_mat); % inverted rescaled outer product hessian
207	205	ihh=A'*(hh_mat\A); % inverted outer product hessian
208		if hflag>0 & min(eig(reshape(hessian_mat,n,n)))>0,
	206	if hflag>0 && min(eig(reshape(hessian_mat,n,n)))>0,
209	207	hh0 = Areshape(hessian_mat,n,n)A'; %rescaled second order derivatives
210	208	hh = reshape(hessian_mat,n,n); %rescaled second order derivatives
211	209	sd0=sqrt(diag(hh0)); %rescaled 'standard errors' using second order derivatives

243	241	end
244	242	hh1=h1;
245	243	htol1=htol;
246		save hess.mat
247		% 11/25/03 SA Created from Hessian_sparse (removed sparse)
248
249
	244	save hess.mat hessian_mat

+5

-0

matlab/plot_icforecast.m less more

25	25	%
26	26	% You should have received a copy of the GNU General Public License
27	27	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	28
	29	if exist('OCTAVE_VERSION') && octave_ver_less_than('3.4.0')
	30	% The set() command on the handle returned by area() crashes in Octave 3.2
	31	error('plot_conditional_forecast: you need Octave >= 3.4 (because of a bug in older versions)')
	32	end
28	33
29	34	load conditional_forecasts;
30	35

+2

-2

matlab/print_info.m less more

47	47	error(['The Jacobian matrix evaluated at the steady state contains elements ' ...
48	48	'that are not real or are infinite'])
49	49	case 7
50		error(['One of the eigenvalues is is close to 0/0 (the absolute ' ...
51		'value of numerator and denominator is smaller than 1e-6).'])
	50	error(['One of the eigenvalues is close to 0/0 (the absolute ' ...
	51	'value of numerator and denominator is smaller than 1e-6)'])
52	52	case 19
53	53	error('The steadystate file did not compute the steady state (inconsistent deep parameters).')
54	54	case 20

+0

-6

matlab/ramsey_policy.m less more

25	25
26	26	if options_.noprint == 0
27	27	disp_steady_state(M_,oo_)
28		for i=M_.orig_endo_nbr:M_.endo_nbr
29		if strmatch('mult_',M_.endo_names(i,:))
30		disp(sprintf('%s \t\t %g',M_.endo_names(i,:), ...
31		oo_.dr.ys(i)));
32		end
33		end
34	28	end
35	29
36	30

+3

-3

matlab/read_variables.m less more

43	43
44	44	dyn_size_01 = size(dyn_data_01,1);
45	45	var_size_01 = size(var_names_01,1);
46		if exist(file_name_01)
	46	if exist([file_name_01 '.m'],'file')
47	47	file_name_02 = [file_name_01 '.m'];
48	48	dyn_instr_01 = file_name_01;
49	49	eval(dyn_instr_01);

55	55	end
56	56	dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
57	57	end
58		elseif exist([file_name_01 '.mat'])
	58	elseif exist([file_name_01 '.mat'],'file')
59	59	file_name_02 = [file_name_01 '.mat'];
60	60	s = load(file_name_01);
61	61	for dyn_i_01=1:var_size_01

66	66	end
67	67	dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
68	68	end
69		elseif exist([file_name_01 '.xls'])
	69	elseif exist([file_name_01 '.xls'],'file')
70	70	file_name_02 = [file_name_01 '.xls'];
71	71	[num,txt,raw] = xlsread(file_name_01,xls_sheet,xls_range);
72	72	for dyn_i_01=1:var_size_01

+1

-1

matlab/resol.m less more

70	70	check1 = 0;
71	71	% testing for steadystate file
72	72	if (~options_.bytecode)
73		fh = str2func([M_.fname '_static']);
	73	fh = [M_.fname '_static'];
74	74	end;
75	75
76	76	if options_.steadystate_flag

+2

-2

matlab/set_prior.m less more

221	221	bayestopt_.p4(k(k2)) = Inf(length(k2),1);
222	222	for i=1:length(k)
223	223	[bayestopt_.p6(k(i)),bayestopt_.p7(k(i))] = ...
224		inverse_gamma_specification(bayestopt_.p1(k(i))-bayestopt_.p3(k(i)),bayestopt_.p2(k(i)),1) ;
	224	inverse_gamma_specification(bayestopt_.p1(k(i))-bayestopt_.p3(k(i)),bayestopt_.p2(k(i)),1,0) ;
225	225	bayestopt_.p5(k(i)) = compute_prior_mode([ bayestopt_.p6(k(i)) , bayestopt_.p7(k(i)) , bayestopt_.p3(k(i)) ], 4) ;
226	226	end
227	227

243	243	bayestopt_.p4(k(k2)) = Inf(length(k2),1);
244	244	for i=1:length(k)
245	245	[bayestopt_.p6(k(i)),bayestopt_.p7(k(i))] = ...
246		inverse_gamma_specification(bayestopt_.p1(k(i))-bayestopt_.p3(k(i)),bayestopt_.p2(k(i)),2);
	246	inverse_gamma_specification(bayestopt_.p1(k(i))-bayestopt_.p3(k(i)),bayestopt_.p2(k(i)),2,0);
247	247	bayestopt_.p5(k(i)) = compute_prior_mode([ bayestopt_.p6(k(i)) , bayestopt_.p7(k(i)) , bayestopt_.p3(k(i)) ], 6) ;
248	248	end
249	249

+5

-5

matlab/set_state_space.m less more

37	37	lead_lag_incidence = M_.lead_lag_incidence;
38	38	klen = max_lag + max_lead + 1;
39	39
40		fwrd_var = find(any(lead_lag_incidence(max_lag+2:end,:),1))';
	40	fwrd_var = find(lead_lag_incidence(max_lag+2:end,:))';
41	41	if max_lag > 0
42		pred_var = find(any(lead_lag_incidence(1,:),1))';
	42	pred_var = find(lead_lag_incidence(1,:))';
43	43	both_var = intersect(pred_var,fwrd_var);
44	44	pred_var = setdiff(pred_var,both_var);
45	45	fwrd_var = setdiff(fwrd_var,both_var);

60	60	if max_lag > 0
61	61	kmask = [];
62	62	if max_lead > 0
63		kmask = [cumsum(flipud(lead_lag_incidence(max_lag+2:end,order_var)),1)] ;
	63	kmask = lead_lag_incidence(max_lag+2,order_var) ;
64	64	end
65		kmask = [kmask; flipud(cumsum(lead_lag_incidence(1,order_var),1))] ;
	65	kmask = [kmask; lead_lag_incidence(1,order_var)] ;
66	66	else
67		kmask = cumsum(flipud(lead_lag_incidence(max_lag+2:klen,order_var)),1) ;
	67	kmask = lead_lag_incidence(max_lag+2,order_var) ;
68	68	end
69	69
70	70	kmask = kmask';

+6

-0

matlab/simult_.m less more

37	37
38	38	y_ = zeros(size(y0,1),iter+M_.maximum_lag);
39	39	y_(:,1) = y0;
	40
	41	% stoch_simul sets k_order_solver=1 if order=3, but does so only locally, so we
	42	% have to do it here also
	43	if options_.order == 3
	44	options_.k_order_solver = 1;
	45	end
40	46
41	47	if ~options_.k_order_solver
42	48	if iorder==1

+1

-1

matlab/solve_one_boundary.m less more

352	352	[r, y, g1, g2, g3] = feval(fname, y, x, params, ...
353	353	steady_state, it_, 0);
354	354	else
355		[r, y, g1] = feval(fname, y, x, params, steady_state);
	355	[r, y, g1] = feval(fname, y, x, params);
356	356	end;
357	357	if max(abs(r)) >= options_.solve_tolf
358	358	[dx,flag1] = bicgstab(g1,-r,1e-7,Blck_size,L1,U1);

+5

-5

matlab/steady_.m less more

77	77	else
78	78	[r, g1, ssss] = feval([M_.fname '_static'], b, ss, ...
79	79	[oo_.exo_steady_state; oo_.exo_det_steady_state], M_.params);
	80	idx = find(abs(ssss-oo_.steady_state)>10*options_.dynatol);
	81	if ~isempty(idx)
	82	check1 = 1;
	83	end
80	84	end
81	85	end
82		check1 = any(check2);
83		idx = find(abs(ssss-oo_.steady_state)>10*options_.dynatol);
84		if ~isempty(idx)
85		check1 = 1;
86		end
	86	check1 = check1 \| any(check2);
87	87	elseif options_.bytecode
88	88	[check1, residuals] = bytecode('evaluate','static',oo_.steady_state,...
89	89	[oo_.exo_steady_state; ...

+4

-1

matlab/stoch_simul.m less more

130	130	if PI_PCL_solver
131	131	PCL_Part_info_moments (0, PCL_varobs, oo_.dr, i_var);
132	132	elseif options_.periods == 0
133		disp_th_moments(oo_.dr,var_list);
	133	% There is no code for theoretical moments at 3rd order
	134	if options_.order <= 2
	135	disp_th_moments(oo_.dr,var_list);
	136	end
134	137	else
135	138	disp_moments(oo_.endo_simul,var_list);
136	139	end

+4

-0

matlab/th_autocovariances.m less more

42	42
43	43	if nargin<5
44	44	nodecomposition = 0;
	45	end
	46
	47	if options_.order >= 3
	48	error('Theoretical moments not implemented above 2nd order')
45	49	end
46	50
47	51	endo_nbr = M_.endo_nbr;

+6

-5

matlab/vnorm.m less more

55	55
56	56	if nargin>1
57	57	dim = varargin{1};
58		if isempty(dim)
59		idx = find(size(A)~=1);
60		dim = idx(1);
61		elseif dim~=floor(dim) \|\| dim<1
	58	if ~isempty(dim) && (dim~=floor(dim) \|\| dim<1),
62	59	error('Dimension must be positive integer');
63	60	end
64	61	if nargin>2

66	63	end
67	64	end
68	65
69
	66	if isempty(dim)
	67	idx = find(size(A)~=1);
	68	dim = idx(1);
	69	end
	70
70	71	if isempty(ntype)
71	72	y = sqrt(sum( abs(A).^2 , dim) );
72	73	elseif ntype==1

+13

-15

mex/build/matlab/configure less more

0	0	#! /bin/sh
1	1	# Guess values for system-dependent variables and create Makefiles.
2		# Generated by GNU Autoconf 2.68 for dynare 4.2.2.
	2	# Generated by GNU Autoconf 2.68 for dynare 4.2.4.
3	3	#
4	4	#
5	5	# Copyright (C) 1992, 1993, 1994, 1995, 1996, 1998, 1999, 2000, 2001,

556	556	# Identity of this package.
557	557	PACKAGE_NAME='dynare'
558	558	PACKAGE_TARNAME='dynare'
559		PACKAGE_VERSION='4.2.2'
560		PACKAGE_STRING='dynare 4.2.2'
	559	PACKAGE_VERSION='4.2.4'
	560	PACKAGE_STRING='dynare 4.2.4'
561	561	PACKAGE_BUGREPORT=''
562	562	PACKAGE_URL=''
563	563

1252	1252	# Omit some internal or obsolete options to make the list less imposing.
1253	1253	# This message is too long to be a string in the A/UX 3.1 sh.
1254	1254	cat <<_ACEOF
1255		\`configure' configures dynare 4.2.2 to adapt to many kinds of systems.
	1255	\`configure' configures dynare 4.2.4 to adapt to many kinds of systems.
1256	1256
1257	1257	Usage: $0 [OPTION]... [VAR=VALUE]...
1258	1258

1322	1322
1323	1323	if test -n "$ac_init_help"; then
1324	1324	case $ac_init_help in
1325		short \| recursive ) echo "Configuration of dynare 4.2.2:";;
	1325	short \| recursive ) echo "Configuration of dynare 4.2.4:";;
1326	1326	esac
1327	1327	cat <<\_ACEOF
1328	1328

1430	1430	test -n "$ac_init_help" && exit $ac_status
1431	1431	if $ac_init_version; then
1432	1432	cat <<\_ACEOF
1433		dynare configure 4.2.2
	1433	dynare configure 4.2.4
1434	1434	generated by GNU Autoconf 2.68
1435	1435
1436	1436	Copyright (C) 2010 Free Software Foundation, Inc.

1607	1607	This file contains any messages produced by compilers while
1608	1608	running configure, to aid debugging if configure makes a mistake.
1609	1609
1610		It was created by dynare $as_me 4.2.2, which was
	1610	It was created by dynare $as_me 4.2.4, which was
1611	1611	generated by GNU Autoconf 2.68. Invocation command line was
1612	1612
1613	1613	$ $0 $@

2423	2423
2424	2424	# Define the identity of the package.
2425	2425	PACKAGE='dynare'
2426		VERSION='4.2.2'
	2426	VERSION='4.2.4'
2427	2427
2428	2428
2429	2429	cat >>confdefs.h <<_ACEOF

3072	3072	ax_mexopts_ok="yes"
3073	3073	;;
3074	3074	maci \| maci64)
3075		SDKROOT='/Developer/SDKs/MacOSX10.5.sdk'
3076		MACOSX_DEPLOYMENT_TARGET='10.5'
	3075	SDKROOT='/Developer/SDKs/MacOSX10.6.sdk'
	3076	MACOSX_DEPLOYMENT_TARGET='10.6'
3077	3077	if test "${MATLAB_ARCH}" = "maci"; then
3078	3078	ARCHS='i386'
3079		MATLAB_FFLAGS=''
3080	3079	else
3081	3080	ARCHS='x86_64'
3082		MATLAB_FFLAGS='-m64'
3083	3081	fi
3084	3082	MATLAB_DEFS="$MATLAB_DEFS -DNDEBUG"
3085	3083	MATLAB_CFLAGS="-fno-common -no-cpp-precomp -arch $ARCHS -isysroot $SDKROOT -mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -fexceptions -O2"
3086	3084	MATLAB_LDFLAGS="-L$MATLAB/bin/${MATLAB_ARCH} -Wl,-twolevel_namespace -undefined error -arch $ARCHS -Wl,-syslibroot,$SDKROOT -mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -bundle -Wl,-exported_symbols_list,\$(top_srcdir)/mexFunction-MacOSX.map"
3087	3085	MATLAB_LIBS="-lmx -lmex -lmat -lstdc++ -lmwlapack"
3088	3086	MATLAB_CXXFLAGS="-fno-common -no-cpp-precomp -fexceptions -arch $ARCHS -isysroot $SDKROOT -mmacosx-version-min=$MACOSX_DEPLOYMENT_TARGET -O2"
3089		MATLAB_FFLAGS="-fexceptions $MATLAB_FFLAGS -fbackslash"
	3087	MATLAB_FFLAGS="-fexceptions -fbackslash -arch $ARCHS"
3090	3088	# Starting from MATLAB 7.5, BLAS and LAPACK are in distinct libraries
3091	3089
3092	3090

6188	6186	# report actual input values of CONFIG_FILES etc. instead of their
6189	6187	# values after options handling.
6190	6188	ac_log="
6191		This file was extended by dynare $as_me 4.2.2, which was
	6189	This file was extended by dynare $as_me 4.2.4, which was
6192	6190	generated by GNU Autoconf 2.68. Invocation command line was
6193	6191
6194	6192	CONFIG_FILES = $CONFIG_FILES

6245	6243	cat >>$CONFIG_STATUS <<_ACEOF \|\| ac_write_fail=1
6246	6244	ac_cs_config="`$as_echo "$ac_configure_args" \| sed 's/^ //; s/[\\""\`\$]/\\\\&/g'`"
6247	6245	ac_cs_version="\\
6248		dynare config.status 4.2.2
	6246	dynare config.status 4.2.4
6249	6247	configured by $0, generated by GNU Autoconf 2.68,
6250	6248	with options \\"\$ac_cs_config\\"
6251	6249

+1

-1

mex/build/matlab/configure.ac less more

17	17	dnl along with Dynare. If not, see <http://www.gnu.org/licenses/>.
18	18
19	19	AC_PREREQ([2.61])
20		AC_INIT([dynare], [4.2.2])
	20	AC_INIT([dynare], [4.2.4])
21	21	AC_CONFIG_SRCDIR([configure.ac])
22	22	AM_INIT_AUTOMAKE([-Wall -Werror foreign])
23	23

+10

-10

mex/build/octave/configure less more

0	0	#! /bin/sh
1	1	# Guess values for system-dependent variables and create Makefiles.
2		# Generated by GNU Autoconf 2.68 for dynare 4.2.2.
	2	# Generated by GNU Autoconf 2.68 for dynare 4.2.4.
3	3	#
4	4	#
5	5	# Copyright (C) 1992, 1993, 1994, 1995, 1996, 1998, 1999, 2000, 2001,

555	555	# Identity of this package.
556	556	PACKAGE_NAME='dynare'
557	557	PACKAGE_TARNAME='dynare'
558		PACKAGE_VERSION='4.2.2'
559		PACKAGE_STRING='dynare 4.2.2'
	558	PACKAGE_VERSION='4.2.4'
	559	PACKAGE_STRING='dynare 4.2.4'
560	560	PACKAGE_BUGREPORT=''
561	561	PACKAGE_URL=''
562	562

1235	1235	# Omit some internal or obsolete options to make the list less imposing.
1236	1236	# This message is too long to be a string in the A/UX 3.1 sh.
1237	1237	cat <<_ACEOF
1238		\`configure' configures dynare 4.2.2 to adapt to many kinds of systems.
	1238	\`configure' configures dynare 4.2.4 to adapt to many kinds of systems.
1239	1239
1240	1240	Usage: $0 [OPTION]... [VAR=VALUE]...
1241	1241

1305	1305
1306	1306	if test -n "$ac_init_help"; then
1307	1307	case $ac_init_help in
1308		short \| recursive ) echo "Configuration of dynare 4.2.2:";;
	1308	short \| recursive ) echo "Configuration of dynare 4.2.4:";;
1309	1309	esac
1310	1310	cat <<\_ACEOF
1311	1311

1409	1409	test -n "$ac_init_help" && exit $ac_status
1410	1410	if $ac_init_version; then
1411	1411	cat <<\_ACEOF
1412		dynare configure 4.2.2
	1412	dynare configure 4.2.4
1413	1413	generated by GNU Autoconf 2.68
1414	1414
1415	1415	Copyright (C) 2010 Free Software Foundation, Inc.

1586	1586	This file contains any messages produced by compilers while
1587	1587	running configure, to aid debugging if configure makes a mistake.
1588	1588
1589		It was created by dynare $as_me 4.2.2, which was
	1589	It was created by dynare $as_me 4.2.4, which was
1590	1590	generated by GNU Autoconf 2.68. Invocation command line was
1591	1591
1592	1592	$ $0 $@

2402	2402
2403	2403	# Define the identity of the package.
2404	2404	PACKAGE='dynare'
2405		VERSION='4.2.2'
	2405	VERSION='4.2.4'
2406	2406
2407	2407
2408	2408	cat >>confdefs.h <<_ACEOF

5508	5508	# report actual input values of CONFIG_FILES etc. instead of their
5509	5509	# values after options handling.
5510	5510	ac_log="
5511		This file was extended by dynare $as_me 4.2.2, which was
	5511	This file was extended by dynare $as_me 4.2.4, which was
5512	5512	generated by GNU Autoconf 2.68. Invocation command line was
5513	5513
5514	5514	CONFIG_FILES = $CONFIG_FILES

5565	5565	cat >>$CONFIG_STATUS <<_ACEOF \|\| ac_write_fail=1
5566	5566	ac_cs_config="`$as_echo "$ac_configure_args" \| sed 's/^ //; s/[\\""\`\$]/\\\\&/g'`"
5567	5567	ac_cs_version="\\
5568		dynare config.status 4.2.2
	5568	dynare config.status 4.2.4
5569	5569	configured by $0, generated by GNU Autoconf 2.68,
5570	5570	with options \\"\$ac_cs_config\\"
5571	5571

+1

-1

mex/build/octave/configure.ac less more

17	17	dnl along with Dynare. If not, see <http://www.gnu.org/licenses/>.
18	18
19	19	AC_PREREQ([2.61])
20		AC_INIT([dynare], [4.2.2])
	20	AC_INIT([dynare], [4.2.4])
21	21	AC_CONFIG_SRCDIR([configure.ac])
22	22	AM_INIT_AUTOMAKE([-Wall -Werror foreign])
23	23

+2

-1

mex/sources/Makefile.in less more

41	41	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
42	42	$(top_srcdir)/m4/ax_latex_class.m4 \
43	43	$(top_srcdir)/m4/ax_latex_test.m4 \
44		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	44	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
45	45	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
46	46	$(top_srcdir)/configure.ac
47	47	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

103	103	LN_S = @LN_S@
104	104	LTLIBOBJS = @LTLIBOBJS@
105	105	MAKEINFO = @MAKEINFO@
	106	MATLAB = @MATLAB@
106	107	MKDIR_P = @MKDIR_P@
107	108	OBJEXT = @OBJEXT@
108	109	OCTAVE = @OCTAVE@

+9

-1

mex/sources/mjdgges/mjdgges.c less more

59	59	pei = eval_i;
60	60	for (per = eval_r; per <= &eval_r[i_n-1]; ++per)
61	61	{
62		per = par / *pb;
	62	if ((fabs(par) > 1e-6) \|\| (fabs(pb) > 1e-6))
	63	per = par / *pb;
	64	else
	65	{
	66	/* the ratio is too close to 0/0;
	67	returns specific error number only if no other error */
	68	if (i_info == 0)
	69	*info = -30;
	70	}
63	71	if (pai == 0.0 && pb == 0.0)
64	72	*pei = 0.0;
65	73	else

+16

-5

preprocessor/DynamicModel.cc less more

3604	3604
3605	3605	// Order of auxiliary variable definition equations:
3606	3606	// - expectation (entered before this function is called)
3607		// - lead variables from lower lead to higher lead (need to be listed in reverse order)
	3607	// - lead variables from lower lead to higher lead
3608	3608	// - lag variables from lower lag to higher lag
3609		if ((type == avEndoLead) \|\| (type == avExoLead))
3610		copy(neweqs.rbegin(), neweqs.rend(), back_inserter(aux_equations));
3611		else
3612		copy(neweqs.begin(), neweqs.end(), back_inserter(aux_equations));
	3609	copy(neweqs.begin(), neweqs.end(), back_inserter(aux_equations));
3613	3610
3614	3611	if (neweqs.size() > 0)
3615	3612	{

3759	3756	it != trendVars.end(); it++)
3760	3757	eval_context[*it] = 2; //not <= 0 bc of log, not 1 bc of powers
3761	3758	}
	3759
	3760	bool
	3761	DynamicModel::isModelLocalVariableUsed() const
	3762	{
	3763	set<int> used_local_vars;
	3764	size_t i = 0;
	3765	while (i < equations.size() && used_local_vars.size() == 0)
	3766	{
	3767	equations[i]->collectModelLocalVariables(used_local_vars);
	3768	i++;
	3769	}
	3770	return used_local_vars.size() > 0;
	3771	}
	3772

+1

-0

preprocessor/DynamicModel.hh less more

495	495	else
496	496	return (-1);
497	497	};
	498	bool isModelLocalVariableUsed() const;
498	499	};
499	500
500	501	#endif

+2

-1

preprocessor/Makefile.in less more

44	44	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
45	45	$(top_srcdir)/m4/ax_latex_class.m4 \
46	46	$(top_srcdir)/m4/ax_latex_test.m4 \
47		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	47	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
48	48	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
49	49	$(top_srcdir)/configure.ac
50	50	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

184	184	LN_S = @LN_S@
185	185	LTLIBOBJS = @LTLIBOBJS@
186	186	MAKEINFO = @MAKEINFO@
	187	MATLAB = @MATLAB@
187	188	MKDIR_P = @MKDIR_P@
188	189	OBJEXT = @OBJEXT@
189	190	OCTAVE = @OCTAVE@

+27

-2

preprocessor/ModFile.cc less more

139	139	cerr << "ERROR: In 'model' block, 'use_dll' option is not compatible with 'block' or 'bytecode'" << endl;
140	140	exit(EXIT_FAILURE);
141	141	}
142
	142	if (block \|\| byte_code)
	143	if (dynamic_model.isModelLocalVariableUsed())
	144	{
	145	cerr << "ERROR: In 'model' block, 'block' or 'bytecode' options are not yet compatible with pound expressions" << endl;
	146	exit(EXIT_FAILURE);
	147	}
143	148	if ((stochastic_statement_present \|\| mod_file_struct.check_present \|\| mod_file_struct.steady_present) && no_static)
144	149	{
145	150	cerr << "ERROR: no_static option is incompatible with stoch_simul, estimation, osr, ramsey_policy, steady and check commands" << endl;

378	383	<< "% from model file (.mod)" << endl << endl;
379	384
380	385	if (clear_all)
381		mOutputFile << "clear all" << endl;
	386	{
	387	mOutputFile << "clear all" << endl
	388	// this is a work-around for a bug in Octave 3.2
	389	<< "clear global" << endl;
	390	}
382	391
383	392	mOutputFile << "tic;" << endl
384	393	<< "global M_ oo_ options_ ys0_ ex0_" << endl

440	449	if (!use_dll)
441	450	mOutputFile << "erase_compiled_function('" + basename + "_dynamic');" << endl;
442	451
	452	// Erase generated steady state file (see ticket #224)
	453	string steadystatefile = basename + "_steadystate.m";
	454	ifstream in(steadystatefile.c_str(), ios::binary);
	455	if (!in.fail())
	456	{
	457	string line;
	458	getline(in, line);
	459	if (!line.compare(STEADY_STATE_GENERATED_HEADER))
	460	{
	461	in.close();
	462	unlink(steadystatefile.c_str());
	463	}
	464	else
	465	in.close();
	466	}
	467
443	468	#if defined(_WIN32) \|\| defined(__CYGWIN32__)
444	469	// If using USE_DLL with MSVC, check that the user didn't use a function not supported by MSVC (because MSVC doesn't comply with C99 standard)
445	470	if (use_dll && msvc)

+18

-4

preprocessor/ModelTree.cc less more

848	848	}
849	849	if (i > 0)
850	850	{
851		if ((prev_Type == EVALUATE_FORWARD && Simulation_Type == EVALUATE_FORWARD)
852		\|\| (prev_Type == EVALUATE_BACKWARD && Simulation_Type == EVALUATE_BACKWARD))
	851	bool is_lead = false, is_lag = false;
	852	int c_Size = (block_type_size_mfs[block_type_size_mfs.size()-1]).second.first;
	853	int first_equation = (block_type_size_mfs[block_type_size_mfs.size()-1]).first.second;
	854	if (c_Size > 0 && ((prev_Type == EVALUATE_FORWARD && Simulation_Type == EVALUATE_FORWARD && !is_lead)
	855	\|\| (prev_Type == EVALUATE_BACKWARD && Simulation_Type == EVALUATE_BACKWARD && !is_lag)))
	856	{
	857	for (int j = first_equation; j < first_equation+c_Size; j++)
	858	{
	859	dynamic_jacob_map_t::const_iterator it = dynamic_jacobian.find(make_pair(-1, make_pair(equation_reordered[eq], variable_reordered[j])));
	860	if (it != dynamic_jacobian.end())
	861	is_lag = true;
	862	it = dynamic_jacobian.find(make_pair(+1, make_pair(equation_reordered[eq], variable_reordered[j])));
	863	if (it != dynamic_jacobian.end())
	864	is_lead = true;
	865	}
	866	}
	867	if ((prev_Type == EVALUATE_FORWARD && Simulation_Type == EVALUATE_FORWARD && !is_lead)
	868	\|\| (prev_Type == EVALUATE_BACKWARD && Simulation_Type == EVALUATE_BACKWARD && !is_lag))
853	869	{
854	870	//merge the current block with the previous one
855	871	BlockSimulationType c_Type = (block_type_size_mfs[block_type_size_mfs.size()-1]).first.first;
856		int c_Size = (block_type_size_mfs[block_type_size_mfs.size()-1]).second.first;
857		int first_equation = (block_type_size_mfs[block_type_size_mfs.size()-1]).first.second;
858	872	c_Size++;
859	873	block_type_size_mfs[block_type_size_mfs.size()-1] = make_pair(make_pair(c_Type, first_equation), make_pair(c_Size, c_Size));
860	874	if (block_lag_lead[block_type_size_mfs.size()-1].first > Lag)

+5

-1

preprocessor/SteadyStateModel.cc less more

112	112	exit(EXIT_FAILURE);
113	113	}
114	114
115		output << "function [ys_, check_] = " << basename << "_steadystate(";
	115	/* The following header is used when the preprocessor deletes the old
	116	generated steady state file: it distinguishes this file from a hand
	117	written one (see ticket #224) */
	118	output << STEADY_STATE_GENERATED_HEADER << endl
	119	<< "function [ys_, check_] = " << basename << "_steadystate(";
116	120	if (ramsey_policy)
117	121	output << "ys_";
118	122	else

+2

-0

preprocessor/SteadyStateModel.hh less more

21	21
22	22	#include "DataTree.hh"
23	23	#include "StaticModel.hh"
	24
	25	#define STEADY_STATE_GENERATED_HEADER "% Generated file, will be deleted at next run of Dynare"
24	26
25	27	class SteadyStateModel : public DataTree
26	28	{

+2

-1

preprocessor/macro/Makefile.in less more

43	43	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
44	44	$(top_srcdir)/m4/ax_latex_class.m4 \
45	45	$(top_srcdir)/m4/ax_latex_test.m4 \
46		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	46	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
47	47	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
48	48	$(top_srcdir)/configure.ac
49	49	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

132	132	LN_S = @LN_S@
133	133	LTLIBOBJS = @LTLIBOBJS@
134	134	MAKEINFO = @MAKEINFO@
	135	MATLAB = @MATLAB@
135	136	MKDIR_P = @MKDIR_P@
136	137	OBJEXT = @OBJEXT@
137	138	OCTAVE = @OCTAVE@

+100

-0

tests/AIM/data_ca1.m less more

	0	data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
	1	-0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
	2	-0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
	3	-0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
	4	-0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
	5	-0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
	6	-0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
	7	1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
	8	2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
	9	1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
	10	1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
	11	1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
	12	1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
	13	0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
	14	1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
	15	1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
	16	0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
	17	1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
	18	1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
	19	-0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
	20	0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
	21	0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
	22	-0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
	23	2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
	24	1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
	25	1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
	26	1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
	27	1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
	28	1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
	29	0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
	30	0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
	31	1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
	32	0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
	33	0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
	34	0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
	35	0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
	36	-0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
	37	-0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
	38	-0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
	39	-1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
	40	0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
	41	0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
	42	0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
	43	-0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
	44	0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
	45	0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
	46	0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
	47	0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
	48	0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
	49	0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
	50	0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
	51	1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
	52	1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
	53	1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
	54	0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
	55	0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
	56	-0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
	57	0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
	58	0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
	59	0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
	60	0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
	61	1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
	62	0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
	63	0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
	64	1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
	65	1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
	66	0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
	67	1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
	68	0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
	69	1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
	70	1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
	71	1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
	72	1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
	73	1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
	74	1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
	75	1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
	76	0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
	77	1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
	78	0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
	79	0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
	80	0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
	81	-0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
	82	0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
	83	1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
	84	1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
	85	0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
	86	];
	87
	88	data = reshape(data,5,86)';
	89	y_obs = data(:,1);
	90	pie_obs = data(:,2);
	91	R_obs = data(:,3);
	92	de = data(:,4);
	93	dq = data(:,5);
	94
	95	%Country: Canada
	96	%Sample Range: 1981:2 to 2002:3
	97	%Observations: 86
	98	%Variables: Real GDP Growth [%], Inflation [annualized %], Nom Rate [%],
	99	% Exchange Rate Change [%], Terms of Trade Change [%]

+210

-0

tests/AIM/fsdat.m less more

	0	data_q = [
	1	18.02 1474.5 150.2
	2	17.94 1538.2 150.9
	3	18.01 1584.5 151.4
	4	18.42 1644.1 152
	5	18.73 1678.6 152.7
	6	19.46 1693.1 153.3
	7	19.55 1724 153.9
	8	19.56 1758.2 154.7
	9	19.79 1760.6 155.4
	10	19.77 1779.2 156
	11	19.82 1778.8 156.6
	12	20.03 1790.9 157.3
	13	20.12 1846 158
	14	20.1 1882.6 158.6
	15	20.14 1897.3 159.2
	16	20.22 1887.4 160
	17	20.27 1858.2 160.7
	18	20.34 1849.9 161.4
	19	20.39 1848.5 162
	20	20.42 1868.9 162.8
	21	20.47 1905.6 163.6
	22	20.56 1959.6 164.3
	23	20.62 1994.4 164.9
	24	20.78 2020.1 165.7
	25	21 2030.5 166.5
	26	21.2 2023.6 167.2
	27	21.33 2037.7 167.9
	28	21.62 2033.4 168.7
	29	21.71 2066.2 169.5
	30	22.01 2077.5 170.2
	31	22.15 2071.9 170.9
	32	22.27 2094 171.7
	33	22.29 2070.8 172.5
	34	22.56 2012.6 173.1
	35	22.64 2024.7 173.8
	36	22.77 2072.3 174.5
	37	22.88 2120.6 175.3
	38	22.92 2165 176.045
	39	22.91 2223.3 176.727
	40	22.94 2221.4 177.481
	41	23.03 2230.95 178.268
	42	23.13 2279.22 179.694
	43	23.22 2265.48 180.335
	44	23.32 2268.29 181.094
	45	23.4 2238.57 181.915
	46	23.45 2251.68 182.634
	47	23.51 2292.02 183.337
	48	23.56 2332.61 184.103
	49	23.63 2381.01 184.894
	50	23.75 2422.59 185.553
	51	23.81 2448.01 186.203
	52	23.87 2471.86 186.926
	53	23.94 2476.67 187.68
	54	24 2508.7 188.299
	55	24.07 2538.05 188.906
	56	24.12 2586.26 189.631
	57	24.29 2604.62 190.362
	58	24.35 2666.69 190.954
	59	24.41 2697.54 191.56
	60	24.52 2729.63 192.256
	61	24.64 2739.75 192.938
	62	24.77 2808.88 193.467
	63	24.88 2846.34 193.994
	64	25.01 2898.79 194.647
	65	25.17 2970.48 195.279
	66	25.32 3042.35 195.763
	67	25.53 3055.53 196.277
	68	25.79 3076.51 196.877
	69	26.02 3102.36 197.481
	70	26.14 3127.15 197.967
	71	26.31 3129.53 198.455
	72	26.6 3154.19 199.012
	73	26.9 3177.98 199.572
	74	27.21 3236.18 199.995
	75	27.49 3292.07 200.452
	76	27.75 3316.11 200.997
	77	28.12 3331.22 201.538
	78	28.39 3381.86 201.955
	79	28.73 3390.23 202.419
	80	29.14 3409.65 202.986
	81	29.51 3392.6 203.584
	82	29.94 3386.49 204.086
	83	30.36 3391.61 204.721
	84	30.61 3422.95 205.419
	85	31.02 3389.36 206.13
	86	31.5 3481.4 206.763
	87	31.93 3500.95 207.362
	88	32.27 3523.8 208
	89	32.54 3533.79 208.642
	90	33.02 3604.73 209.142
	91	33.2 3687.9 209.637
	92	33.49 3726.18 210.181
	93	33.95 3790.44 210.737
	94	34.36 3892.22 211.192
	95	34.94 3919.01 211.663
	96	35.61 3907.08 212.191
	97	36.29 3947.11 212.708
	98	37.01 3908.15 213.144
	99	37.79 3922.57 213.602
	100	38.96 3879.98 214.147
	101	40.13 3854.13 214.7
	102	41.05 3800.93 215.135
	103	41.66 3835.21 215.652
	104	42.41 3907.02 216.289
	105	43.19 3952.48 216.848
	106	43.69 4044.59 217.314
	107	44.15 4072.19 217.776
	108	44.77 4088.49 218.338
	109	45.57 4126.39 218.917
	110	46.32 4176.28 219.427
	111	47.07 4260.08 219.956
	112	47.66 4329.46 220.573
	113	48.63 4328.33 221.201
	114	49.42 4345.51 221.719
	115	50.41 4510.73 222.281
	116	51.27 4552.14 222.933
	117	52.35 4603.65 223.583
	118	53.51 4605.65 224.152
	119	54.65 4615.64 224.737
	120	55.82 4644.93 225.418
	121	56.92 4656.23 226.117
	122	58.18 4678.96 226.754
	123	59.55 4566.62 227.389
	124	61.01 4562.25 228.07
	125	62.59 4651.86 228.689
	126	64.15 4739.16 229.155
	127	65.37 4696.82 229.674
	128	66.65 4753.02 230.301
	129	67.87 4693.76 230.903
	130	68.86 4615.89 231.395
	131	69.72 4634.88 231.906
	132	70.66 4612.08 232.498
	133	71.44 4618.26 233.074
	134	72.08 4662.97 233.546
	135	72.83 4763.57 234.028
	136	73.48 4849 234.603
	137	74.19 4939.23 235.153
	138	75.02 5053.56 235.605
	139	75.58 5132.87 236.082
	140	76.25 5170.34 236.657
	141	76.81 5203.68 237.232
	142	77.63 5257.26 237.673
	143	78.25 5283.73 238.176
	144	78.76 5359.6 238.789
	145	79.45 5393.57 239.387
	146	79.81 5460.83 239.861
	147	80.22 5466.95 240.368
	148	80.84 5496.29 240.962
	149	81.45 5526.77 241.539
	150	82.09 5561.8 242.009
	151	82.68 5618 242.52
	152	83.33 5667.39 243.12
	153	84.09 5750.57 243.721
	154	84.67 5785.29 244.208
	155	85.56 5844.05 244.716
	156	86.66 5878.7 245.354
	157	87.44 5952.83 245.966
	158	88.45 6010.96 246.46
	159	89.39 6055.61 247.017
	160	90.13 6087.96 247.698
	161	90.88 6093.51 248.374
	162	92 6152.59 248.928
	163	93.18 6171.57 249.564
	164	94.14 6142.1 250.299
	165	95.11 6078.96 251.031
	166	96.27 6047.49 251.65
	167	97 6074.66 252.295
	168	97.7 6090.14 253.033
	169	98.31 6105.25 253.743
	170	99.13 6175.69 254.338
	171	99.79 6214.22 255.032
	172	100.17 6260.74 255.815
	173	100.88 6327.12 256.543
	174	101.84 6327.93 257.151
	175	102.35 6359.9 257.785
	176	102.83 6393.5 258.516
	177	103.51 6476.86 259.191
	178	104.13 6524.5 259.738
	179	104.71 6600.31 260.351
	180	105.39 6629.47 261.04
	181	106.09 6688.61 261.692
	182	106.75 6717.46 262.236
	183	107.24 6724.2 262.847
	184	107.75 6779.53 263.527
	185	108.29 6825.8 264.169
	186	108.91 6882 264.681
	187	109.24 6983.91 265.258
	188	109.74 7020 265.887
	189	110.23 7093.12 266.491
	190	111 7166.68 266.987
	191	111.43 7236.5 267.545
	192	111.76 7311.24 268.171
	193	112.08 7364.63 268.815
	194	];
	195	%GDPD GDPQ GPOP
	196
	197	series = zeros(193,2);
	198	series(:,2) = data_q(:,1);
	199	series(:,1) = 1000*data_q(:,2)./data_q(:,3);
	200
	201	Y_obs = series(:,1);
	202	P_obs = series(:,2);
	203
	204	series = series(2:193,:)./series(1:192,:);
	205
	206	gy_obs = series(:,1);
	207	gp_obs = series(:,2);
	208
	209	ti = [1950:0.25:1997.75];⏎

+103

-58

tests/Makefile.am less more

0		DYNARE_ROOT = $(abs_top_srcdir)/matlab
1
2		MODS = \
	0	MODFILES = \
3	1	ramst.mod \
4	2	ramst_a.mod \
5	3	example1.mod \
6	4	example2.mod \
7	5	example1_use_dll.mod \
8		example1_with_tags.mod\
	6	example1_with_tags.mod \
9	7	t_sgu_ex1.mod \
10	8	osr_example.mod \
11	9	optimal_policy/ramsey.mod \

19	17	comments.mod \
20	18	histval_sto.mod \
21	19	histval_det.mod \
22		expectation.mod \
	20	auxiliary_variables/test1.mod \
	21	expectations/expectation.mod \
	22	expectations/expectation_ss.mod \
	23	expectations/expectation_ss_old.mod \
	24	expectations/expectation_nested.mod \
	25	steady_state/walsh1_initval.mod \
	26	steady_state/walsh1_old_ss.mod \
	27	steady_state/walsh1_ssm.mod \
	28	steady_state/walsh1_ssm_block.mod \
	29	steady_state/multi_leads.mod \
23	30	steady_state_operator/standard.mod \
24	31	steady_state_operator/use_dll.mod \
25	32	steady_state_operator/block.mod \

46	53	fs2000/fs2000.mod \
47	54	fs2000/fs2000a.mod \
48	55	fs2000/fs2000c.mod \
	56	fs2000/fs2000d.mod \
49	57	homotopy/homotopy1_test.mod \
50	58	homotopy/homotopy2_test.mod \
51	59	homotopy/homotopy3_test.mod \

72	80	external_function/example1_no_deriv_functions_provided.mod \
73	81	external_function/example1_no_deriv_functions_provided_dll.mod \
74	82	seeds.mod \
75		simul/example1.mod
76
77		EXTRA_DIST = $(MODS) \
	83	identification/kim/kim2.mod \
	84	identification/as2007/as2007.mod \
	85	simul/example1.mod \
	86	conditional_forecasts/fs2000_est.mod \
	87	recursive/ls2003.mod \
	88	recursive/ls2003_bayesian.mod \
	89	kalman_filter_smoother/gen_data.mod \
	90	kalman_filter_smoother/algo1.mod \
	91	kalman_filter_smoother/algo2.mod \
	92	kalman_filter_smoother/algo3.mod \
	93	kalman_filter_smoother/algo4.mod \
	94	kalman_filter_smoother/algo4a.mod \
	95	kalman_filter_smoother/algo4b.mod \
	96	kalman_filter_smoother/algoH1.mod \
	97	kalman_filter_smoother/algoH2.mod \
	98	kalman_filter_smoother/algoH3.mod \
	99	kalman_filter_smoother/fs2000.mod \
	100	kalman_filter_smoother/fs2000_1.mod \
	101	kalman_filter_smoother/fs2000_2.mod \
	102	kalman_filter_smoother/fs2000a.mod \
	103	second_order/burnside_1.mod \
	104	second_order/ds1.mod \
	105	second_order/ds2.mod
	106
	107	EXTRA_DIST = \
	108	$(MODFILES) \
	109	homotopy/common.mod \
	110	block_bytecode/ls2003.mod \
	111	fs2000_ssfile_aux.m \
	112	printMakeCheckMatlabErrMsg.m \
	113	printMakeCheckOctaveErrMsg.m \
	114	ramst_initval_file_data.m \
	115	run_test.m \
	116	run_test_matlab.m \
78	117	run_test_octave.m \
79		block_bytecode/run_block_bytecode_tests.m \
	118	test.m \
	119	AIM/data_ca1.m \
	120	AIM/fs2000_b1L1L_AIM_steadystate.m \
	121	AIM/fs2000_b1L1L_steadystate.m \
	122	AIM/fsdat.m \
80	123	block_bytecode/run_ls2003.m \
81		block_bytecode/ls2003.mod \
	124	bvar_a_la_sims/bvar_sample.m \
82	125	external_function/extFunDeriv.m \
83	126	external_function/extFunNoDerivs.m \
84	127	external_function/extFunWithFirstAndSecondDerivs.m \
	128	expectations/expectation_ss_old_steadystate.m \
	129	steady_state/walsh1_old_ss_steadystate.m \
	130	fs2000/fs2000a_steadystate.m \
85	131	fs2000/fsdat_simul.m \
86		fs2000/fs2000a_steadystate.m \
87		identification/kim/kim2_steadystate.m \
88		identification/as2007/as2007_steadystate.m \
89		AIM/fs2000_b1L1L_steadystate.m \
90		AIM/fs2000_b1L1L_AIM_steadystate.m \
91		test.m \
92		objectives \
93		ramst_initval_file_data.m \
94		homotopy/common.mod \
95		bvar_a_la_sims/bvar_sample.m \
96		fs2000_ssfile_aux.m
97
98		TARGETS = check-matlab
	132	k_order_perturbation/run_fs2000kplusplus.m \
	133	ls2003/data_ca1.m \
	134	measurement_errors/data_ca1.m \
	135	missing/simulate_data_with_missing_observations.m \
	136	objectives/sgu_ex1.mat \
	137	conditional_forecasts/fsdat_simul.m \
	138	recursive/data_ca1.m \
	139	kalman_filter_smoother/fsdat_simul.m \
	140	kalman_filter_smoother/fs2000a_steadystate.m \
	141	identification/kim/kim2_steadystate.m
	142
	143	TARGETS =
	144
	145	if HAVE_CMD_LINE_MATLAB
	146	TARGETS += check-matlab
	147	endif
99	148
100	149	if HAVE_OCTAVE
101		TARGETS += check-octave check-block-bytecode
	150	TARGETS += check-octave
102	151	endif
103	152
104	153	check-local: $(TARGETS)
105	154
106		check-octave: $(MODS)
107		@set -e; \
108		for modfile in $(MODS); do \
109		$(OCTAVE) --norc --silent --no-history run_test_octave.m $$modfile $(DYNARE_ROOT) $(PACKAGE_VERSION); \
110		done
111		.PHONY: check-octave
112
113		check-block-bytecode:
114		cd block_bytecode && $(OCTAVE) --norc --silent --no-history run_block_bytecode_tests.m $(DYNARE_ROOT) $(PACKAGE_VERSION)
115		.PHONY: check-block-bytecode
116
117		check-matlab: $(MODS)
118		# MATLAB stuff to be added here
119		.PHONY: check-matlab
	155	check-matlab:
	156	DYNARE_VERSION="$(PACKAGE_VERSION)" MODFILES="$(MODFILES)" $(MATLAB)/bin/matlab -nodesktop -nosplash -r run_test_matlab
	157
	158	check-octave:
	159	DYNARE_VERSION="$(PACKAGE_VERSION)" MODFILES="$(MODFILES)" $(OCTAVE) --norc --silent --no-history run_test_octave.m
120	160
121	161	clean-local:
122		rm -f $(patsubst %.mod, %.m, $(MODS)) \
123		$(patsubst %.mod, %_static.*, $(MODS)) \
124		$(patsubst %.mod, %_objective_static.m, $(MODS)) \
125		$(patsubst %.mod, %_dynamic.*, $(MODS))
126
127		rm -f $(patsubst %.mod, %_results.mat, $(MODS)) \
128		$(patsubst %.mod, %_mode.mat, $(MODS)) \
129		$(patsubst %.mod, %_mh_mode.mat, $(MODS)) \
130		$(patsubst %.mod, %_mean.mat, $(MODS)) \
131		$(patsubst %.mod, %_pindx.mat, $(MODS)) \
132		$(patsubst %.mod, %_params.mat, $(MODS)) \
133		$(patsubst %.mod, %_simul, $(MODS)) \
134		$(patsubst %.mod, %.log, $(MODS))
135
136		rm -rf $(patsubst %.mod, %, $(MODS))
137
138		rm -f $(patsubst %.mod, %*.pdf, $(MODS)) \
139		$(patsubst %.mod, %*.eps, $(MODS)) \
140		$(patsubst %.mod, %*.fig, $(MODS))
	162	rm -f $(patsubst %.mod, %.m, $(MODFILES)) \
	163	$(patsubst %.mod, %_static.*, $(MODFILES)) \
	164	$(patsubst %.mod, %_objective_static.m, $(MODFILES)) \
	165	$(patsubst %.mod, %_dynamic.*, $(MODFILES))
	166
	167	rm -f $(patsubst %.mod, %_results.mat, $(MODFILES)) \
	168	$(patsubst %.mod, %_mode.mat, $(MODFILES)) \
	169	$(patsubst %.mod, %_mh_mode.mat, $(MODFILES)) \
	170	$(patsubst %.mod, %_mean.mat, $(MODFILES)) \
	171	$(patsubst %.mod, %_pindx.mat, $(MODFILES)) \
	172	$(patsubst %.mod, %_params.mat, $(MODFILES)) \
	173	$(patsubst %.mod, %_simul, $(MODFILES)) \
	174	$(patsubst %.mod, %.log, $(MODFILES))
	175
	176	rm -rf $(patsubst %.mod, %, $(MODFILES))
	177
	178	rm -f $(patsubst %.mod, %*.pdf, $(MODFILES)) \
	179	$(patsubst %.mod, %*.eps, $(MODFILES)) \
	180	$(patsubst %.mod, %*.fig, $(MODFILES))
141	181
142	182	rm -f $(shell find -name g1.mat) \
143	183	$(shell find -name g2.mat) \

158	198
159	199	rm -rf partial_information/PItest3aHc0PCLsimModPiYrVarobsAll_PCL* partial_information/PItest3aHc0PCLsimModPiYrVarobsCNR_PCL*
160	200
161		rm -rf block_bytecode/ws block_bytecode/ls2003_tmp*
	201	rm -rf block_bytecode/ls2003_tmp*
	202
	203	rm -f $(shell find -name wsOct) \
	204	$(shell find -name wsMat.mat)
	205
	206	rm -f run_test_matlab_output.txt run_test_octave_output.txt

+102

-58

tests/Makefile.in less more

32	32	POST_UNINSTALL = :
33	33	build_triplet = @build@
34	34	host_triplet = @host@
35		@HAVE_OCTAVE_TRUE@am__append_1 = check-octave check-block-bytecode
	35	@HAVE_CMD_LINE_MATLAB_TRUE@am__append_1 = check-matlab
	36	@HAVE_OCTAVE_TRUE@am__append_2 = check-octave
36	37	subdir = tests
37	38	DIST_COMMON = $(srcdir)/Makefile.am $(srcdir)/Makefile.in
38	39	ACLOCAL_M4 = $(top_srcdir)/aclocal.m4

42	43	$(top_srcdir)/m4/ax_latex_bibtex_test.m4 \
43	44	$(top_srcdir)/m4/ax_latex_class.m4 \
44	45	$(top_srcdir)/m4/ax_latex_test.m4 \
45		$(top_srcdir)/m4/ax_prog_ln_s.m4 \
	46	$(top_srcdir)/m4/ax_matlab.m4 $(top_srcdir)/m4/ax_prog_ln_s.m4 \
46	47	$(top_srcdir)/m4/ax_pthread.m4 $(top_srcdir)/m4/ax_tex_test.m4 \
47	48	$(top_srcdir)/configure.ac
48	49	am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \

104	105	LN_S = @LN_S@
105	106	LTLIBOBJS = @LTLIBOBJS@
106	107	MAKEINFO = @MAKEINFO@
	108	MATLAB = @MATLAB@
107	109	MKDIR_P = @MKDIR_P@
108	110	OBJEXT = @OBJEXT@
109	111	OCTAVE = @OCTAVE@

183	185	top_build_prefix = @top_build_prefix@
184	186	top_builddir = @top_builddir@
185	187	top_srcdir = @top_srcdir@
186		DYNARE_ROOT = $(abs_top_srcdir)/matlab
187		MODS = \
	188	MODFILES = \
188	189	ramst.mod \
189	190	ramst_a.mod \
190	191	example1.mod \
191	192	example2.mod \
192	193	example1_use_dll.mod \
193		example1_with_tags.mod\
	194	example1_with_tags.mod \
194	195	t_sgu_ex1.mod \
195	196	osr_example.mod \
196	197	optimal_policy/ramsey.mod \

204	205	comments.mod \
205	206	histval_sto.mod \
206	207	histval_det.mod \
207		expectation.mod \
	208	auxiliary_variables/test1.mod \
	209	expectations/expectation.mod \
	210	expectations/expectation_ss.mod \
	211	expectations/expectation_ss_old.mod \
	212	expectations/expectation_nested.mod \
	213	steady_state/walsh1_initval.mod \
	214	steady_state/walsh1_old_ss.mod \
	215	steady_state/walsh1_ssm.mod \
	216	steady_state/walsh1_ssm_block.mod \
	217	steady_state/multi_leads.mod \
208	218	steady_state_operator/standard.mod \
209	219	steady_state_operator/use_dll.mod \
210	220	steady_state_operator/block.mod \

231	241	fs2000/fs2000.mod \
232	242	fs2000/fs2000a.mod \
233	243	fs2000/fs2000c.mod \
	244	fs2000/fs2000d.mod \
234	245	homotopy/homotopy1_test.mod \
235	246	homotopy/homotopy2_test.mod \
236	247	homotopy/homotopy3_test.mod \

257	268	external_function/example1_no_deriv_functions_provided.mod \
258	269	external_function/example1_no_deriv_functions_provided_dll.mod \
259	270	seeds.mod \
260		simul/example1.mod
261
262		EXTRA_DIST = $(MODS) \
	271	identification/kim/kim2.mod \
	272	identification/as2007/as2007.mod \
	273	simul/example1.mod \
	274	conditional_forecasts/fs2000_est.mod \
	275	recursive/ls2003.mod \
	276	recursive/ls2003_bayesian.mod \
	277	kalman_filter_smoother/gen_data.mod \
	278	kalman_filter_smoother/algo1.mod \
	279	kalman_filter_smoother/algo2.mod \
	280	kalman_filter_smoother/algo3.mod \
	281	kalman_filter_smoother/algo4.mod \
	282	kalman_filter_smoother/algo4a.mod \
	283	kalman_filter_smoother/algo4b.mod \
	284	kalman_filter_smoother/algoH1.mod \
	285	kalman_filter_smoother/algoH2.mod \
	286	kalman_filter_smoother/algoH3.mod \
	287	kalman_filter_smoother/fs2000.mod \
	288	kalman_filter_smoother/fs2000_1.mod \
	289	kalman_filter_smoother/fs2000_2.mod \
	290	kalman_filter_smoother/fs2000a.mod \
	291	second_order/burnside_1.mod \
	292	second_order/ds1.mod \
	293	second_order/ds2.mod
	294
	295	EXTRA_DIST = \
	296	$(MODFILES) \
	297	homotopy/common.mod \
	298	block_bytecode/ls2003.mod \
	299	fs2000_ssfile_aux.m \
	300	printMakeCheckMatlabErrMsg.m \
	301	printMakeCheckOctaveErrMsg.m \
	302	ramst_initval_file_data.m \
	303	run_test.m \
	304	run_test_matlab.m \
263	305	run_test_octave.m \
264		block_bytecode/run_block_bytecode_tests.m \
	306	test.m \
	307	AIM/data_ca1.m \
	308	AIM/fs2000_b1L1L_AIM_steadystate.m \
	309	AIM/fs2000_b1L1L_steadystate.m \
	310	AIM/fsdat.m \
265	311	block_bytecode/run_ls2003.m \
266		block_bytecode/ls2003.mod \
	312	bvar_a_la_sims/bvar_sample.m \
267	313	external_function/extFunDeriv.m \
268	314	external_function/extFunNoDerivs.m \
269	315	external_function/extFunWithFirstAndSecondDerivs.m \
	316	expectations/expectation_ss_old_steadystate.m \
	317	steady_state/walsh1_old_ss_steadystate.m \
	318	fs2000/fs2000a_steadystate.m \
270	319	fs2000/fsdat_simul.m \
271		fs2000/fs2000a_steadystate.m \
272		identification/kim/kim2_steadystate.m \
273		identification/as2007/as2007_steadystate.m \
274		AIM/fs2000_b1L1L_steadystate.m \
275		AIM/fs2000_b1L1L_AIM_steadystate.m \
276		test.m \
277		objectives \
278		ramst_initval_file_data.m \
279		homotopy/common.mod \
280		bvar_a_la_sims/bvar_sample.m \
281		fs2000_ssfile_aux.m
282
283		TARGETS = check-matlab $(am__append_1)
	320	k_order_perturbation/run_fs2000kplusplus.m \
	321	ls2003/data_ca1.m \
	322	measurement_errors/data_ca1.m \
	323	missing/simulate_data_with_missing_observations.m \
	324	objectives/sgu_ex1.mat \
	325	conditional_forecasts/fsdat_simul.m \
	326	recursive/data_ca1.m \
	327	kalman_filter_smoother/fsdat_simul.m \
	328	kalman_filter_smoother/fs2000a_steadystate.m \
	329	identification/kim/kim2_steadystate.m
	330
	331	TARGETS = $(am__append_1) $(am__append_2)
284	332	all: all-am
285	333
286	334	.SUFFIXES:

463	511
464	512	check-local: $(TARGETS)
465	513
466		check-octave: $(MODS)
467		@set -e; \
468		for modfile in $(MODS); do \
469		$(OCTAVE) --norc --silent --no-history run_test_octave.m $$modfile $(DYNARE_ROOT) $(PACKAGE_VERSION); \
470		done
471		.PHONY: check-octave
472
473		check-block-bytecode:
474		cd block_bytecode && $(OCTAVE) --norc --silent --no-history run_block_bytecode_tests.m $(DYNARE_ROOT) $(PACKAGE_VERSION)
475		.PHONY: check-block-bytecode
476
477		check-matlab: $(MODS)
478		# MATLAB stuff to be added here
479		.PHONY: check-matlab
	514	check-matlab:
	515	DYNARE_VERSION="$(PACKAGE_VERSION)" MODFILES="$(MODFILES)" $(MATLAB)/bin/matlab -nodesktop -nosplash -r run_test_matlab
	516
	517	check-octave:
	518	DYNARE_VERSION="$(PACKAGE_VERSION)" MODFILES="$(MODFILES)" $(OCTAVE) --norc --silent --no-history run_test_octave.m
480	519
481	520	clean-local:
482		rm -f $(patsubst %.mod, %.m, $(MODS)) \
483		$(patsubst %.mod, %_static.*, $(MODS)) \
484		$(patsubst %.mod, %_objective_static.m, $(MODS)) \
485		$(patsubst %.mod, %_dynamic.*, $(MODS))
486
487		rm -f $(patsubst %.mod, %_results.mat, $(MODS)) \
488		$(patsubst %.mod, %_mode.mat, $(MODS)) \
489		$(patsubst %.mod, %_mh_mode.mat, $(MODS)) \
490		$(patsubst %.mod, %_mean.mat, $(MODS)) \
491		$(patsubst %.mod, %_pindx.mat, $(MODS)) \
492		$(patsubst %.mod, %_params.mat, $(MODS)) \
493		$(patsubst %.mod, %_simul, $(MODS)) \
494		$(patsubst %.mod, %.log, $(MODS))
495
496		rm -rf $(patsubst %.mod, %, $(MODS))
497
498		rm -f $(patsubst %.mod, %*.pdf, $(MODS)) \
499		$(patsubst %.mod, %*.eps, $(MODS)) \
500		$(patsubst %.mod, %*.fig, $(MODS))
	521	rm -f $(patsubst %.mod, %.m, $(MODFILES)) \
	522	$(patsubst %.mod, %_static.*, $(MODFILES)) \
	523	$(patsubst %.mod, %_objective_static.m, $(MODFILES)) \
	524	$(patsubst %.mod, %_dynamic.*, $(MODFILES))
	525
	526	rm -f $(patsubst %.mod, %_results.mat, $(MODFILES)) \
	527	$(patsubst %.mod, %_mode.mat, $(MODFILES)) \
	528	$(patsubst %.mod, %_mh_mode.mat, $(MODFILES)) \
	529	$(patsubst %.mod, %_mean.mat, $(MODFILES)) \
	530	$(patsubst %.mod, %_pindx.mat, $(MODFILES)) \
	531	$(patsubst %.mod, %_params.mat, $(MODFILES)) \
	532	$(patsubst %.mod, %_simul, $(MODFILES)) \
	533	$(patsubst %.mod, %.log, $(MODFILES))
	534
	535	rm -rf $(patsubst %.mod, %, $(MODFILES))
	536
	537	rm -f $(patsubst %.mod, %*.pdf, $(MODFILES)) \
	538	$(patsubst %.mod, %*.eps, $(MODFILES)) \
	539	$(patsubst %.mod, %*.fig, $(MODFILES))
501	540
502	541	rm -f $(shell find -name g1.mat) \
503	542	$(shell find -name g2.mat) \

518	557
519	558	rm -rf partial_information/PItest3aHc0PCLsimModPiYrVarobsAll_PCL* partial_information/PItest3aHc0PCLsimModPiYrVarobsCNR_PCL*
520	559
521		rm -rf block_bytecode/ws block_bytecode/ls2003_tmp*
	560	rm -rf block_bytecode/ls2003_tmp*
	561
	562	rm -f $(shell find -name wsOct) \
	563	$(shell find -name wsMat.mat)
	564
	565	rm -f run_test_matlab_output.txt run_test_octave_output.txt
522	566
523	567	# Tell versions [3.59,3.63) of GNU make to not export all variables.
524	568	# Otherwise a system limit (for SysV at least) may be exceeded.

+14

-0

tests/auxiliary_variables/test1.mod less more

	0	var x, y;
	1	varexo e;
	2
	3	model;
	4	expectation(-2)(x(+4)*y(+1)) = 1;
	5	y = exp(e);
	6	end;
	7
	8	initval;
	9	y = 1;
	10	x = 1;
	11	end;
	12
	13	steady;

+0

-61

~~tests/block_bytecode/run_block_bytecode_tests.m~~ less more

0		## Copyright (C) 2010 Dynare Team
1		##
2		## This file is part of Dynare.
3		##
4		## Dynare is free software: you can redistribute it and/or modify
5		## it under the terms of the GNU General Public License as published by
6		## the Free Software Foundation, either version 3 of the License, or
7		## (at your option) any later version.
8		##
9		## Dynare is distributed in the hope that it will be useful,
10		## but WITHOUT ANY WARRANTY; without even the implied warranty of
11		## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12		## GNU General Public License for more details.
13		##
14		## You should have received a copy of the GNU General Public License
15		## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
16
17		addpath(argv(){1})
18
19		if !strcmp(dynare_version(), argv(){2})
20		error("Incorrect version of Dynare is being tested")
21		endif
22
23		## Ask gnuplot to create graphics in text mode
24		## Note that setenv() was introduced in Octave 3.0.2, for compatibility
25		## with MATLAB
26		putenv("GNUTERM", "dumb")
27
28		for block = 0:1
29		for bytecode = 0:1
30		## Recall that solve_algo=7 and stack_solve_algo=2 are not supported
31		## under Octave
32		default_solve_algo = 2;
33		default_stack_solve_algo = 0;
34		if !block && !bytecode
35		solve_algos = 0:4;
36		stack_solve_algos = 0;
37		elseif block && !bytecode
38		solve_algos = [0:4 6 8];
39		stack_solve_algos = [0 1 3 4];
40		else
41		solve_algos = [0:6 8];
42		stack_solve_algos = [0 1 3:5];
43		endif
44
45		for i = 1:length(solve_algos)
46		save ws
47		run_ls2003(block, bytecode, solve_algos(i), default_stack_solve_algo)
48		load ws
49		endfor
50		for i = 1:length(stack_solve_algos)
51		save ws
52		run_ls2003(block, bytecode, default_solve_algo, stack_solve_algos(i))
53		load ws
54		endfor
55		endfor
56		endfor
57
58		## Local variables:
59		## mode: Octave
60		## End:

+28

-26

tests/block_bytecode/run_ls2003.m less more

0		## Copyright (C) 2010 Dynare Team
1		##
2		## This file is part of Dynare.
3		##
4		## Dynare is free software: you can redistribute it and/or modify
5		## it under the terms of the GNU General Public License as published by
6		## the Free Software Foundation, either version 3 of the License, or
7		## (at your option) any later version.
8		##
9		## Dynare is distributed in the hope that it will be useful,
10		## but WITHOUT ANY WARRANTY; without even the implied warranty of
11		## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12		## GNU General Public License for more details.
13		##
14		## You should have received a copy of the GNU General Public License
15		## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	0	function run_ls2003(block, bytecode, solve_algo, stack_solve_algo)
16	1
17		function run_ls2003(block, bytecode, solve_algo, stack_solve_algo)
18		printf("\nTEST: ls2003 (block=%d, bytecode=%d, solve_algo=%d, stack_solve_algo=%d)...\n", block, bytecode, solve_algo, stack_solve_algo);
19		fid = fopen("ls2003_tmp.mod", "w");
	2	% Copyright (C) 2010-2011 Dynare Team
	3	%
	4	% This file is part of Dynare.
	5	%
	6	% Dynare is free software: you can redistribute it and/or modify
	7	% it under the terms of the GNU General Public License as published by
	8	% the Free Software Foundation, either version 3 of the License, or
	9	% (at your option) any later version.
	10	%
	11	% Dynare is distributed in the hope that it will be useful,
	12	% but WITHOUT ANY WARRANTY; without even the implied warranty of
	13	% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	14	% GNU General Public License for more details.
	15	%
	16	% You should have received a copy of the GNU General Public License
	17	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	18
	19	disp(['TEST: ls2003 (block=' num2str(block) ', bytecode=' ...
	20	num2str(bytecode) ', solve_algo=' num2str(solve_algo) ...
	21	', stack_solve_algo=' num2str(stack_solve_algo) ')...']);
	22	fid = fopen('ls2003_tmp.mod', 'w');
20	23	assert(fid > 0);
21		fprintf(fid, "@#define block = %d\n@#define bytecode = %d\n@#define solve_algo = %d\n@#define stack_solve_algo = %d\n@#include \"ls2003.mod\"\n", block, bytecode, solve_algo, stack_solve_algo)
	24	fprintf(fid, ['@#define block = %d\n@#define bytecode = %d\n' ...
	25	'@#define solve_algo = %d\n@#define stack_solve_algo = %d\n' ...
	26	'@#include \"ls2003.mod\"\n'], block, bytecode, ...
	27	solve_algo, stack_solve_algo);
22	28	fclose(fid);
23		dynare("ls2003_tmp.mod")
24		endfunction
25
26		## Local variables:
27		## mode: Octave
28		## End:
	29	dynare('ls2003_tmp.mod','console')
	30	end

+135

-0

tests/conditional_forecasts/fs2000_est.mod less more

	0	// See fs2000.mod in the examples/ directory for details on the model
	1
	2	var m P c e W R k d n l gy_obs gp_obs y dA;
	3	varexo e_a e_m;
	4
	5	parameters alp bet gam mst rho psi del;
	6
	7	alp = 0.33;
	8	bet = 0.99;
	9	gam = 0.003;
	10	mst = 1.011;
	11	rho = 0.7;
	12	psi = 0.787;
	13	del = 0.02;
	14
	15	model;
	16	dA = exp(gam+e_a);
	17	log(m) = (1-rho)log(mst) + rholog(m(-1))+e_m;
	18	-P/(c(+1)P(+1)m)+betP(+1)(alpexp(-alp(gam+log(e(+1))))k^(alp-1)n(+1)^(1-alp)+(1-del)exp(-(gam+log(e(+1)))))/(c(+2)P(+2)*m(+1))=0;
	19	W = l/n;
	20	-(psi/(1-psi))(cP/(1-n))+l/n = 0;
	21	R = P(1-alp)exp(-alp(gam+e_a))k(-1)^alp*n^(-alp)/W;
	22	1/(cP)-betP(1-alp)exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)/(mlc(+1)P(+1)) = 0;
	23	c+k = exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)+(1-del)exp(-(gam+e_a))*k(-1);
	24	P*c = m;
	25	m-1+d = l;
	26	e = exp(e_a);
	27	y = k(-1)^alpn^(1-alp)exp(-alp*(gam+e_a));
	28	gy_obs = dA*y/y(-1);
	29	gp_obs = (P/P(-1))*m(-1)/dA;
	30	end;
	31
	32	initval;
	33	k = 6;
	34	m = mst;
	35	P = 2.25;
	36	c = 0.45;
	37	e = 1;
	38	W = 4;
	39	R = 1.02;
	40	d = 0.85;
	41	n = 0.19;
	42	l = 0.86;
	43	y = 0.6;
	44	gy_obs = exp(gam);
	45	gp_obs = exp(-gam);
	46	dA = exp(gam);
	47	end;
	48
	49	shocks;
	50	var e_a; stderr 0.014;
	51	var e_m; stderr 0.005;
	52	end;
	53
	54	steady_state_model;
	55	dA = exp(gam);
	56	gst = 1/dA;
	57	m = mst;
	58
	59	khst = ( (1-gstbet(1-del)) / (alpgst^alpbet) )^(1/(alp-1));
	60	xist = ( ((khstgst)^alp - (1-gst(1-del))*khst)/mst )^(-1);
	61	nust = psimst^2/( (1-alp)(1-psi)betgst^alp*khst^alp );
	62	n = xist/(nust+xist);
	63	P = xist + nust;
	64	k = khst*n;
	65
	66	l = psimstn/( (1-psi)*(1-n) );
	67	c = mst/P;
	68	d = l - mst + 1;
	69	y = k^alpn^(1-alp)gst^alp;
	70	R = mst/bet;
	71
	72	W = l/n;
	73	e = 1;
	74
	75	gp_obs = m/dA;
	76	gy_obs = dA;
	77	end;
	78
	79	steady;
	80
	81	check;
	82
	83	estimated_params;
	84	alp, beta_pdf, 0.356, 0.02;
	85	bet, beta_pdf, 0.993, 0.002;
	86	gam, normal_pdf, 0.0085, 0.003;
	87	mst, normal_pdf, 1.0002, 0.007;
	88	rho, beta_pdf, 0.129, 0.223;
	89	psi, beta_pdf, 0.65, 0.05;
	90	del, beta_pdf, 0.01, 0.005;
	91	stderr e_a, inv_gamma_pdf, 0.035449, inf;
	92	stderr e_m, inv_gamma_pdf, 0.008862, inf;
	93	end;
	94
	95	varobs gp_obs gy_obs;
	96
	97	options_.solve_tolf = 1e-12;
	98
	99	// Metropolis replications are too few, this is only for testing purpose
	100	estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=10000,mh_nblocks=1,mh_jscale=0.8);
	101
	102	conditional_forecast_paths;
	103	var gy_obs;
	104	periods 1 2 3:5;
	105	values 0.01 -0.02 0;
	106	var gp_obs;
	107	periods 1:5;
	108	values 0.05;
	109	end;
	110
	111	conditional_forecast(parameter_set=prior_mode, controlled_varexo=(e_a,e_m));
	112
	113	if ~(exist('OCTAVE_VERSION') && octave_ver_less_than('3.4.0'))
	114	plot_conditional_forecast(periods=10) gy_obs gp_obs;
	115	end
	116
	117	conditional_forecast(parameter_set=posterior_mode, controlled_varexo=(e_a,e_m));
	118
	119	if ~(exist('OCTAVE_VERSION') && octave_ver_less_than('3.4.0'))
	120	plot_conditional_forecast(periods=10) gy_obs gp_obs;
	121	end
	122
	123	conditional_forecast(parameter_set=posterior_mean, controlled_varexo=(e_a,e_m));
	124
	125	if ~(exist('OCTAVE_VERSION') && octave_ver_less_than('3.4.0'))
	126	plot_conditional_forecast(periods=10) gy_obs gp_obs;
	127	end
	128
	129	conditional_forecast(parameter_set=posterior_median, controlled_varexo=(e_a,e_m));
	130
	131	if ~(exist('OCTAVE_VERSION') && octave_ver_less_than('3.4.0'))
	132	plot_conditional_forecast(periods=10) gy_obs gp_obs;
	133	end
	134

+828

-0

tests/conditional_forecasts/fsdat_simul.m less more

	0	gy_obs =[
	1	1.0030045
	2	0.99990934
	3	1.0172778
	4	0.99464043
	5	1.0253423
	6	1.0150215
	7	0.97772557
	8	0.97832186
	9	1.0159561
	10	1.0085937
	11	1.0102649
	12	1.0007604
	13	1.0112596
	14	1.0163279
	15	1.0173204
	16	1.0103896
	17	1.0006493
	18	0.99447124
	19	1.0196405
	20	1.0089304
	21	0.99650737
	22	1.0139707
	23	0.97865842
	24	1.0192225
	25	0.99139628
	26	1.0141362
	27	1.0196612
	28	0.97483476
	29	0.99686151
	30	0.99594464
	31	1.0000642
	32	1.0172243
	33	1.0025773
	34	0.97199728
	35	1.0217815
	36	1.0219949
	37	0.99490252
	38	1.0190728
	39	1.0111337
	40	1.0003792
	41	0.98969164
	42	1.010438
	43	1.0216309
	44	1.0016671
	45	1.0357588
	46	0.98803787
	47	1.0093457
	48	1.0177035
	49	0.98548204
	50	1.0274294
	51	1.0141377
	52	1.0091174
	53	0.96427632
	54	1.0083272
	55	1.0007882
	56	0.99038262
	57	1.0031336
	58	0.99500213
	59	0.98203716
	60	0.9889452
	61	1.011632
	62	0.99451949
	63	0.97291047
	64	0.98750871
	65	0.99992418
	66	0.97657318
	67	0.99930448
	68	1.0008515
	69	1.0044064
	70	0.98133792
	71	1.0091702
	72	1.0087023
	73	1.0119876
	74	1.0143019
	75	1.0311061
	76	0.99340471
	77	1.0057428
	78	0.99197259
	79	1.0071019
	80	0.99448853
	81	1.0061819
	82	1.0070088
	83	0.9950913
	84	1.0302318
	85	0.9817693
	86	1.0072885
	87	0.97355282
	88	0.98782586
	89	1.0136674
	90	0.99863956
	91	1.0205668
	92	0.99611384
	93	1.0073805
	94	0.99691529
	95	1.0089194
	96	1.0030467
	97	1.0112006
	98	1.0260523
	99	0.97803331
	100	0.99423374
	101	1.0043727
	102	1.0140173
	103	1.0111473
	104	0.99524348
	105	0.99775943
	106	0.9958619
	107	0.9982344
	108	1.0210212
	109	1.0022288
	110	1.0014801
	111	1.011456
	112	1.0124871
	113	0.99843599
	114	0.99324886
	115	0.99912838
	116	1.003327
	117	1.0072071
	118	1.0115223
	119	1.009266
	120	1.0070554
	121	1.0129916
	122	1.0053413
	123	1.0051638
	124	0.99212952
	125	1.0214422
	126	0.98716707
	127	0.99905788
	128	0.98877357
	129	0.98568476
	130	0.99767393
	131	1.0061791
	132	0.98423439
	133	0.99492949
	134	0.98786999
	135	0.99754239
	136	1.0168619
	137	0.99472384
	138	1.0041658
	139	0.98123181
	140	1.0112882
	141	0.99245422
	142	1.0010255
	143	1.0017799
	144	1.0089968
	145	1.0072824
	146	0.99768475
	147	1.0044726
	148	1.0118678
	149	1.0056385
	150	1.0276965
	151	1.0025122
	152	1.0065161
	153	1.0234338
	154	0.99760167
	155	0.98922272
	156	1.0101918
	157	1.011615
	158	1.0085286
	159	1.0074455
	160	0.98866757
	161	0.99959012
	162	1.0129881
	163	0.99127881
	164	0.97971901
	165	1.0185314
	166	1.020054
	167	1.0132605
	168	0.98063643
	169	0.99490253
	170	1.0101531
	171	1.0004526
	172	1.0059109
	173	0.98974491
	174	1.0062391
	175	1.0216488
	176	0.99398446
	177	0.97786609
	178	1.0019274
	179	0.99587153
	180	1.0095881
	181	1.0111887
	182	0.99457649
	183	0.97896734
	184	1.000172
	185	1.0142951
	186	1.0034224
	187	1.0037242
	188	1.0016059
	189	1.016556
	190	0.99687023
	191	1.0117844
	192	1.0059212
	193	0.98083159
	194	0.98638851
	195	1.0128713
	196	1.0096232
	197	1.0115891
	198	1.0011213
	199	1.0147105
	200	1.0066344
	201	1.0164429
	202	0.99825038
	203	0.99403411
	204
	205	];
	206
	207	gp_obs =[
	208	1.0079715
	209	1.0074573
	210	1.0153107
	211	1.0152677
	212	1.0011653
	213	0.99950061
	214	1.0328311
	215	1.0192317
	216	1.009827
	217	0.99588916
	218	1.007474
	219	1.0113061
	220	0.98696624
	221	0.99978663
	222	0.98240542
	223	0.98861723
	224	0.99008763
	225	1.0185076
	226	1.0052452
	227	0.99447194
	228	1.0092685
	229	1.01208
	230	1.0105237
	231	0.98513875
	232	1.0165628
	233	0.99485934
	234	1.0050255
	235	1.0140756
	236	1.0093128
	237	1.0155868
	238	1.0107023
	239	0.99212762
	240	1.0095465
	241	1.0028435
	242	1.0069437
	243	1.0070473
	244	1.0145902
	245	1.0186922
	246	1.0059917
	247	1.0113072
	248	1.0107386
	249	0.99769196
	250	0.99793444
	251	1.0050791
	252	0.98307821
	253	1.0107594
	254	0.99689982
	255	0.98667064
	256	0.9991662
	257	0.98274722
	258	0.98422032
	259	0.99393016
	260	1.0118567
	261	0.99912781
	262	1.0023744
	263	1.0086662
	264	1.0164773
	265	1.0169327
	266	1.0372478
	267	1.0314242
	268	1.0004256
	269	1.0110541
	270	1.0076575
	271	1.0119851
	272	1.0055188
	273	1.0213959
	274	1.0234416
	275	1.0264917
	276	1.0292725
	277	1.0385184
	278	1.0200999
	279	1.0107697
	280	1.008583
	281	1.0200332
	282	1.0030413
	283	1.0108659
	284	1.0185145
	285	1.0168619
	286	1.0180462
	287	1.0239657
	288	1.0205509
	289	1.0189973
	290	1.0246446
	291	1.0135089
	292	1.0352973
	293	1.0099289
	294	1.0266474
	295	1.0279829
	296	1.0101653
	297	1.041216
	298	1.0103861
	299	1.0114727
	300	1.0054605
	301	1.0190722
	302	1.0114837
	303	1.0179213
	304	1.006082
	305	1.0049696
	306	1.0143629
	307	0.9971036
	308	1.0005602
	309	1.0078403
	310	1.0240222
	311	1.0195063
	312	1.0355136
	313	1.0218743
	314	1.0171331
	315	1.0049817
	316	1.0140974
	317	1.0168431
	318	1.0049966
	319	1.0045568
	320	1.0156414
	321	1.0273055
	322	1.0197653
	323	1.0030624
	324	1.0154993
	325	0.99782084
	326	0.99711648
	327	1.014408
	328	1.0057417
	329	0.99936837
	330	1.0096934
	331	1.0095138
	332	1.0057734
	333	1.0114497
	334	1.0059784
	335	1.0328889
	336	1.0098032
	337	1.0041114
	338	1.0101247
	339	1.0181588
	340	1.0115712
	341	1.0227509
	342	1.0065104
	343	1.0110902
	344	1.0298169
	345	1.0089532
	346	1.0368733
	347	1.0123033
	348	1.0060763
	349	1.0150937
	350	1.0239325
	351	0.99555536
	352	0.99861271
	353	1.0076201
	354	0.99941535
	355	1.0119522
	356	1.0129183
	357	0.99288924
	358	1.0260784
	359	1.0144982
	360	1.0121985
	361	1.0234916
	362	1.02215
	363	1.0190118
	364	1.0172679
	365	1.0118398
	366	1.0002123
	367	1.0092124
	368	1.0071943
	369	0.99508468
	370	1.0019303
	371	1.0030733
	372	0.9964198
	373	1.0027298
	374	0.99797614
	375	1.006942
	376	0.99793928
	377	1.0083214
	378	1.0283732
	379	1.0111102
	380	1.016936
	381	1.0229061
	382	0.98846454
	383	1.0015387
	384	1.0201769
	385	1.0079822
	386	1.0064007
	387	1.0095543
	388	1.0092207
	389	1.0135485
	390	1.0198974
	391	1.0140252
	392	1.0128686
	393	1.0092903
	394	1.0141974
	395	1.0023492
	396	0.99731455
	397	1.0026598
	398	0.99303643
	399	1.0036469
	400	1.0160975
	401	1.0368378
	402	1.0139625
	403	1.01493
	404	1.0113531
	405	1.0114548
	406	0.99833441
	407	0.99648401
	408	0.97645361
	409	1.0154053
	410	1.01703
	411
	412	];
	413
	414	Y_obs =[
	415	1
	416	0.99690484
	417	1.0111781
	418	1.0028141
	419	1.0251518
	420	1.0371688
	421	1.0118899
	422	0.98720726
	423	1.0001589
	424	1.0057481
	425	1.0130085
	426	1.0107643
	427	1.0190194
	428	1.0323428
	429	1.0466587
	430	1.0540438
	431	1.0516886
	432	1.0431553
	433	1.0597913
	434	1.0657172
	435	1.0592201
	436	1.0701863
	437	1.0458402
	438	1.0620582
	439	1.0504499
	440	1.0615817
	441	1.0782384
	442	1.0500687
	443	1.0439257
	444	1.0368658
	445	1.0339255
	446	1.0481453
	447	1.0477181
	448	1.0167109
	449	1.0354878
	450	1.0544782
	451	1.0463762
	452	1.0624445
	453	1.0705737
	454	1.0679484
	455	1.0546356
	456	1.0620691
	457	1.0806955
	458	1.0793581
	459	1.1121124
	460	1.0971458
	461	1.1034869
	462	1.1181859
	463	1.1006634
	464	1.1250883
	465	1.1362214
	466	1.1423343
	467	1.1036061
	468	1.1089288
	469	1.1067125
	470	1.0940906
	471	1.0942197
	472	1.0862174
	473	1.06525
	474	1.0511907
	475	1.0598182
	476	1.0513331
	477	1.0212391
	478	1.0057433
	479	1.002663
	480	0.97623167
	481	0.97253165
	482	0.97037865
	483	0.97178055
	484	0.95011397
	485	0.95627969
	486	0.96197747
	487	0.97096053
	488	0.98225794
	489	1.0103595
	490	1.0007597
	491	1.003498
	492	0.99246608
	493	0.99656347
	494	0.98804749
	495	0.99122491
	496	0.99522926
	497	0.98731605
	498	1.0145434
	499	0.99330816
	500	0.99759216
	501	0.96814048
	502	0.95296183
	503	0.96362471
	504	0.95925977
	505	0.97682205
	506	0.96993138
	507	0.9743074
	508	0.96821818
	509	0.97413308
	510	0.9741753
	511	0.98237142
	512	1.0054193
	513	0.98044807
	514	0.9716773
	515	0.9730455
	516	0.98405828
	517	0.99220103
	518	0.98444001
	519	0.97919493
	520	0.97205233
	521	0.96728223
	522	0.98529893
	523	0.98452324
	524	0.98299888
	525	0.99145042
	526	1.000933
	527	0.99636447
	528	0.98660883
	529	0.98273271
	530	0.98305518
	531	0.98725774
	532	0.99577549
	533	1.002037
	534	1.0060879
	535	1.016075
	536	1.0184118
	537	1.0205711
	538	1.0096961
	539	1.0281337
	540	1.0122963
	541	1.0083497
	542	0.99411874
	543	0.976799
	544	0.97146842
	545	0.97464304
	546	0.95587292
	547	0.94779791
	548	0.93266339
	549	0.92720128
	550	0.94105864
	551	0.93277798
	552	0.93393927
	553	0.91216657
	554	0.92045028
	555	0.9099
	556	0.90792098
	557	0.90669634
	558	0.91268867
	559	0.91696661
	560	0.91164685
	561	0.91311495
	562	0.92197825
	563	0.92461222
	564	0.94930422
	565	0.9488119
	566	0.95232353
	567	0.97275278
	568	0.96734995
	569	0.95356817
	570	0.96075548
	571	0.96936594
	572	0.97489002
	573	0.97933106
	574	0.96499412
	575	0.96157973
	576	0.97156334
	577	0.95983765
	578	0.93655215
	579	0.95207909
	580	0.96912862
	581	0.97938462
	582	0.95701655
	583	0.94891457
	584	0.95606317
	585	0.95351125
	586	0.95641767
	587	0.94315807
	588	0.94639265
	589	0.96503697
	590	0.95601693
	591	0.93087851
	592	0.92980141
	593	0.92266844
	594	0.92925206
	595	0.93743628
	596	0.92900826
	597	0.9049711
	598	0.90213859
	599	0.91342916
	600	0.91384707
	601	0.91456681
	602	0.91316822
	603	0.92671976
	604	0.92058549
	605	0.92936541
	606	0.93228212
	607	0.91010921
	608	0.89349322
	609	0.90336005
	610	0.90997873
	611	0.91856328
	612	0.91668007
	613	0.92838606
	614	0.932016
	615	0.94545438
	616	0.94070026
	617	0.93172987
	618
	619	];
	620
	621	P_obs =[
	622	1
	623	0.99948573
	624	1.0068249
	625	1.0141211
	626	1.0073149
	627	0.99884398
	628	1.0237035
	629	1.0349636
	630	1.036819
	631	1.0247366
	632	1.0242391
	633	1.0275737
	634	1.0065684
	635	0.99838346
	636	0.97281734
	637	0.95346302
	638	0.9355791
	639	0.9461152
	640	0.94338882
	641	0.92988921
	642	0.9311862
	643	0.93529467
	644	0.93784681
	645	0.91501401
	646	0.92360522
	647	0.91049302
	648	0.90754698
	649	0.91365103
	650	0.91499228
	651	0.92260749
	652	0.92533824
	653	0.90949431
	654	0.91106924
	655	0.90594116
	656	0.90491334
	657	0.9039891
	658	0.91060772
	659	0.92132842
	660	0.91934854
	661	0.92268418
	662	0.92545127
	663	0.91517169
	664	0.90513459
	665	0.90224212
	666	0.87734878
	667	0.88013667
	668	0.86906494
	669	0.84776403
	670	0.83895869
	671	0.81373437
	672	0.78998314
	673	0.77594176
	674	0.77982695
	675	0.77098321
	676	0.76538611
	677	0.76608075
	678	0.77458654
	679	0.78354767
	680	0.81282389
	681	0.83627649
	682	0.82873051
	683	0.83181309
	684	0.83149903
	685	0.83551261
	686	0.83305985
	687	0.84648418
	688	0.86195421
	689	0.88047436
	690	0.90177533
	691	0.93232215
	692	0.94445051
	693	0.9472487
	694	0.94786015
	695	0.95992178
	696	0.95499149
	697	0.95788581
	698	0.9684288
	699	0.97731917
	700	0.98739379
	701	1.0033879
	702	1.0159673
	703	1.0269931
	704	1.0436661
	705	1.0492034
	706	1.0765292
	707	1.0784865
	708	1.0971624
	709	1.1171737
	710	1.1193675
	711	1.1526119
	712	1.1550265
	713	1.1585277
	714	1.1560166
	715	1.1671172
	716	1.1706294
	717	1.1805791
	718	1.1786896
	719	1.1756876
	720	1.1820789
	721	1.171211
	722	1.1637997
	723	1.1636684
	724	1.179719
	725	1.1912538
	726	1.2187959
	727	1.2326986
	728	1.2418602
	729	1.2388704
	730	1.2449963
	731	1.2538678
	732	1.2508929
	733	1.2474781
	734	1.255148
	735	1.274482
	736	1.2862757
	737	1.2813665
	738	1.2888943
	739	1.2787436
	740	1.2678886
	741	1.274325
	742	1.2720952
	743	1.263492
	744	1.2652139
	745	1.2667561
	746	1.264558
	747	1.2680362
	748	1.2660431
	749	1.2909605
	750	1.2927921
	751	1.288932
	752	1.2910852
	753	1.3012725
	754	1.3048721
	755	1.3196515
	756	1.3181903
	757	1.321309
	758	1.3431543
	759	1.344136
	760	1.3730377
	761	1.3773695
	762	1.3754742
	763	1.3825964
	764	1.3985574
	765	1.3861412
	766	1.3767823
	767	1.3764309
	768	1.3678747
	769	1.3718554
	770	1.3768022
	771	1.3617199
	772	1.3798267
	773	1.3863533
	774	1.3905803
	775	1.4061004
	776	1.4202788
	777	1.4313191
	778	1.4406155
	779	1.4444837
	780	1.4367244
	781	1.4379653
	782	1.4371881
	783	1.4243012
	784	1.41826
	785	1.4133617
	786	1.40181
	787	1.3965683
	788	1.3865729
	789	1.3855433
	790	1.3755111
	791	1.3758609
	792	1.3962625
	793	1.3994012
	794	1.4083656
	795	1.4233002
	796	1.4037932
	797	1.3973604
	798	1.4095657
	799	1.4095764
	800	1.4080055
	801	1.4095882
	802	1.4108374
	803	1.4164143
	804	1.4283402
	805	1.4343939
	806	1.4392909
	807	1.4406097
	808	1.4468355
	809	1.4412132
	810	1.4305562
	811	1.4252445
	812	1.4103094
	813	1.4059847
	814	1.4141106
	815	1.4429769
	816	1.4489679
	817	1.4559263
	818	1.4593079
	819	1.4627911
	820	1.453154
	821	1.4416665
	822	1.4101485
	823	1.4175823
	824	1.4266407
	825
	826	];
	827

+0

-44

~~tests/expectation.mod~~ less more

0		// Example 1 from Collard's guide to Dynare
1		var y, c, k, a, h, b;
2		varexo e, u;
3
4		parameters beta, rho, alpha, delta, theta, psi, tau;
5
6		alpha = 0.36;
7		rho = 0.95;
8		tau = 0.025;
9		beta = 0.99;
10		delta = 0.025;
11		psi = 0;
12		theta = 2.95;
13
14		phi = 0.1;
15
16		model;
17		cthetah^(1+psi)=expectation(1)((1-alpha)y)+expectation(-2)((1-alpha)y);
18		k = beta(((exp(b)c)/(exp(b(+1))*c(+1)))
19		(exp(b(+1))alphay(+1)+(1-delta)k));
20		y = exp(a)(k(-1)^alpha)(h^(1-alpha));
21		k = exp(b)(y-c)+(1-delta)k(-1);
22		a = rhoa(-1)+taub(-1) + e;
23		b = taua(-1)+rhob(-1) + u;
24		end;
25
26		initval;
27		y = 1.08068253095672;
28		c = 0.80359242014163;
29		h = 0.29175631001732;
30		k = 11.08360443260358;
31		a = 0;
32		b = 0;
33		e = 0;
34		u = 0;
35		end;
36
37		shocks;
38		var e; stderr 0.009;
39		var u; stderr 0.009;
40		var e, u = phi0.0090.009;
41		end;
42
43		stoch_simul;

+44

-0

tests/expectations/expectation.mod less more

	0	// Example 1 from Collard's guide to Dynare
	1	var y, c, k, a, h, b;
	2	varexo e, u;
	3
	4	parameters beta, rho, alpha, delta, theta, psi, tau;
	5
	6	alpha = 0.36;
	7	rho = 0.95;
	8	tau = 0.025;
	9	beta = 0.99;
	10	delta = 0.025;
	11	psi = 0;
	12	theta = 2.95;
	13
	14	phi = 0.1;
	15
	16	model;
	17	cthetah^(1+psi)=expectation(1)((1-alpha)y)+expectation(-2)((1-alpha)y);
	18	k = beta(((exp(b)c)/(exp(b(+1))*c(+1)))
	19	(exp(b(+1))alphay(+1)+(1-delta)k));
	20	y = exp(a)(k(-1)^alpha)(h^(1-alpha));
	21	k = exp(b)(y-c)+(1-delta)k(-1);
	22	a = rhoa(-1)+taub(-1) + e;
	23	b = taua(-1)+rhob(-1) + u;
	24	end;
	25
	26	initval;
	27	y = 1.08068253095672;
	28	c = 0.80359242014163;
	29	h = 0.29175631001732;
	30	k = 11.08360443260358;
	31	a = 0;
	32	b = 0;
	33	e = 0;
	34	u = 0;
	35	end;
	36
	37	shocks;
	38	var e; stderr 0.009;
	39	var u; stderr 0.009;
	40	var e, u = phi0.0090.009;
	41	end;
	42
	43	stoch_simul(order=1,irf=0);

+44

-0

tests/expectations/expectation_nested.mod less more

	0	// Example 1 from Collard's guide to Dynare
	1	var y, c, k, a, h, b;
	2	varexo e, u;
	3
	4	parameters beta, rho, alpha, delta, theta, psi, tau;
	5
	6	alpha = 0.36;
	7	rho = 0.95;
	8	tau = 0.025;
	9	beta = 0.99;
	10	delta = 0.025;
	11	psi = 0;
	12	theta = 2.95;
	13
	14	phi = 0.1;
	15
	16	model;
	17	cthetah^(1+psi)=expectation(1)(((1-alpha)y)+expectation(3)((1-alpha)y));
	18	k = beta(((exp(b)c)/(exp(b(+1))*c(+1)))
	19	(exp(b(+1))alphay(+1)+(1-delta)k));
	20	y = exp(a)(k(-1)^alpha)(h^(1-alpha));
	21	k = exp(b)(y-c)+(1-delta)k(-1);
	22	a = rhoa(-1)+taub(-1) + e;
	23	b = taua(-1)+rhob(-1) + u;
	24	end;
	25
	26	initval;
	27	y = 1.08068253095672;
	28	c = 0.80359242014163;
	29	h = 0.29175631001732;
	30	k = 11.08360443260358;
	31	a = 0;
	32	b = 0;
	33	e = 0;
	34	u = 0;
	35	end;
	36
	37	shocks;
	38	var e; stderr 0.009;
	39	var u; stderr 0.009;
	40	var e, u = phi0.0090.009;
	41	end;
	42
	43	stoch_simul(order=1,irf=0);

+46

-0

tests/expectations/expectation_ss.mod less more

	0	// Example 1 from Collard's guide to Dynare
	1	var y, c, k, a, h, b;
	2	varexo e, u;
	3
	4	parameters beta, rho, alpha, delta, theta, psi, tau;
	5
	6	alpha = 0.36;
	7	rho = 0.95;
	8	tau = 0.025;
	9	beta = 0.99;
	10
	11	delta = 0.025;
	12	psi = 0;
	13	theta = 2.95;
	14
	15	phi = 0.1;
	16
	17	model;
	18	cthetah^(1+psi)=(expectation(1)((1-alpha)y)+expectation(-2)((1-alpha)y))/2;
	19	k = beta(((exp(b)c)/(exp(b(+1))*c(+1)))
	20	(exp(b(+1))alphay(+1)+(1-delta)k));
	21	y = exp(a)(k(-1)^alpha)(h^(1-alpha));
	22	k = exp(b)(y-c)+(1-delta)k(-1);
	23	a = rhoa(-1)+taub(-1) + e;
	24	b = taua(-1)+rhob(-1) + u;
	25	end;
	26
	27	steady_state_model;
	28	a = 0;
	29	b = 0;
	30	h = 1/3;
	31	k = ((1/beta-(1-delta))/(alpha*h^(1-alpha)))^(1/(alpha-1));
	32	y = k^alpha*h^(1-alpha);
	33	c = y - delta*k;
	34	theta = (1-alpha)y/(ch^(1+psi));
	35	end;
	36
	37	steady;
	38
	39	shocks;
	40	var e; stderr 0.009;
	41	var u; stderr 0.009;
	42	var e, u = phi0.0090.009;
	43	end;
	44
	45	stoch_simul(order=1,irf=0);

+36

-0

tests/expectations/expectation_ss_old.mod less more

	0	// Example 1 from Collard's guide to Dynare
	1	var y, c, k, a, h, b;
	2	varexo e, u;
	3
	4	parameters beta, rho, alpha, delta, theta, psi, tau;
	5
	6	alpha = 0.36;
	7	rho = 0.95;
	8	tau = 0.025;
	9	beta = 0.99;
	10
	11	delta = 0.025;
	12	psi = 0;
	13	theta = 2.95;
	14
	15	phi = 0.1;
	16
	17	model;
	18	cthetah^(1+psi)=(expectation(1)((1-alpha)y)+expectation(-2)((1-alpha)y))/2;
	19	k = beta(((exp(b)c)/(exp(b(+1))*c(+1)))
	20	(exp(b(+1))alphay(+1)+(1-delta)k));
	21	y = exp(a)(k(-1)^alpha)(h^(1-alpha));
	22	k = exp(b)(y-c)+(1-delta)k(-1);
	23	a = rhoa(-1)+taub(-1) + e;
	24	b = taua(-1)+rhob(-1) + u;
	25	end;
	26
	27	steady;
	28
	29	shocks;
	30	var e; stderr 0.009;
	31	var u; stderr 0.009;
	32	var e, u = phi0.0090.009;
	33	end;
	34
	35	stoch_simul(order=1,irf=0);

+12

-0

tests/expectations/expectation_ss_old_steadystate.m less more

	0	function [ys_, check_] = expectation_ss_old_steadystate(ys_orig_, exo_)
	1	ys_=zeros(6,1);
	2	global M_
	3	ys_(4)=0;
	4	ys_(6)=0;
	5	ys_(5)=0.3333333333333333;
	6	ys_(3)=((1/M_.params(1)-(1-M_.params(4)))/(M_.params(3)*ys_(5)^(1-M_.params(3))))^(1/(M_.params(3)-1));
	7	ys_(1)=ys_(5)^(1-M_.params(3))*ys_(3)^M_.params(3);
	8	ys_(2)=ys_(1)-M_.params(4)*ys_(3);
	9	M_.params(5)=(1-M_.params(3))ys_(1)/(ys_(2)ys_(5)^(1+M_.params(6)));
	10	check_=0;
	11	end

+75

-0

tests/fs2000/fs2000d.mod less more

	0	// See fs2000.mod in the examples/ directory for details on the model
	1
	2	var m P c e W R k d n l gy_obs gp_obs y dA;
	3	varexo e_a e_m;
	4
	5	parameters alp bet gam mst rho psi del;
	6
	7	alp = 0.33;
	8	bet = 0.99;
	9	gam = 0.003;
	10	mst = 1.011;
	11	rho = 0.7;
	12	psi = 0.787;
	13	del = 0.02;
	14
	15	model;
	16	dA = exp(gam+e_a);
	17	log(m) = (1-rho)log(mst) + rholog(m(-1))+e_m;
	18	-P/(c(+1)P(+1)m)+betP(+1)(alpexp(-alp(gam+log(e(+1))))k^(alp-1)n(+1)^(1-alp)+(1-del)exp(-(gam+log(e(+1)))))/(c(+2)P(+2)*m(+1))=0;
	19	W = l/n;
	20	-(psi/(1-psi))(cP/(1-n))+l/n = 0;
	21	R = P(1-alp)exp(-alp(gam+e_a))k(-1)^alp*n^(-alp)/W;
	22	1/(cP)-betP(1-alp)exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)/(mlc(+1)P(+1)) = 0;
	23	c+k = exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)+(1-del)exp(-(gam+e_a))*k(-1);
	24	P*c = m;
	25	m-1+d = l;
	26	e = exp(e_a);
	27	y = k(-1)^alpn^(1-alp)exp(-alp*(gam+e_a));
	28	gy_obs = dA*y/y(-1);
	29	gp_obs = (P/P(-1))*m(-1)/dA;
	30	end;
	31
	32	initval;
	33	k = 6;
	34	m = mst;
	35	P = 2.25;
	36	c = 0.45;
	37	e = 1;
	38	W = 4;
	39	R = 1.02;
	40	d = 0.85;
	41	n = 0.19;
	42	l = 0.86;
	43	y = 0.6;
	44	gy_obs = exp(gam);
	45	gp_obs = exp(-gam);
	46	dA = exp(gam);
	47	end;
	48
	49	shocks;
	50	var e_a; stderr 0.014;
	51	var e_m; stderr 0.005;
	52	end;
	53
	54	steady;
	55
	56	check;
	57
	58	estimated_params;
	59	alp, beta_pdf, 0.356, 0.02;
	60	bet, beta_pdf, 0.993, 0.002;
	61	gam, normal_pdf, 0.0085, 0.003;
	62	mst, normal_pdf, 1.0002, 0.007;
	63	rho, beta_pdf, 0.129, 0.223;
	64	psi, beta_pdf, 0.65, 0.05;
	65	del, beta_pdf, 0.01, 0.005;
	66	stderr e_a, inv_gamma_pdf, 0.035449, inf;
	67	stderr e_m, inv_gamma_pdf, 0.008862, inf;
	68	end;
	69
	70	varobs gp_obs gy_obs;
	71
	72	options_.solve_tolf = 1e-12;
	73
	74	estimation(order=1,datafile=fsdat_simul,nobs=192,mode_compute=5,loglinear,mh_replic=0);

+63

-0

tests/identification/as2007/as2007.mod less more

	0	var pie y R g z YGR INFL INT;
	1	varexo e_R e_g e_z;
	2	parameters tau kap psi1 psi2 rhoR rhog rhoz rr_steady pi_steady gam_steady std_R std_g std_z;
	3
	4	tau = 2;
	5	kap = 0.15;
	6
	7	psi1 = 1.5;
	8	psi2 = 1;
	9
	10	rhoR = 0.96;
	11	rhog = 0.95;
	12	rhoz = 0.65;
	13
	14	rr_steady = 0.4;
	15	pi_steady = 4.0;
	16	gam_steady = 0.5;
	17	std_R = 0.2;
	18	std_g = 0.8;
	19	std_z = 0.45;
	20
	21	model;
	22	y = y(+1) + g - g(+1) - 1/tau*(R-pie(+1)-z(+1));
	23	pie = 1/(1+rr_steady/400)pie(+1)+kap(y-g);
	24	R = rhoRR(-1)+(1-rhoR)psi1pie+(1-rhoR)psi2(y-y(-1)+z)+e_Rstd_R;
	25	g = rhogg(-1)+e_gstd_g;
	26	z = rhozz(-1)+e_zstd_z;
	27	YGR = gam_steady+100*(y-y(-1)+z);
	28	INFL = pi_steady+400*pie;
	29	INT = pi_steady+rr_steady+4gam_steady+400R;
	30	end;
	31
	32	estimated_params;
	33	tau, gamma_pdf, 2, 0.5;
	34	kap, beta_pdf, 0.2, 0.1;
	35	psi1, gamma_pdf, 1.5, 0.25;
	36	psi2, gamma_pdf, 0.5, 0.25;
	37	rhoR, beta_pdf, 0.5, 0.2;
	38	rhog, beta_pdf, 0.8, 0.1;
	39	rhoz, beta_pdf, 0.66, 0.15;
	40	rr_steady, gamma_pdf, 0.8, 0.5;
	41	pi_steady, gamma_pdf, 0.8, 0.5;
	42	gam_steady, gamma_pdf, 0.8, 0.5;
	43	std_R, inv_gamma_pdf, 0.05, inf;
	44	std_g, inv_gamma_pdf, 0.05, inf;
	45	std_z, inv_gamma_pdf, 0.05, inf;
	46	end;
	47
	48	steady;
	49	check;
	50
	51	varobs YGR INFL INT;
	52
	53	shocks;
	54	var e_g;
	55	stderr 1;
	56	var e_R;
	57	stderr 1;
	58	var e_z;
	59	stderr 1;
	60	end;
	61
	62	identification;

+0

-32

~~tests/identification/as2007/as2007_steadystate.m~~ less more

0		function [ys,check1]=as2007_steadystate(junk, ys)
1
2		global M_ options_
3
4		for j=1:size(M_.param_names,1)
5		eval([deblank(M_.param_names(j,:)),' = M_.params(j);'])
6		assignin('base',deblank(M_.param_names(j,:)),M_.params(j));
7		end
8		for j=1:size(M_.endo_names,1)
9		eval([deblank(M_.endo_names(j,:)),' = NaN;'])
10		end
11
12		check1=0;
13
14		pie=0;
15		y=0;
16		R=0;
17		g=0;
18		z=0;
19		YGR=gam_steady;
20		INFL = pi_steady;
21		INT = pi_steady+rr_steady+4*gam_steady;
22
23		ys=[
24		pie
25		y
26		R
27		g
28		z
29		YGR
30		INFL
31		INT];⏎

+52

-0

tests/identification/kim/kim2.mod less more

	0	var c k i a lam;
	1	varexo ea;
	2
	3	parameters
	4	alph
	5	betae
	6	delta
	7	as
	8	s
	9	theta
	10	phi
	11	;
	12
	13
	14	alph=0.6;
	15	betae=0.99;
	16	delta=0.0125;
	17	as=0.1;
	18	phi=2;
	19	s=betaedeltaalph/(1-betae+delta*betae);
	20	theta=1;
	21
	22	model;
	23	//(1-(betaedeltaalph/(1-betae+deltabetae)))^theta/c^(1+theta)/(1+theta)(1+theta)(i/(betaedeltaalph/(1-betae+deltabetae)))^theta(i/k/delta)^phi=betae(1-(betaedeltaalph/(1-betae+deltabetae)))^theta/c(+1)^(1+theta)/(1+theta)(alph(1+theta)a(+1)^(1+theta)k^(alph(1+theta)-1)+(1-delta)(i(+1)/k/delta)^phi(1+theta)(i(+1)/(betaedeltaalph/(1-betae+deltabetae)))^theta);
	24	lam(1+theta)(i/(betaedeltaalph/(1-betae+deltabetae)))^theta(i/k/delta)^phi=betaelam(+1)(alph(1+theta)a(+1)^(1+theta)k^(alph(1+theta)-1)+(1-delta)(i(+1)/k/delta)^phi(1+theta)(i(+1)/(betaedeltaalph/(1-betae+deltabetae)))^theta);
	25	k=(delta(i/delta)^(1-phi)+(1-delta)k(-1)^(1-phi))^(1/(1-phi));
	26	((1-(betaedeltaalph/(1-betae+deltabetae)))(c/(1-(betaedeltaalph/(1-betae+delta*betae))))^(1+theta) +
	27	(betaedeltaalph/(1-betae+deltabetae))(i/(betaedeltaalph/(1-betae+delta*betae)))^(1+theta))^(1/(1+theta))=
	28	(a*k(-1)^alph);
	29	a = as+ea;
	30	lam = (1-(betaedeltaalph/(1-betae+delta*betae)))^theta/c^(1+theta)/(1+theta);
	31	end;
	32
	33	steady;
	34	check;
	35
	36	shocks;
	37	var ea = 1;
	38	end;
	39
	40	estimated_params;
	41	alph ,uniform_pdf,0.6,0.04,0.5,0.7;
	42	//betae ,uniform_pdf,0.99,0.004,0.98,1;
	43	//delta ,uniform_pdf,0.0125,0.001,0.01,0.015;
	44	phi ,uniform_pdf,0.5,0.2,0,10;
	45	theta ,uniform_pdf,0.3,0.1,0,10;
	46	end;
	47
	48	varobs c i;
	49	//varobs c i lam; //to check if observing lam identifies phi and theta
	50	//identification(ar=1);
	51	identification(prior_mc=100);

+1

-1

tests/k_order_perturbation/fs2000k2a.mod less more

54	54
55	55	steady;
56	56
57		stoch_simul(order=2);
	57	stoch_simul(order=2,nograph);
58	58
59	59

+1

-1

tests/k_order_perturbation/fs2000k_1_use_dll.mod less more

63	63	stoch_simul(order=2,k_order_solver,irf=0);
64	64
65	65	if ~exist('fs2000k2_use_dll_results.mat','file');
66		error('fs2000k2 must be run first');
	66	error('fs2000k2_use_dll must be run first');
67	67	end;
68	68
69	69	oo1 = load('fs2000k2_use_dll_results','oo_');

+1

-0

tests/k_order_perturbation/run_fs2000kplusplus.m less more

0

system('../../dynare++/src/dynare++ --order 3 --no-centralize fs2000k++.mod')

+56

-0

tests/kalman_filter_smoother/algo1.mod less more

	0	var z dw dx dy dc1 dc2;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	end;
	13
	14	estimated_params;
	15	rho_w, normal_pdf, 0.5,0.2;
	16	rho_x, normal_pdf, 0.5,0.2;
	17	rho_y, normal_pdf, 0.5,0.2;
	18	rho_z, normal_pdf, 0.8,0.2;
	19
	20	a1, normal_pdf, 0.1,0.2;
	21	a2, normal_pdf, -0.1,0.2;
	22	a3, normal_pdf, 0.1,0.2;
	23	b , normal_pdf, 1,0.2;
	24	c , normal_pdf, 1,0.2;
	25
	26	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	27	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	28	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	29	stderr e_z, uniform_pdf,,, 0.01, 0.1;
	30	end;
	31
	32	varobs dw dx dy z;
	33
	34	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,filtered_vars);
	35
	36	//checking smoother consistency
	37	X = oo_.SmoothedVariables;
	38	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2];
	39	X = oo_.SmoothedShocks;
	40	E = [X.e_w X.e_x X.e_y X.e_z];
	41	A = oo_.dr.ghx;
	42	B = oo_.dr.ghu;
	43	err = zeros(6,200);
	44	for t=2:200;
	45	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	46	end;
	47	if max(max(abs(err))) > 1e-10;
	48	error('Test fails')
	49	end;
	50
	51	d=load('data');
	52	dat = [d.dw d.dx d.dy d.z];
	53	if max(max(abs(dat(1000:1199,:)-S(:,[2:4 1])))) > 1e-10;
	54	error('Test fails');
	55	end;⏎

+87

-0

tests/kalman_filter_smoother/algo2.mod less more

	0	var z dw dx dy dc1 dc2;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	end;
	13
	14	estimated_params;
	15	rho_w, normal_pdf, 0.5,0.2;
	16	rho_x, normal_pdf, 0.5,0.2;
	17	rho_y, normal_pdf, 0.5,0.2;
	18	rho_z, normal_pdf, 0.8,0.2;
	19
	20	a1, normal_pdf, 0.1,0.2;
	21	a2, normal_pdf, -0.1,0.2;
	22	a3, normal_pdf, 0.1,0.2;
	23	b , normal_pdf, 1,0.2;
	24	c , normal_pdf, 1,0.2;
	25
	26	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	27	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	28	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	29	stderr e_z, uniform_pdf,,, 0.01, 0.1;
	30	end;
	31
	32	varobs dw dx dy z;
	33
	34	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,mode_compute=0,mode_file=algo1_mode,kalman_algo=2,filtered_vars);
	35
	36	//checking smoother consistency
	37	X = oo_.SmoothedVariables;
	38	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2];
	39	X = oo_.SmoothedShocks;
	40	E = [X.e_w X.e_x X.e_y X.e_z];
	41	A = oo_.dr.ghx;
	42	B = oo_.dr.ghu;
	43	err = zeros(6,200);
	44	for t=2:200;
	45	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	46	end;
	47	if max(max(abs(err))) > 1e-10;
	48	error('Test fails');
	49	end;
	50
	51	d=load('data');
	52	dat = [d.dw d.dx d.dy d.z];
	53	if max(max(abs(dat(1000:1199,:)-S(:,[2:4 1])))) > 1e-10;
	54	error('Test fails');
	55	end;
	56
	57	o1 = load('algo1_results');
	58	obj_endo={'SmoothedVariables'; 'FilteredVariables'; 'UpdatedVariables'};
	59	obj_exo = {'SmoothedShocks';};
	60	nobj_endo = size(obj_endo,1);
	61	nobj_exo = size(obj_exo,1);
	62	for i=1:nobj_endo;
	63	err_endo = zeros(eval(['size(oo_.' obj_endo{i} '.' M_.endo_names(1,:) ',1);']),M_.endo_nbr);
	64	for j=1:M_.endo_nbr;
	65	var1 = eval(['o1.oo_.' obj_endo{i} '.' M_.endo_names(j,:)]);
	66	var2 = eval(['oo_.' obj_endo{i} '.' M_.endo_names(j,:)]);
	67	err_endo(:,j) = var1-var2;
	68	end;
	69	if max(max(abs(err_endo))) > 1e-10;
	70	error('Test fails');
	71	end;
	72	end;
	73
	74
	75	err_exo = zeros(200,M_.exo_nbr,nobj_exo);
	76	for i=1:nobj_exo;
	77	err_exo = zeros(size(eval(['oo_.' obj_exo{i} '.' M_.exo_names(1,:)]),1),M_.exo_nbr);
	78	for j=1:M_.exo_nbr;
	79	var1 = eval(['o1.oo_.' obj_exo{i} '.' M_.exo_names(j,:)]);
	80	var2 = eval(['oo_.' obj_exo{i} '.' M_.exo_names(j,:)]);
	81	err_exo(:,j,i) = var1 - var2;
	82	end;
	83	if max(max(abs(err_exo))) > 1e-10;
	84	error('Test fails')
	85	end;
	86	end;

+59

-0

tests/kalman_filter_smoother/algo3.mod less more

	0	var z dw dx dy dc1 dc2 w x y;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	w = w(-1) + dw;
	13	x = x(-1) + dx;
	14	y = y(-1) + dy;
	15	end;
	16
	17	estimated_params;
	18	rho_w, normal_pdf, 0.5,0.2;
	19	rho_x, normal_pdf, 0.5,0.2;
	20	rho_y, normal_pdf, 0.5,0.2;
	21	rho_z, normal_pdf, 0.8,0.2;
	22
	23	a1, normal_pdf, 0.1,0.2;
	24	a2, normal_pdf, -0.1,0.2;
	25	a3, normal_pdf, 0.1,0.2;
	26	b , normal_pdf, 1,0.2;
	27	c , normal_pdf, 1,0.2;
	28
	29	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	30	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	31	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	32	stderr e_z, inv_gamma_pdf,0.01, inf;
	33	end;
	34
	35	varobs w x y;
	36
	37	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter,filtered_vars);
	38
	39	//checking smoother consistency
	40	X = oo_.SmoothedVariables;
	41	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2 X.w X.x X.y];
	42	X = oo_.SmoothedShocks;
	43	E = [X.e_w X.e_x X.e_y X.e_z];
	44	A = oo_.dr.ghx;
	45	B = oo_.dr.ghu;
	46	err = zeros(M_.endo_nbr,200);
	47	for t=2:200;
	48	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	49	end;
	50	if max(max(abs(err))) > 1e-10;
	51	error('Test fails');
	52	end;
	53
	54	d=load('data');
	55	dat = [d.w d.x d.y];
	56	if max(max(abs(dat(1000:1199,:)-S(:,[7:9])))) > 1e-10;
	57	error('Test fails');
	58	end;

+91

-0

tests/kalman_filter_smoother/algo4.mod less more

	0	var z dw dx dy dc1 dc2 w x y;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	w = w(-1) + dw;
	13	x = x(-1) + dx;
	14	y = y(-1) + dy;
	15	end;
	16
	17	estimated_params;
	18	rho_w, normal_pdf, 0.5,0.2;
	19	rho_x, normal_pdf, 0.5,0.2;
	20	rho_y, normal_pdf, 0.5,0.2;
	21	rho_z, normal_pdf, 0.8,0.2;
	22
	23	a1, normal_pdf, 0.1,0.2;
	24	a2, normal_pdf, -0.1,0.2;
	25	a3, normal_pdf, 0.1,0.2;
	26	b , normal_pdf, 1,0.2;
	27	c , normal_pdf, 1,0.2;
	28
	29	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	30	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	31	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	32	stderr e_z, inv_gamma_pdf,0.01, inf;
	33	end;
	34
	35	varobs w x y;
	36
	37	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,mode_compute=0,mode_file=algo3_mode,diffuse_filter,kalman_algo=4,filtered_vars);
	38
	39	//checking smoother consistency
	40	X = oo_.SmoothedVariables;
	41	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2 X.w X.x X.y];
	42	X = oo_.SmoothedShocks;
	43	E = [X.e_w X.e_x X.e_y X.e_z];
	44	A = oo_.dr.ghx;
	45	B = oo_.dr.ghu;
	46	err = zeros(M_.endo_nbr,200);
	47	for t=2:200;
	48	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	49	end;
	50	if max(max(abs(err))) > 1e-10;
	51	error('Test fails');
	52	end;
	53
	54	d=load('data');
	55	dat = [d.w d.x d.y];
	56	if max(max(abs(dat(1000:1199,:)-S(:,[7:9])))) > 1e-10;
	57	error('Test fails');
	58	end;
	59
	60	o1 = load('algo3_results');
	61	obj_endo={'SmoothedVariables'; 'FilteredVariables'; 'UpdatedVariables'};
	62	obj_exo = {'SmoothedShocks';};
	63	nobj_endo = size(obj_endo,1);
	64	nobj_exo = size(obj_exo,1);
	65	for i=1:nobj_endo;
	66	err_endo = zeros(eval(['size(oo_.' obj_endo{i} '.' M_.endo_names(1,:) ',1);']),M_.endo_nbr);
	67	for j=1:M_.endo_nbr;
	68	var1 = eval(['o1.oo_.' obj_endo{i} '.' M_.endo_names(j,:)]);
	69	var2 = eval(['oo_.' obj_endo{i} '.' M_.endo_names(j,:)]);
	70	err_endo(:,j) = var1-var2;
	71	end;
	72	if max(max(abs(err_endo))) > 1e-10;
	73	error('Test fails');
	74	end;
	75	end;
	76
	77
	78	err_exo = zeros(200,M_.exo_nbr,nobj_exo);
	79	for i=1:nobj_exo;
	80	err_exo = zeros(size(eval(['oo_.' obj_exo{i} '.' M_.exo_names(1,:)]),1),M_.exo_nbr);
	81	for j=1:M_.exo_nbr;
	82	var1 = eval(['o1.oo_.' obj_exo{i} '.' M_.exo_names(j,:)]);
	83	var2 = eval(['oo_.' obj_exo{i} '.' M_.exo_names(j,:)]);
	84	err_exo(:,j,i) = var1 - var2;
	85	end;
	86	if max(max(abs(err_exo))) > 1e-10;
	87	error('Test fails');
	88	end;
	89	end;
	90

+58

-0

tests/kalman_filter_smoother/algo4a.mod less more

	0	var z dw dx dy dc1 dc2 y;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	y = y(-1) + dy;
	13	end;
	14
	15	estimated_params;
	16	rho_w, normal_pdf, 0.5,0.2;
	17	rho_x, normal_pdf, 0.5,0.2;
	18	rho_y, normal_pdf, 0.5,0.2;
	19	rho_z, normal_pdf, 0.8,0.2;
	20
	21	a1, normal_pdf, 0.1,0.2;
	22	a2, normal_pdf, -0.1,0.2;
	23	a3, normal_pdf, 0.1,0.2;
	24	b , normal_pdf, 1,0.2;
	25	c , normal_pdf, 1,0.2;
	26
	27	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	28	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	29	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	30	stderr e_z, uniform_pdf,,, 0.01, 0.1;
	31	end;
	32
	33	varobs dw dx y z;
	34
	35	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter);
	36	//estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,mode_compute=0,mode_file=algo3_mode,diffuse_filter);
	37
	38	//checking smoother consistency
	39	X = oo_.SmoothedVariables;
	40	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2 X.y];
	41	X = oo_.SmoothedShocks;
	42	E = [X.e_w X.e_x X.e_y X.e_z];
	43	A = oo_.dr.ghx;
	44	B = oo_.dr.ghu;
	45	err = zeros(M_.endo_nbr,200);
	46	for t=2:200;
	47	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	48	end;
	49	if max(max(abs(err))) > 1e-10;
	50	error('Test fails');
	51	end;
	52
	53	d=load('data');
	54	dat = [d.dw d.dx d.y d.z];
	55	if max(max(abs(dat(1000:1199,:)-S(:,[2 3 7 1])))) > 1e-10;
	56	error('Test fails');
	57	end;

+58

-0

tests/kalman_filter_smoother/algo4b.mod less more

	0	var z dw dx dy dc1 dc2 y;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	y = y(-1) + dy;
	13	end;
	14
	15	estimated_params;
	16	rho_w, normal_pdf, 0.5,0.2;
	17	rho_x, normal_pdf, 0.5,0.2;
	18	rho_y, normal_pdf, 0.5,0.2;
	19	rho_z, normal_pdf, 0.8,0.2;
	20
	21	a1, normal_pdf, 0.1,0.2;
	22	a2, normal_pdf, -0.1,0.2;
	23	a3, normal_pdf, 0.1,0.2;
	24	b , normal_pdf, 1,0.2;
	25	c , normal_pdf, 1,0.2;
	26
	27	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	28	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	29	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	30	stderr e_z, uniform_pdf,,, 0.01, 0.1;
	31	end;
	32
	33	varobs dw dx y z;
	34
	35	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter);
	36	//estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,mode_compute=0,mode_file=algo3_mode,diffuse_filter);
	37
	38	//checking smoother consistency
	39	X = oo_.SmoothedVariables;
	40	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2 X.y];
	41	X = oo_.SmoothedShocks;
	42	E = [X.e_w X.e_x X.e_y X.e_z];
	43	A = oo_.dr.ghx;
	44	B = oo_.dr.ghu;
	45	err = zeros(M_.endo_nbr,200);
	46	for t=2:200;
	47	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	48	end;
	49	if max(max(abs(err))) > 1e-10;
	50	error('Test fails');
	51	end;
	52
	53	d=load('data');
	54	dat = [d.dw d.dx d.y d.z];
	55	if max(max(abs(dat(1000:1199,:)-S(:,[2 3 7 1])))) > 1e-10;
	56	error('Test fails');
	57	end;

+60

-0

tests/kalman_filter_smoother/algoH1.mod less more

	0	var z dw dx dy dc1 dc2;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	end;
	13
	14	estimated_params;
	15	rho_w, normal_pdf, 0.5,0.2;
	16	rho_x, normal_pdf, 0.5,0.2;
	17	rho_y, normal_pdf, 0.5,0.2;
	18	rho_z, normal_pdf, 0.8,0.2;
	19
	20	a1, normal_pdf, 0.1,0.2;
	21	a2, normal_pdf, -0.1,0.2;
	22	a3, normal_pdf, 0.1,0.2;
	23	b , normal_pdf, 1,0.2;
	24	c , normal_pdf, 1,0.2;
	25
	26	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	27	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	28	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	29	stderr e_z, uniform_pdf,,, 0.01, 0.1;
	30
	31	stderr z, normal_pdf, 0, 0.1;
	32	end;
	33
	34	varobs dw dx dy z;
	35
	36	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0);
	37
	38	//checking smoother consistency
	39	X = oo_.SmoothedVariables;
	40	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2];
	41	X = oo_.SmoothedShocks;
	42	E = [X.e_w X.e_x X.e_y X.e_z];
	43	A = oo_.dr.ghx;
	44	B = oo_.dr.ghu;
	45	err = zeros(6,200);
	46	for t=2:200;
	47	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	48	end;
	49	if max(max(abs(err))) > 1e-10;
	50	error('Test fails');
	51	end;
	52
	53	d=load('data');
	54	dat = [d.dw d.dx d.dy d.z];
	55	X = oo_.SmoothedMeasurementErrors;
	56	ME = [X.dw X.dx X.dy X.z];
	57	if max(max(abs(dat(1000:1199,:)-S(:,[2:4 1])-ME))) > 1e-10;
	58	error('Test fails');
	59	end;

+95

-0

tests/kalman_filter_smoother/algoH2.mod less more

	0	var z dw dx dy dc1 dc2;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	end;
	13
	14	estimated_params;
	15	rho_w, normal_pdf, 0.5,0.2;
	16	rho_x, normal_pdf, 0.5,0.2;
	17	rho_y, normal_pdf, 0.5,0.2;
	18	rho_z, normal_pdf, 0.8,0.2;
	19
	20	a1, normal_pdf, 0.1,0.2;
	21	a2, normal_pdf, -0.1,0.2;
	22	a3, normal_pdf, 0.1,0.2;
	23	b , normal_pdf, 1,0.2;
	24	c , normal_pdf, 1,0.2;
	25
	26	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	27	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	28	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	29	stderr e_z, uniform_pdf,,, 0.01, 0.1;
	30
	31	stderr z, normal_pdf, 0, 0.1;
	32	end;
	33
	34	varobs dw dx dy z;
	35
	36	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,mode_compute=0,mode_file=algoH1_mode,kalman_algo=2,filtered_vars);
	37
	38	//checking smoother consistency
	39	X = oo_.SmoothedVariables;
	40	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2];
	41	X = oo_.SmoothedShocks;
	42	E = [X.e_w X.e_x X.e_y X.e_z];
	43	A = oo_.dr.ghx;
	44	B = oo_.dr.ghu;
	45	err = zeros(6,200);
	46	for t=2:200;
	47	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	48	end;
	49	if max(max(abs(err))) > 1e-10;
	50	error('Test fails');
	51	end;
	52
	53	d=load('data');
	54	dat = [d.dw d.dx d.dy d.z];
	55	X = oo_.SmoothedMeasurementErrors;
	56	ME = [X.dw X.dx X.dy X.z];
	57	if max(max(abs(dat(1000:1199,:)-S(:,[2:4 1])-ME))) > 1e-10;
	58	error('Test fails');
	59	end;
	60
	61	o1 = load('algoH1_results');
	62	obj_endo={'SmoothedVariables'; 'FilteredVariables'; 'UpdatedVariables'};
	63	obj_exo = {'SmoothedShocks';};
	64	nobj_endo = size(obj_endo,1);
	65	nobj_exo = size(obj_exo,1);
	66	for i=1:nobj_endo;
	67	err_endo = zeros(eval(['size(oo_.' obj_endo{i} '.' M_.endo_names(1,:) ',1);']),M_.endo_nbr);
	68	for j=1:M_.endo_nbr;
	69	var1 = eval(['o1.oo_.' obj_endo{i} '.' M_.endo_names(j,:)]);
	70	var2 = eval(['oo_.' obj_endo{i} '.' M_.endo_names(j,:)]);
	71	err_endo(:,j) = var1-var2;
	72	end;
	73	if max(max(abs(err_endo))) > 1e-10;
	74	error('Test fails');
	75	end;
	76	end;
	77
	78
	79	err_exo = zeros(200,M_.exo_nbr,nobj_exo);
	80	for i=1:nobj_exo;
	81	err_exo = zeros(size(eval(['oo_.' obj_exo{i} '.' M_.exo_names(1,:)]),1),M_.exo_nbr);
	82	for j=1:M_.exo_nbr;
	83	var1 = eval(['o1.oo_.' obj_exo{i} '.' M_.exo_names(j,:)]);
	84	var2 = eval(['oo_.' obj_exo{i} '.' M_.exo_names(j,:)]);
	85	err_exo(:,j,i) = var1 - var2;
	86	end;
	87	if max(max(abs(err_exo))) > 1e-10;
	88	error('Test fails');
	89	end;
	90	end;
	91
	92	if max(max(abs(o1.oo_.SmoothedMeasurementErrors.z - oo_.SmoothedMeasurementErrors.z))) > 1e-10;
	93	error('Test fails');
	94	end;

+65

-0

tests/kalman_filter_smoother/algoH3.mod less more

	0	var z dw dx dy dc1 dc2 w x y;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	model(linear);
	6	dw = rho_wdw(-1)+a1(dc1(-1))+e_w;
	7	dx = rho_xdx(-1)+a2(dc1(-1))+e_x;
	8	dy = rho_ydy(-1)+a3(dc2(-1))+e_y;
	9	z = rho_z*z(-1)+dw-dx+e_z;
	10	dc1 = dc1(-1)+dx-bdy-cdw;
	11	dc2 = dc2(-1)+dx-b*dy;
	12	w = w(-1) + dw;
	13	x = x(-1) + dx;
	14	y = y(-1) + dy;
	15	end;
	16
	17	estimated_params;
	18	rho_w, normal_pdf, 0.5,0.2;
	19	rho_x, normal_pdf, 0.5,0.2;
	20	rho_y, normal_pdf, 0.5,0.2;
	21	rho_z, normal_pdf, 0.8,0.2;
	22
	23	a1, normal_pdf, 0.1,0.2;
	24	a2, normal_pdf, -0.1,0.2;
	25	a3, normal_pdf, 0.1,0.2;
	26	b , normal_pdf, 1,0.2;
	27	c , normal_pdf, 1,0.2;
	28
	29	stderr e_w, uniform_pdf,,, 0.01, 0.1;
	30	stderr e_x, uniform_pdf,,, 0.01, 0.1;
	31	stderr e_y, uniform_pdf,,, 0.01, 0.1;
	32	stderr e_z, inv_gamma_pdf,0.01, inf;
	33
	34	stderr w, inv_gamma_pdf, 0.01,inf;
	35	end;
	36
	37	varobs w x y;
	38
	39	estimation(datafile=data,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter);
	40
	41	stoch_simul(irf=0);
	42
	43	//checking smoother consistency
	44	X = oo_.SmoothedVariables;
	45	S = [X.z X.dw X.dx X.dy X.dc1 X.dc2 X.w X.x X.y];
	46	X = oo_.SmoothedShocks;
	47	E = [X.e_w X.e_x X.e_y X.e_z];
	48	A = oo_.dr.ghx;
	49	B = oo_.dr.ghu;
	50	err = zeros(M_.endo_nbr,200);
	51	for t=2:200;
	52	err(:,t) = S(t,:)'-AS(t-1,:)'-BE(t,:)';
	53	end;
	54	if max(max(abs(err))) > 1e-10;
	55	error('Test fails');
	56	end;
	57
	58	d=load('data');
	59	dat = [d.w d.x d.y];
	60	X = oo_.SmoothedMeasurementErrors;
	61	ME = [X.w X.x X.y];
	62	if max(max(abs(dat(1000:1199,:)-S(:,[7:9])-ME))) > 1E-10;
	63	error('Test fails');
	64	end;

+74

-0

tests/kalman_filter_smoother/fs2000.mod less more

	0	// See fs2000.mod in the examples/ directory for details on the model
	1
	2	var m P c e W R k d n l gy_obs gp_obs y dA;
	3	varexo e_a e_m;
	4
	5	parameters alp bet gam mst rho psi del;
	6
	7	alp = 0.33;
	8	bet = 0.99;
	9	gam = 0.003;
	10	mst = 1.011;
	11	rho = 0.7;
	12	psi = 0.787;
	13	del = 0.02;
	14
	15	model;
	16	dA = exp(gam+e_a);
	17	log(m) = (1-rho)log(mst) + rholog(m(-1))+e_m;
	18	-P/(c(+1)P(+1)m)+betP(+1)(alpexp(-alp(gam+log(e(+1))))k^(alp-1)n(+1)^(1-alp)+(1-del)exp(-(gam+log(e(+1)))))/(c(+2)P(+2)*m(+1))=0;
	19	W = l/n;
	20	-(psi/(1-psi))(cP/(1-n))+l/n = 0;
	21	R = P(1-alp)exp(-alp(gam+e_a))k(-1)^alp*n^(-alp)/W;
	22	1/(cP)-betP(1-alp)exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)/(mlc(+1)P(+1)) = 0;
	23	c+k = exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)+(1-del)exp(-(gam+e_a))*k(-1);
	24	P*c = m;
	25	m-1+d = l;
	26	e = exp(e_a);
	27	y = k(-1)^alpn^(1-alp)exp(-alp*(gam+e_a));
	28	gy_obs = dA*y/y(-1);
	29	gp_obs = (P/P(-1))*m(-1)/dA;
	30	end;
	31
	32	initval;
	33	k = 6;
	34	m = mst;
	35	P = 2.25;
	36	c = 0.45;
	37	e = 1;
	38	W = 4;
	39	R = 1.02;
	40	d = 0.85;
	41	n = 0.19;
	42	l = 0.86;
	43	y = 0.6;
	44	gy_obs = exp(gam);
	45	gp_obs = exp(-gam);
	46	dA = exp(gam);
	47	end;
	48
	49	shocks;
	50	var e_a; stderr 0.014;
	51	var e_m; stderr 0.005;
	52	end;
	53
	54	steady;
	55
	56	check;
	57
	58	estimated_params;
	59	alp, beta_pdf, 0.356, 0.02;
	60	bet, beta_pdf, 0.993, 0.002;
	61	gam, normal_pdf, 0.0085, 0.003;
	62	mst, normal_pdf, 1.0002, 0.007;
	63	rho, beta_pdf, 0.129, 0.223;
	64	psi, beta_pdf, 0.65, 0.05;
	65	del, beta_pdf, 0.01, 0.005;
	66	stderr e_a, inv_gamma_pdf, 0.035449, inf;
	67	stderr e_m, inv_gamma_pdf, 0.008862, inf;
	68	end;
	69
	70	varobs gp_obs gy_obs;
	71
	72	//estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,mode_check);
	73	estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0,mode_compute=0);

+74

-0

tests/kalman_filter_smoother/fs2000_1.mod less more

	0	// See fs2000.mod in the examples/ directory for details on the model
	1	// For initial value of the parameter, the filter reaches steady state at period 137
	2
	3	var m P c e W R k d n l gy_obs gp_obs y dA;
	4	varexo e_a e_m;
	5
	6	parameters alp bet gam mst rho psi del;
	7
	8	alp = 0.33;
	9	bet = 0.99;
	10	gam = 0.003;
	11	mst = 1.011;
	12	rho = 0.7;
	13	psi = 0.787;
	14	del = 0.02;
	15
	16	model;
	17	dA = exp(gam+e_a);
	18	log(m) = (1-rho)log(mst) + rholog(m(-1))+e_m;
	19	-P/(c(+1)P(+1)m)+betP(+1)(alpexp(-alp(gam+log(e(+1))))k^(alp-1)n(+1)^(1-alp)+(1-del)exp(-(gam+log(e(+1)))))/(c(+2)P(+2)*m(+1))=0;
	20	W = l/n;
	21	-(psi/(1-psi))(cP/(1-n))+l/n = 0;
	22	R = P(1-alp)exp(-alp(gam+e_a))k(-1)^alp*n^(-alp)/W;
	23	1/(cP)-betP(1-alp)exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)/(mlc(+1)P(+1)) = 0;
	24	c+k = exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)+(1-del)exp(-(gam+e_a))*k(-1);
	25	P*c = m;
	26	m-1+d = l;
	27	e = exp(e_a);
	28	y = k(-1)^alpn^(1-alp)exp(-alp*(gam+e_a));
	29	gy_obs = dA*y/y(-1);
	30	gp_obs = (P/P(-1))*m(-1)/dA;
	31	end;
	32
	33	initval;
	34	k = 6;
	35	m = mst;
	36	P = 2.25;
	37	c = 0.45;
	38	e = 1;
	39	W = 4;
	40	R = 1.02;
	41	d = 0.85;
	42	n = 0.19;
	43	l = 0.86;
	44	y = 0.6;
	45	gy_obs = exp(gam);
	46	gp_obs = exp(-gam);
	47	dA = exp(gam);
	48	end;
	49
	50	shocks;
	51	var e_a; stderr 0.014;
	52	var e_m; stderr 0.005;
	53	end;
	54
	55	steady;
	56
	57	check;
	58
	59	estimated_params;
	60	alp, beta_pdf, 0.356, 0.02;
	61	bet, beta_pdf, 0.993, 0.002;
	62	gam, normal_pdf, 0.0085, 0.003;
	63	mst, normal_pdf, 1.0002, 0.007;
	64	rho, beta_pdf, 0.129, 0.223;
	65	psi, beta_pdf, 0.65, 0.05;
	66	del, beta_pdf, 0.01, 0.005;
	67	stderr e_a, inv_gamma_pdf, 0.035449, inf;
	68	stderr e_m, inv_gamma_pdf, 0.008862, inf;
	69	end;
	70
	71	varobs gp_obs gy_obs;
	72
	73	estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0,mode_compute=0);

+74

-0

tests/kalman_filter_smoother/fs2000_2.mod less more

	0	// See fs2000.mod in the examples/ directory for details on the model
	1	// For initial value of the parameter, the filter reaches steady state at period 137
	2
	3	var m P c e W R k d n l gy_obs gp_obs y dA;
	4	varexo e_a e_m;
	5
	6	parameters alp bet gam mst rho psi del;
	7
	8	alp = 0.33;
	9	bet = 0.99;
	10	gam = 0.003;
	11	mst = 1.011;
	12	rho = 0.7;
	13	psi = 0.787;
	14	del = 0.02;
	15
	16	model;
	17	dA = exp(gam+e_a);
	18	log(m) = (1-rho)log(mst) + rholog(m(-1))+e_m;
	19	-P/(c(+1)P(+1)m)+betP(+1)(alpexp(-alp(gam+log(e(+1))))k^(alp-1)n(+1)^(1-alp)+(1-del)exp(-(gam+log(e(+1)))))/(c(+2)P(+2)*m(+1))=0;
	20	W = l/n;
	21	-(psi/(1-psi))(cP/(1-n))+l/n = 0;
	22	R = P(1-alp)exp(-alp(gam+e_a))k(-1)^alp*n^(-alp)/W;
	23	1/(cP)-betP(1-alp)exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)/(mlc(+1)P(+1)) = 0;
	24	c+k = exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)+(1-del)exp(-(gam+e_a))*k(-1);
	25	P*c = m;
	26	m-1+d = l;
	27	e = exp(e_a);
	28	y = k(-1)^alpn^(1-alp)exp(-alp*(gam+e_a));
	29	gy_obs = dA*y/y(-1);
	30	gp_obs = (P/P(-1))*m(-1)/dA;
	31	end;
	32
	33	initval;
	34	k = 6;
	35	m = mst;
	36	P = 2.25;
	37	c = 0.45;
	38	e = 1;
	39	W = 4;
	40	R = 1.02;
	41	d = 0.85;
	42	n = 0.19;
	43	l = 0.86;
	44	y = 0.6;
	45	gy_obs = exp(gam);
	46	gp_obs = exp(-gam);
	47	dA = exp(gam);
	48	end;
	49
	50	shocks;
	51	var e_a; stderr 0.014;
	52	var e_m; stderr 0.005;
	53	end;
	54
	55	steady;
	56
	57	check;
	58
	59	estimated_params;
	60	alp, beta_pdf, 0.356, 0.02;
	61	bet, beta_pdf, 0.993, 0.002;
	62	gam, normal_pdf, 0.0085, 0.003;
	63	mst, normal_pdf, 1.0002, 0.007;
	64	rho, beta_pdf, 0.129, 0.223;
	65	psi, beta_pdf, 0.65, 0.05;
	66	del, beta_pdf, 0.01, 0.005;
	67	stderr e_a, inv_gamma_pdf, 0.035449, inf;
	68	stderr e_m, inv_gamma_pdf, 0.008862, inf;
	69	end;
	70
	71	varobs gp_obs gy_obs;
	72
	73	estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0,mode_compute=0,kalman_algo=2);

+79

-0

tests/kalman_filter_smoother/fs2000a.mod less more

	0	// See fs2000.mod in the examples/ directory for details on the model
	1	// This version estimates the model in level rather than in growth rates
	2
	3	var m P c e W R k d n l gy_obs gp_obs Y_obs P_obs y dA;
	4	varexo e_a e_m;
	5
	6	parameters alp bet gam mst rho psi del;
	7
	8	alp = 0.33;
	9	bet = 0.99;
	10	gam = 0.003;
	11	mst = 1.011;
	12	rho = 0.7;
	13	psi = 0.787;
	14	del = 0.02;
	15
	16	model;
	17	dA = exp(gam+e_a);
	18	log(m) = (1-rho)log(mst) + rholog(m(-1))+e_m;
	19	-P/(c(+1)P(+1)m)+betP(+1)(alpexp(-alp(gam+log(e(+1))))k^(alp-1)n(+1)^(1-alp)+(1-del)exp(-(gam+log(e(+1)))))/(c(+2)P(+2)*m(+1))=0;
	20	W = l/n;
	21	-(psi/(1-psi))(cP/(1-n))+l/n = 0;
	22	R = P(1-alp)exp(-alp(gam+e_a))k(-1)^alp*n^(-alp)/W;
	23	1/(cP)-betP(1-alp)exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)/(mlc(+1)P(+1)) = 0;
	24	c+k = exp(-alp(gam+e_a))k(-1)^alpn^(1-alp)+(1-del)exp(-(gam+e_a))*k(-1);
	25	P*c = m;
	26	m-1+d = l;
	27	e = exp(e_a);
	28	y = k(-1)^alpn^(1-alp)exp(-alp*(gam+e_a));
	29	gy_obs = dA*y/y(-1);
	30	gp_obs = (P/P(-1))*m(-1)/dA;
	31	Y_obs/Y_obs(-1) = gy_obs;
	32	P_obs/P_obs(-1) = gp_obs;
	33	end;
	34
	35	initval;
	36	k = 6;
	37	m = mst;
	38	P = 2.25;
	39	c = 0.45;
	40	e = 1;
	41	W = 4;
	42	R = 1.02;
	43	d = 0.85;
	44	n = 0.19;
	45	l = 0.86;
	46	y = 0.6;
	47	gy_obs = exp(gam);
	48	gp_obs = exp(-gam);
	49	dA = exp(gam);
	50	end;
	51
	52	shocks;
	53	var e_a; stderr 0.014;
	54	var e_m; stderr 0.005;
	55	end;
	56
	57	estimated_params;
	58	alp, beta_pdf, 0.356, 0.02;
	59	bet, beta_pdf, 0.993, 0.002;
	60	gam, normal_pdf, 0.0085, 0.003;
	61	mst, normal_pdf, 1.0002, 0.007;
	62	rho, beta_pdf, 0.129, 0.223;
	63	psi, beta_pdf, 0.65, 0.05;
	64	del, beta_pdf, 0.01, 0.005;
	65	stderr e_a, inv_gamma_pdf, 0.035449, inf;
	66	stderr e_m, inv_gamma_pdf, 0.008862, inf;
	67	end;
	68
	69	varobs P_obs Y_obs;
	70
	71	observation_trends;
	72	P_obs (log(mst)-gam);
	73	Y_obs (gam);
	74	end;
	75
	76	estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0,
	77	mode_compute=0,mh_nblocks=2,mh_drop=0.45,mh_jscale=0.65,diffuse_filter,smoother) P_obs gp_obs gy_obs;
	78

+60

-0

tests/kalman_filter_smoother/fs2000a_steadystate.m less more

	0	% computes the steady state of fs2000 analyticaly
	1	% largely inspired by the program of F. Schorfheide
	2	function [ys,check] = fs2000a_steadystate(ys,exe)
	3	global M_
	4
	5	alp = M_.params(1);
	6	bet = M_.params(2);
	7	gam = M_.params(3);
	8	mst = M_.params(4);
	9	rho = M_.params(5);
	10	psi = M_.params(6);
	11	del = M_.params(7);
	12
	13	check = 0;
	14
	15	dA = exp(gam);
	16	gst = 1/dA;
	17	m = mst;
	18
	19	khst = ( (1-gstbet(1-del)) / (alpgst^alpbet) )^(1/(alp-1));
	20	xist = ( ((khstgst)^alp - (1-gst(1-del))*khst)/mst )^(-1);
	21	nust = psimst^2/( (1-alp)(1-psi)betgst^alp*khst^alp );
	22	n = xist/(nust+xist);
	23	P = xist + nust;
	24	k = khst*n;
	25
	26	l = psimstn/( (1-psi)*(1-n) );
	27	c = mst/P;
	28	d = l - mst + 1;
	29	y = k^alpn^(1-alp)gst^alp;
	30	R = mst/bet;
	31	W = l/n;
	32	ist = y-c;
	33	q = 1 - d;
	34
	35	e = 1;
	36
	37	gp_obs = m/dA;
	38	gy_obs = dA;
	39
	40	P_obs = 1;
	41	Y_obs = 1;
	42
	43	ys =[
	44	m
	45	P
	46	c
	47	e
	48	W
	49	R
	50	k
	51	d
	52	n
	53	l
	54	gy_obs
	55	gp_obs
	56	Y_obs
	57	P_obs
	58	y
	59	dA ];⏎

+828

-0

tests/kalman_filter_smoother/fsdat_simul.m less more

	0	gy_obs =[
	1	1.0030045
	2	0.99990934
	3	1.0172778
	4	0.99464043
	5	1.0253423
	6	1.0150215
	7	0.97772557
	8	0.97832186
	9	1.0159561
	10	1.0085937
	11	1.0102649
	12	1.0007604
	13	1.0112596
	14	1.0163279
	15	1.0173204
	16	1.0103896
	17	1.0006493
	18	0.99447124
	19	1.0196405
	20	1.0089304
	21	0.99650737
	22	1.0139707
	23	0.97865842
	24	1.0192225
	25	0.99139628
	26	1.0141362
	27	1.0196612
	28	0.97483476
	29	0.99686151
	30	0.99594464
	31	1.0000642
	32	1.0172243
	33	1.0025773
	34	0.97199728
	35	1.0217815
	36	1.0219949
	37	0.99490252
	38	1.0190728
	39	1.0111337
	40	1.0003792
	41	0.98969164
	42	1.010438
	43	1.0216309
	44	1.0016671
	45	1.0357588
	46	0.98803787
	47	1.0093457
	48	1.0177035
	49	0.98548204
	50	1.0274294
	51	1.0141377
	52	1.0091174
	53	0.96427632
	54	1.0083272
	55	1.0007882
	56	0.99038262
	57	1.0031336
	58	0.99500213
	59	0.98203716
	60	0.9889452
	61	1.011632
	62	0.99451949
	63	0.97291047
	64	0.98750871
	65	0.99992418
	66	0.97657318
	67	0.99930448
	68	1.0008515
	69	1.0044064
	70	0.98133792
	71	1.0091702
	72	1.0087023
	73	1.0119876
	74	1.0143019
	75	1.0311061
	76	0.99340471
	77	1.0057428
	78	0.99197259
	79	1.0071019
	80	0.99448853
	81	1.0061819
	82	1.0070088
	83	0.9950913
	84	1.0302318
	85	0.9817693
	86	1.0072885
	87	0.97355282
	88	0.98782586
	89	1.0136674
	90	0.99863956
	91	1.0205668
	92	0.99611384
	93	1.0073805
	94	0.99691529
	95	1.0089194
	96	1.0030467
	97	1.0112006
	98	1.0260523
	99	0.97803331
	100	0.99423374
	101	1.0043727
	102	1.0140173
	103	1.0111473
	104	0.99524348
	105	0.99775943
	106	0.9958619
	107	0.9982344
	108	1.0210212
	109	1.0022288
	110	1.0014801
	111	1.011456
	112	1.0124871
	113	0.99843599
	114	0.99324886
	115	0.99912838
	116	1.003327
	117	1.0072071
	118	1.0115223
	119	1.009266
	120	1.0070554
	121	1.0129916
	122	1.0053413
	123	1.0051638
	124	0.99212952
	125	1.0214422
	126	0.98716707
	127	0.99905788
	128	0.98877357
	129	0.98568476
	130	0.99767393
	131	1.0061791
	132	0.98423439
	133	0.99492949
	134	0.98786999
	135	0.99754239
	136	1.0168619
	137	0.99472384
	138	1.0041658
	139	0.98123181
	140	1.0112882
	141	0.99245422
	142	1.0010255
	143	1.0017799
	144	1.0089968
	145	1.0072824
	146	0.99768475
	147	1.0044726
	148	1.0118678
	149	1.0056385
	150	1.0276965
	151	1.0025122
	152	1.0065161
	153	1.0234338
	154	0.99760167
	155	0.98922272
	156	1.0101918
	157	1.011615
	158	1.0085286
	159	1.0074455
	160	0.98866757
	161	0.99959012
	162	1.0129881
	163	0.99127881
	164	0.97971901
	165	1.0185314
	166	1.020054
	167	1.0132605
	168	0.98063643
	169	0.99490253
	170	1.0101531
	171	1.0004526
	172	1.0059109
	173	0.98974491
	174	1.0062391
	175	1.0216488
	176	0.99398446
	177	0.97786609
	178	1.0019274
	179	0.99587153
	180	1.0095881
	181	1.0111887
	182	0.99457649
	183	0.97896734
	184	1.000172
	185	1.0142951
	186	1.0034224
	187	1.0037242
	188	1.0016059
	189	1.016556
	190	0.99687023
	191	1.0117844
	192	1.0059212
	193	0.98083159
	194	0.98638851
	195	1.0128713
	196	1.0096232
	197	1.0115891
	198	1.0011213
	199	1.0147105
	200	1.0066344
	201	1.0164429
	202	0.99825038
	203	0.99403411
	204
	205	];
	206
	207	gp_obs =[
	208	1.0079715
	209	1.0074573
	210	1.0153107
	211	1.0152677
	212	1.0011653
	213	0.99950061
	214	1.0328311
	215	1.0192317
	216	1.009827
	217	0.99588916
	218	1.007474
	219	1.0113061
	220	0.98696624
	221	0.99978663
	222	0.98240542
	223	0.98861723
	224	0.99008763
	225	1.0185076
	226	1.0052452
	227	0.99447194
	228	1.0092685
	229	1.01208
	230	1.0105237
	231	0.98513875
	232	1.0165628
	233	0.99485934
	234	1.0050255
	235	1.0140756
	236	1.0093128
	237	1.0155868
	238	1.0107023
	239	0.99212762
	240	1.0095465
	241	1.0028435
	242	1.0069437
	243	1.0070473
	244	1.0145902
	245	1.0186922
	246	1.0059917
	247	1.0113072
	248	1.0107386
	249	0.99769196
	250	0.99793444
	251	1.0050791
	252	0.98307821
	253	1.0107594
	254	0.99689982
	255	0.98667064
	256	0.9991662
	257	0.98274722
	258	0.98422032
	259	0.99393016
	260	1.0118567
	261	0.99912781
	262	1.0023744
	263	1.0086662
	264	1.0164773
	265	1.0169327
	266	1.0372478
	267	1.0314242
	268	1.0004256
	269	1.0110541
	270	1.0076575
	271	1.0119851
	272	1.0055188
	273	1.0213959
	274	1.0234416
	275	1.0264917
	276	1.0292725
	277	1.0385184
	278	1.0200999
	279	1.0107697
	280	1.008583
	281	1.0200332
	282	1.0030413
	283	1.0108659
	284	1.0185145
	285	1.0168619
	286	1.0180462
	287	1.0239657
	288	1.0205509
	289	1.0189973
	290	1.0246446
	291	1.0135089
	292	1.0352973
	293	1.0099289
	294	1.0266474
	295	1.0279829
	296	1.0101653
	297	1.041216
	298	1.0103861
	299	1.0114727
	300	1.0054605
	301	1.0190722
	302	1.0114837
	303	1.0179213
	304	1.006082
	305	1.0049696
	306	1.0143629
	307	0.9971036
	308	1.0005602
	309	1.0078403
	310	1.0240222
	311	1.0195063
	312	1.0355136
	313	1.0218743
	314	1.0171331
	315	1.0049817
	316	1.0140974
	317	1.0168431
	318	1.0049966
	319	1.0045568
	320	1.0156414
	321	1.0273055
	322	1.0197653
	323	1.0030624
	324	1.0154993
	325	0.99782084
	326	0.99711648
	327	1.014408
	328	1.0057417
	329	0.99936837
	330	1.0096934
	331	1.0095138
	332	1.0057734
	333	1.0114497
	334	1.0059784
	335	1.0328889
	336	1.0098032
	337	1.0041114
	338	1.0101247
	339	1.0181588
	340	1.0115712
	341	1.0227509
	342	1.0065104
	343	1.0110902
	344	1.0298169
	345	1.0089532
	346	1.0368733
	347	1.0123033
	348	1.0060763
	349	1.0150937
	350	1.0239325
	351	0.99555536
	352	0.99861271
	353	1.0076201
	354	0.99941535
	355	1.0119522
	356	1.0129183
	357	0.99288924
	358	1.0260784
	359	1.0144982
	360	1.0121985
	361	1.0234916
	362	1.02215
	363	1.0190118
	364	1.0172679
	365	1.0118398
	366	1.0002123
	367	1.0092124
	368	1.0071943
	369	0.99508468
	370	1.0019303
	371	1.0030733
	372	0.9964198
	373	1.0027298
	374	0.99797614
	375	1.006942
	376	0.99793928
	377	1.0083214
	378	1.0283732
	379	1.0111102
	380	1.016936
	381	1.0229061
	382	0.98846454
	383	1.0015387
	384	1.0201769
	385	1.0079822
	386	1.0064007
	387	1.0095543
	388	1.0092207
	389	1.0135485
	390	1.0198974
	391	1.0140252
	392	1.0128686
	393	1.0092903
	394	1.0141974
	395	1.0023492
	396	0.99731455
	397	1.0026598
	398	0.99303643
	399	1.0036469
	400	1.0160975
	401	1.0368378
	402	1.0139625
	403	1.01493
	404	1.0113531
	405	1.0114548
	406	0.99833441
	407	0.99648401
	408	0.97645361
	409	1.0154053
	410	1.01703
	411
	412	];
	413
	414	Y_obs =[
	415	1
	416	0.99690484
	417	1.0111781
	418	1.0028141
	419	1.0251518
	420	1.0371688
	421	1.0118899
	422	0.98720726
	423	1.0001589
	424	1.0057481
	425	1.0130085
	426	1.0107643
	427	1.0190194
	428	1.0323428
	429	1.0466587
	430	1.0540438
	431	1.0516886
	432	1.0431553
	433	1.0597913
	434	1.0657172
	435	1.0592201
	436	1.0701863
	437	1.0458402
	438	1.0620582
	439	1.0504499
	440	1.0615817
	441	1.0782384
	442	1.0500687
	443	1.0439257
	444	1.0368658
	445	1.0339255
	446	1.0481453
	447	1.0477181
	448	1.0167109
	449	1.0354878
	450	1.0544782
	451	1.0463762
	452	1.0624445
	453	1.0705737
	454	1.0679484
	455	1.0546356
	456	1.0620691
	457	1.0806955
	458	1.0793581
	459	1.1121124
	460	1.0971458
	461	1.1034869
	462	1.1181859
	463	1.1006634
	464	1.1250883
	465	1.1362214
	466	1.1423343
	467	1.1036061
	468	1.1089288
	469	1.1067125
	470	1.0940906
	471	1.0942197
	472	1.0862174
	473	1.06525
	474	1.0511907
	475	1.0598182
	476	1.0513331
	477	1.0212391
	478	1.0057433
	479	1.002663
	480	0.97623167
	481	0.97253165
	482	0.97037865
	483	0.97178055
	484	0.95011397
	485	0.95627969
	486	0.96197747
	487	0.97096053
	488	0.98225794
	489	1.0103595
	490	1.0007597
	491	1.003498
	492	0.99246608
	493	0.99656347
	494	0.98804749
	495	0.99122491
	496	0.99522926
	497	0.98731605
	498	1.0145434
	499	0.99330816
	500	0.99759216
	501	0.96814048
	502	0.95296183
	503	0.96362471
	504	0.95925977
	505	0.97682205
	506	0.96993138
	507	0.9743074
	508	0.96821818
	509	0.97413308
	510	0.9741753
	511	0.98237142
	512	1.0054193
	513	0.98044807
	514	0.9716773
	515	0.9730455
	516	0.98405828
	517	0.99220103
	518	0.98444001
	519	0.97919493
	520	0.97205233
	521	0.96728223
	522	0.98529893
	523	0.98452324
	524	0.98299888
	525	0.99145042
	526	1.000933
	527	0.99636447
	528	0.98660883
	529	0.98273271
	530	0.98305518
	531	0.98725774
	532	0.99577549
	533	1.002037
	534	1.0060879
	535	1.016075
	536	1.0184118
	537	1.0205711
	538	1.0096961
	539	1.0281337
	540	1.0122963
	541	1.0083497
	542	0.99411874
	543	0.976799
	544	0.97146842
	545	0.97464304
	546	0.95587292
	547	0.94779791
	548	0.93266339
	549	0.92720128
	550	0.94105864
	551	0.93277798
	552	0.93393927
	553	0.91216657
	554	0.92045028
	555	0.9099
	556	0.90792098
	557	0.90669634
	558	0.91268867
	559	0.91696661
	560	0.91164685
	561	0.91311495
	562	0.92197825
	563	0.92461222
	564	0.94930422
	565	0.9488119
	566	0.95232353
	567	0.97275278
	568	0.96734995
	569	0.95356817
	570	0.96075548
	571	0.96936594
	572	0.97489002
	573	0.97933106
	574	0.96499412
	575	0.96157973
	576	0.97156334
	577	0.95983765
	578	0.93655215
	579	0.95207909
	580	0.96912862
	581	0.97938462
	582	0.95701655
	583	0.94891457
	584	0.95606317
	585	0.95351125
	586	0.95641767
	587	0.94315807
	588	0.94639265
	589	0.96503697
	590	0.95601693
	591	0.93087851
	592	0.92980141
	593	0.92266844
	594	0.92925206
	595	0.93743628
	596	0.92900826
	597	0.9049711
	598	0.90213859
	599	0.91342916
	600	0.91384707
	601	0.91456681
	602	0.91316822
	603	0.92671976
	604	0.92058549
	605	0.92936541
	606	0.93228212
	607	0.91010921
	608	0.89349322
	609	0.90336005
	610	0.90997873
	611	0.91856328
	612	0.91668007
	613	0.92838606
	614	0.932016
	615	0.94545438
	616	0.94070026
	617	0.93172987
	618
	619	];
	620
	621	P_obs =[
	622	1
	623	0.99948573
	624	1.0068249
	625	1.0141211
	626	1.0073149
	627	0.99884398
	628	1.0237035
	629	1.0349636
	630	1.036819
	631	1.0247366
	632	1.0242391
	633	1.0275737
	634	1.0065684
	635	0.99838346
	636	0.97281734
	637	0.95346302
	638	0.9355791
	639	0.9461152
	640	0.94338882
	641	0.92988921
	642	0.9311862
	643	0.93529467
	644	0.93784681
	645	0.91501401
	646	0.92360522
	647	0.91049302
	648	0.90754698
	649	0.91365103
	650	0.91499228
	651	0.92260749
	652	0.92533824
	653	0.90949431
	654	0.91106924
	655	0.90594116
	656	0.90491334
	657	0.9039891
	658	0.91060772
	659	0.92132842
	660	0.91934854
	661	0.92268418
	662	0.92545127
	663	0.91517169
	664	0.90513459
	665	0.90224212
	666	0.87734878
	667	0.88013667
	668	0.86906494
	669	0.84776403
	670	0.83895869
	671	0.81373437
	672	0.78998314
	673	0.77594176
	674	0.77982695
	675	0.77098321
	676	0.76538611
	677	0.76608075
	678	0.77458654
	679	0.78354767
	680	0.81282389
	681	0.83627649
	682	0.82873051
	683	0.83181309
	684	0.83149903
	685	0.83551261
	686	0.83305985
	687	0.84648418
	688	0.86195421
	689	0.88047436
	690	0.90177533
	691	0.93232215
	692	0.94445051
	693	0.9472487
	694	0.94786015
	695	0.95992178
	696	0.95499149
	697	0.95788581
	698	0.9684288
	699	0.97731917
	700	0.98739379
	701	1.0033879
	702	1.0159673
	703	1.0269931
	704	1.0436661
	705	1.0492034
	706	1.0765292
	707	1.0784865
	708	1.0971624
	709	1.1171737
	710	1.1193675
	711	1.1526119
	712	1.1550265
	713	1.1585277
	714	1.1560166
	715	1.1671172
	716	1.1706294
	717	1.1805791
	718	1.1786896
	719	1.1756876
	720	1.1820789
	721	1.171211
	722	1.1637997
	723	1.1636684
	724	1.179719
	725	1.1912538
	726	1.2187959
	727	1.2326986
	728	1.2418602
	729	1.2388704
	730	1.2449963
	731	1.2538678
	732	1.2508929
	733	1.2474781
	734	1.255148
	735	1.274482
	736	1.2862757
	737	1.2813665
	738	1.2888943
	739	1.2787436
	740	1.2678886
	741	1.274325
	742	1.2720952
	743	1.263492
	744	1.2652139
	745	1.2667561
	746	1.264558
	747	1.2680362
	748	1.2660431
	749	1.2909605
	750	1.2927921
	751	1.288932
	752	1.2910852
	753	1.3012725
	754	1.3048721
	755	1.3196515
	756	1.3181903
	757	1.321309
	758	1.3431543
	759	1.344136
	760	1.3730377
	761	1.3773695
	762	1.3754742
	763	1.3825964
	764	1.3985574
	765	1.3861412
	766	1.3767823
	767	1.3764309
	768	1.3678747
	769	1.3718554
	770	1.3768022
	771	1.3617199
	772	1.3798267
	773	1.3863533
	774	1.3905803
	775	1.4061004
	776	1.4202788
	777	1.4313191
	778	1.4406155
	779	1.4444837
	780	1.4367244
	781	1.4379653
	782	1.4371881
	783	1.4243012
	784	1.41826
	785	1.4133617
	786	1.40181
	787	1.3965683
	788	1.3865729
	789	1.3855433
	790	1.3755111
	791	1.3758609
	792	1.3962625
	793	1.3994012
	794	1.4083656
	795	1.4233002
	796	1.4037932
	797	1.3973604
	798	1.4095657
	799	1.4095764
	800	1.4080055
	801	1.4095882
	802	1.4108374
	803	1.4164143
	804	1.4283402
	805	1.4343939
	806	1.4392909
	807	1.4406097
	808	1.4468355
	809	1.4412132
	810	1.4305562
	811	1.4252445
	812	1.4103094
	813	1.4059847
	814	1.4141106
	815	1.4429769
	816	1.4489679
	817	1.4559263
	818	1.4593079
	819	1.4627911
	820	1.453154
	821	1.4416665
	822	1.4101485
	823	1.4175823
	824	1.4266407
	825
	826	];
	827

+38

-0

tests/kalman_filter_smoother/gen_data.mod less more

	0	var w x y z dw dx dy;
	1	varexo e_w e_x e_y e_z;
	2
	3	parameters rho_w rho_x rho_y rho_z a1 a2 a3 b c;
	4
	5	rho_w = 0.5;
	6	rho_x = 0.5;
	7	rho_y = 0.5;
	8	rho_z = 0.8;
	9
	10	a1 = 0.1;
	11	a2 = -0.1;
	12	a3 = 0.1;
	13	b = 1;
	14	c = 1;
	15
	16	model(linear);
	17	dw = rho_wdw(-1)+a1(x(-1)-by(-1)-cw(-1))+e_w;
	18	dx = rho_xdx(-1)+a2(x(-1)-by(-1)-cw(-1))+e_x;
	19	dy = rho_ydy(-1)+a3(x(-1)-b*y(-1))+e_y;
	20	z = rho_z*z(-1)+dw-dx+e_z;
	21	w = w(-1)+dw;
	22	x = x(-1)+dx;
	23	y = y(-1)+dy;
	24	end;
	25
	26	shocks;
	27	var e_w; stderr 0.05;
	28	var e_x; stderr 0.05;
	29	var e_y; stderr 0.05;
	30	var e_z; stderr 0.05;
	31	end;
	32
	33	stoch_simul(periods=2000,irf=0);
	34
	35	plot([w x y z]);
	36
	37	save data.mat w x y z dw dx dy;⏎

+100

-0

tests/ls2003/data_ca1.m less more

	0	data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
	1	-0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
	2	-0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
	3	-0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
	4	-0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
	5	-0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
	6	-0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
	7	1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
	8	2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
	9	1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
	10	1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
	11	1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
	12	1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
	13	0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
	14	1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
	15	1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
	16	0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
	17	1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
	18	1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
	19	-0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
	20	0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
	21	0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
	22	-0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
	23	2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
	24	1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
	25	1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
	26	1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
	27	1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
	28	1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
	29	0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
	30	0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
	31	1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
	32	0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
	33	0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
	34	0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
	35	0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
	36	-0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
	37	-0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
	38	-0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
	39	-1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
	40	0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
	41	0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
	42	0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
	43	-0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
	44	0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
	45	0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
	46	0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
	47	0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
	48	0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
	49	0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
	50	0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
	51	1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
	52	1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
	53	1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
	54	0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
	55	0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
	56	-0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
	57	0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
	58	0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
	59	0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
	60	0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
	61	1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
	62	0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
	63	0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
	64	1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
	65	1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
	66	0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
	67	1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
	68	0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
	69	1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
	70	1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
	71	1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
	72	1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
	73	1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
	74	1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
	75	1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
	76	0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
	77	1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
	78	0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
	79	0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
	80	0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
	81	-0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
	82	0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
	83	1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
	84	1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
	85	0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
	86	];
	87
	88	data = reshape(data,5,86)';
	89	y_obs = data(:,1);
	90	pie_obs = data(:,2);
	91	R_obs = data(:,3);
	92	de = data(:,4);
	93	dq = data(:,5);
	94
	95	%Country: Canada
	96	%Sample Range: 1981:2 to 2002:3
	97	%Observations: 86
	98	%Variables: Real GDP Growth [%], Inflation [annualized %], Nom Rate [%],
	99	% Exchange Rate Change [%], Terms of Trade Change [%]

+100

-0

tests/measurement_errors/data_ca1.m less more

	0	data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
	1	-0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
	2	-0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
	3	-0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
	4	-0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
	5	-0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
	6	-0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
	7	1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
	8	2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
	9	1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
	10	1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
	11	1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
	12	1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
	13	0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
	14	1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
	15	1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
	16	0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
	17	1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
	18	1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
	19	-0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
	20	0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
	21	0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
	22	-0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
	23	2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
	24	1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
	25	1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
	26	1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
	27	1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
	28	1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
	29	0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
	30	0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
	31	1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
	32	0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
	33	0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
	34	0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
	35	0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
	36	-0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
	37	-0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
	38	-0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
	39	-1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
	40	0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
	41	0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
	42	0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
	43	-0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
	44	0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
	45	0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
	46	0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
	47	0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
	48	0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
	49	0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
	50	0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
	51	1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
	52	1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
	53	1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
	54	0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
	55	0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
	56	-0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
	57	0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
	58	0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
	59	0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
	60	0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
	61	1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
	62	0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
	63	0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
	64	1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
	65	1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
	66	0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
	67	1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
	68	0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
	69	1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
	70	1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
	71	1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
	72	1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
	73	1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
	74	1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
	75	1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
	76	0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
	77	1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
	78	0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
	79	0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
	80	0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
	81	-0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
	82	0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
	83	1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
	84	1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
	85	0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
	86	];
	87
	88	data = reshape(data,5,86)';
	89	y_obs = data(:,1);
	90	pie_obs = data(:,2);
	91	R_obs = data(:,3);
	92	de = data(:,4);
	93	dq = data(:,5);
	94
	95	%Country: Canada
	96	%Sample Range: 1981:2 to 2002:3
	97	%Observations: 86
	98	%Variables: Real GDP Growth [%], Inflation [annualized %], Nom Rate [%],
	99	% Exchange Rate Change [%], Terms of Trade Change [%]

+76

-0

tests/missing/simulate_data_with_missing_observations.m less more

	0	function data = simulate_data_with_missing_observations(n,m,S,options)
	1	% Simulates data with missing observations.
	2	%
	3	% We simulate data using a n-dimensional VAR(1) model.
	4	%
	5	% INPUTS
	6	% n [integer] scalar, number of variables.
	7	% m [integer] scalar, number of observed variables.
	8	% S [integer] scalar, maximum number of observations per observed variable.
	9	% options [struct] structure of options:
	10	% * if options.missing_info{1} = 1 the missing variables are at the beginning of the sample.
	11	% * if options.missing_info{1} = 2 the missing observations are at the end of the sample.
	12	% * if options.missing_info{1} = 3 the missing observations are randomly distributed.
	13	% * options.missing_info{2} is a vector of integer designing the observed variables for which observations are missing.
	14	% * if options.missing_info{3} is an integer scalar then it defines the number of missing observations per variable.
	15	% * if options.missing_info{3} is a double scalar (in [0,1]) it defines the frequency of missing observations per variable.
	16	% * options.unit_root_info is a scalar integer specifying the number of unit roots in the model.
	17	%
	18	% OUTPUTS
	19	% none
	20	%
	21	% SPECIAL REQUIREMENTS
	22	% none
	23
	24	% Copyright (C) 2010 Dynare Team
	25	%
	26	% This file is part of Dynare.
	27	%
	28	% Dynare is free software: you can redistribute it and/or modify
	29	% it under the terms of the GNU General Public License as published miy
	30	% the Free Software Foundation, either version 3 of the License, or
	31	% (at your option) any later version.
	32	%
	33	% Dynare is distributed in the hope that it will be useful,
	34	% but WITHOUT ANY WARRANTY; without even the implied warranty of
	35	% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	36	% GNU General Public License for more details.
	37	%
	38	% You should have received a copy of the GNU General Public License
	39	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	40
	41	if n<=m
	42	error('n must be greater than m!')
	43	end
	44
	45	% Build the autoregressive matrix.
	46	T_eigenvalues = rand(n,1)*2-1;
	47	if options.unit_root_info
	48	T_eigenvalues(1:options.unit_root_info) = ones(options.unit_root_info,1);
	49	end
	50	T_eigenvectors = randn(n,n);
	51	T = T_eigenvectorsdiag(T_eigenvalues)inv(T_eigenvectors);
	52
	53
	54	% Simulate the VAR(1) model.
	55	data = zeros(n,S+100);
	56	for t=2:size(data,2)
	57	data(:,t) = Tdata(:,t-1)+.01randn(n,1);
	58	end
	59
	60	% Select the observed variables.
	61	data = transpose(data(1:m,100+1:end));
	62
	63	% Remove observations.
	64	if options.missing_info{1}==1
	65	data(1:options.missing_info{3},options.missing_info{2}) = NaN ;
	66	elseif options.missing_info{1}==2
	67	data(S:S-options.missing_info{3},options.missing_info{2}) = NaN ;
	68	elseif options.missing_info{1}==3
	69	for i=1:length(options.missing_info{2})
	70	idx = randi(T,ceil(options.missing_info{3}),1);
	71	data(idx,i) = NaN;
	72	end
	73	else
	74	error('Unknown option!')
	75	end⏎

+0

-33

~~tests/objectives/sgu_ex1.mod~~ less more

0		periods 20000;
1		var c k a;
2		varexo e;
3		parameters alpha beta delta gamma rho;
4
5		beta = 0.95;
6		delta = 1;
7		alpha = 0.3;
8		rho = 0;
9		gamma = 2;
10
11		model;
12		exp(c) + exp(k) = (1-delta) * exp(k(-1)) + exp(a) * exp(k(-1))^alpha;
13		exp(c)^(-gamma) = beta * exp(c(+1))^(-gamma) * (exp(a(+1)) * alpha * exp(k)^(alpha-1) + 1 - delta);
14		a = rho * a(-1) + e;
15		end;
16
17		initval;
18		k=0;
19		c=0;
20		a=0;
21		e=0;
22		end;
23
24		Sigma_e_ = 1;
25
26		stoch_simul(nomoments,irf=0,nocorr,ar=0);
27
28		global dr_
29		dr_obj_ = dr_;
30
31		save sgu_ex1 dr_obj_;
32

+1

-1

tests/optimal_policy/mult_elimination_test.mod less more

5	5	var A de dq dR pie pie_obs pie_s R R_obs y y_obs y_s ;
6	6	varexo e_A e_pies e_q e_ys ;
7	7
8		parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies ls1_r s1 s2 s3 s4 s5;
	8	parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
9	9
10	10	psi1 = 1.54;
11	11	psi2 = 0.25;

+1

-1

tests/optimal_policy/ramsey.mod less more

2	2	var y inflation r;
3	3	varexo y_ inf_;
4	4
5		parameters delta sigma alpha kappa gammarr gammax0 gammac0 rbar lambda1 lambda2;
	5	parameters delta sigma alpha kappa lambda1 lambda2;
6	6
7	7	delta = 0.44;
8	8	kappa = 0.18;

+9

-0

tests/printMakeCheckMatlabErrMsg.m less more

	0	function printMakeCheckMatlabErrMsg(modfilename, exception)
	1	fprintf('\n********************************************\n');
	2	disp('* DYNARE-TEST-MATLAB ERROR ENCOUNTERED *');
	3	disp('********************************************');
	4	disp([' WHILE RUNNING MODFILE: ' modfilename]);
	5	fprintf('\n');
	6	disp(getReport(exception));
	7	fprintf('*************************************\n\n\n');
	8	end

+14

-0

tests/printMakeCheckOctaveErrMsg.m less more

	0	function printMakeCheckOctaveErrMsg(modfilename, err)
	1	printf("\n");
	2	printf("********************************************\n");
	3	printf("* DYNARE-TEST-OCTAVE ERROR ENCOUNTERED *\n");
	4	printf("********************************************\n");
	5	printf(" WHILE RUNNING MODFILE: %s\n", modfilename);
	6	printf(" MSG: %s\n", err.message);
	7	if (isfield(err, 'stack'))
	8	printf(" IN FILE: %s\n", err.stack(1).file);
	9	printf(" IN FUNCTION: %s\n", err.stack(1).name);
	10	printf(" ON LINE and COLUMN: %d and %d\n",err.stack(1).line,err.stack(1).column);
	11	end
	12	printf("*************************************\n\n\n");
	13	end

+100

-0

tests/recursive/data_ca1.m less more

	0	data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
	1	-0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
	2	-0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
	3	-0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
	4	-0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
	5	-0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
	6	-0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
	7	1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
	8	2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
	9	1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
	10	1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
	11	1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
	12	1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
	13	0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
	14	1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
	15	1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
	16	0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
	17	1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
	18	1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
	19	-0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
	20	0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
	21	0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
	22	-0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
	23	2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
	24	1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
	25	1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
	26	1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
	27	1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
	28	1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
	29	0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
	30	0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
	31	1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
	32	0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
	33	0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
	34	0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
	35	0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
	36	-0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
	37	-0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
	38	-0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
	39	-1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
	40	0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
	41	0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
	42	0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
	43	-0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
	44	0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
	45	0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
	46	0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
	47	0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
	48	0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
	49	0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
	50	0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
	51	1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
	52	1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
	53	1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
	54	0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
	55	0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
	56	-0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
	57	0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
	58	0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
	59	0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
	60	0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
	61	1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
	62	0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
	63	0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
	64	1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
	65	1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
	66	0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
	67	1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
	68	0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
	69	1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
	70	1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
	71	1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
	72	1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
	73	1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
	74	1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
	75	1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
	76	0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
	77	1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
	78	0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
	79	0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
	80	0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
	81	-0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
	82	0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
	83	1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
	84	1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
	85	0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
	86	];
	87
	88	data = reshape(data,5,86)';
	89	y_obs = data(:,1);
	90	pie_obs = data(:,2);
	91	R_obs = data(:,3);
	92	de = data(:,4);
	93	dq = data(:,5);
	94
	95	%Country: Canada
	96	%Sample Range: 1981:2 to 2002:3
	97	%Observations: 86
	98	%Variables: Real GDP Growth [%], Inflation [annualized %], Nom Rate [%],
	99	% Exchange Rate Change [%], Terms of Trade Change [%]

+65

-0

tests/recursive/ls2003.mod less more

	0	var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
	1	varexo e_R e_q e_ys e_pies e_A;
	2
	3	parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
	4
	5	psi1 = 1.54;
	6	psi2 = 0.25;
	7	psi3 = 0.25;
	8	rho_R = 0.5;
	9	alpha = 0.3;
	10	rr = 2.51;
	11	k = 0.5;
	12	tau = 0.5;
	13	rho_q = 0.4;
	14	rho_A = 0.2;
	15	rho_ys = 0.9;
	16	rho_pies = 0.7;
	17
	18
	19	model(linear);
	20	y = y(+1) - (tau +alpha(2-alpha)(1-tau))(R-pie(+1))-alpha(tau +alpha(2-alpha)(1-tau))dq(+1) + alpha(2-alpha)((1-tau)/tau)(y_s-y_s(+1))-A(+1);
	21	pie = exp(-rr/400)pie(+1)+alphaexp(-rr/400)dq(+1)-alphadq+(k/(tau+alpha(2-alpha)(1-tau)))y+alpha(2-alpha)(1-tau)/(tau(tau+alpha(2-alpha)(1-tau)))*y_s;
	22	pie = de+(1-alpha)*dq+pie_s;
	23	R = rho_RR(-1)+(1-rho_R)(psi1pie+psi2(y+alpha(2-alpha)((1-tau)/tau)y_s)+psi3de)+e_R;
	24	dq = rho_q*dq(-1)+e_q;
	25	y_s = rho_ys*y_s(-1)+e_ys;
	26	pie_s = rho_pies*pie_s(-1)+e_pies;
	27	A = rho_A*A(-1)+e_A;
	28	y_obs = y-y(-1)+A;
	29	pie_obs = 4*pie;
	30	R_obs = 4*R;
	31	end;
	32
	33	shocks;
	34	var e_R = 1.25^2;
	35	var e_q = 2.5^2;
	36	var e_A = 1.89;
	37	var e_ys = 1.89;
	38	var e_pies = 1.89;
	39	end;
	40
	41	varobs y_obs R_obs pie_obs dq de;
	42
	43	estimated_params;
	44	psi1 , gamma_pdf,1.5,0.5;
	45	psi2 , gamma_pdf,0.25,0.125;
	46	psi3 , gamma_pdf,0.25,0.125;
	47	rho_R ,beta_pdf,0.5,0.2;
	48	alpha ,beta_pdf,0.3,0.1;
	49	rr ,gamma_pdf,2.5,1;
	50	k , gamma_pdf,0.5,0.25;
	51	tau ,gamma_pdf,0.5,0.2;
	52	rho_q ,beta_pdf,0.4,0.2;
	53	rho_A ,beta_pdf,0.5,0.2;
	54	rho_ys ,beta_pdf,0.8,0.1;
	55	rho_pies,beta_pdf,0.7,0.15;
	56	stderr e_R,inv_gamma_pdf,1.2533,0.6551;
	57	stderr e_q,inv_gamma_pdf,2.5066,1.3103;
	58	stderr e_A,inv_gamma_pdf,1.2533,0.6551;
	59	stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
	60	stderr e_pies,inv_gamma_pdf,1.88,0.9827;
	61	end;
	62
	63	estimation(datafile=data_ca1,first_obs=8,nobs=[76 79],mh_nblocks=10,prefilter=1,mh_jscale=0.5,mh_replic=0,forecast=8) y_obs R_obs pie_obs dq de;
	64

+65

-0

tests/recursive/ls2003_bayesian.mod less more

	0	var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
	1	varexo e_R e_q e_ys e_pies e_A;
	2
	3	parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
	4
	5	psi1 = 1.54;
	6	psi2 = 0.25;
	7	psi3 = 0.25;
	8	rho_R = 0.5;
	9	alpha = 0.3;
	10	rr = 2.51;
	11	k = 0.5;
	12	tau = 0.5;
	13	rho_q = 0.4;
	14	rho_A = 0.2;
	15	rho_ys = 0.9;
	16	rho_pies = 0.7;
	17
	18
	19	model(linear);
	20	y = y(+1) - (tau +alpha(2-alpha)(1-tau))(R-pie(+1))-alpha(tau +alpha(2-alpha)(1-tau))dq(+1) + alpha(2-alpha)((1-tau)/tau)(y_s-y_s(+1))-A(+1);
	21	pie = exp(-rr/400)pie(+1)+alphaexp(-rr/400)dq(+1)-alphadq+(k/(tau+alpha(2-alpha)(1-tau)))y+alpha(2-alpha)(1-tau)/(tau(tau+alpha(2-alpha)(1-tau)))*y_s;
	22	pie = de+(1-alpha)*dq+pie_s;
	23	R = rho_RR(-1)+(1-rho_R)(psi1pie+psi2(y+alpha(2-alpha)((1-tau)/tau)y_s)+psi3de)+e_R;
	24	dq = rho_q*dq(-1)+e_q;
	25	y_s = rho_ys*y_s(-1)+e_ys;
	26	pie_s = rho_pies*pie_s(-1)+e_pies;
	27	A = rho_A*A(-1)+e_A;
	28	y_obs = y-y(-1)+A;
	29	pie_obs = 4*pie;
	30	R_obs = 4*R;
	31	end;
	32
	33	shocks;
	34	var e_R = 1.25^2;
	35	var e_q = 2.5^2;
	36	var e_A = 1.89;
	37	var e_ys = 1.89;
	38	var e_pies = 1.89;
	39	end;
	40
	41	varobs y_obs R_obs pie_obs dq de;
	42
	43	estimated_params;
	44	psi1 , gamma_pdf,1.5,0.5;
	45	psi2 , gamma_pdf,0.25,0.125;
	46	psi3 , gamma_pdf,0.25,0.125;
	47	rho_R ,beta_pdf,0.5,0.2;
	48	alpha ,beta_pdf,0.3,0.1;
	49	rr ,gamma_pdf,2.5,1;
	50	k , gamma_pdf,0.5,0.25;
	51	tau ,gamma_pdf,0.5,0.2;
	52	rho_q ,beta_pdf,0.4,0.2;
	53	rho_A ,beta_pdf,0.5,0.2;
	54	rho_ys ,beta_pdf,0.8,0.1;
	55	rho_pies,beta_pdf,0.7,0.15;
	56	stderr e_R,inv_gamma_pdf,1.2533,0.6551;
	57	stderr e_q,inv_gamma_pdf,2.5066,1.3103;
	58	stderr e_A,inv_gamma_pdf,1.2533,0.6551;
	59	stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
	60	stderr e_pies,inv_gamma_pdf,1.88,0.9827;
	61	end;
	62
	63	estimation(datafile=data_ca1,first_obs=8,nobs=[76 79],mh_nblocks=1,prefilter=1,mh_jscale=0.5,mh_replic=2000,forecast=8) y_obs R_obs pie_obs dq de;
	64

+39

-0

tests/run_test.m less more

	0	function run_test()
	1	test_files = {
	2	'.' 'ramst';
	3	'.' 'ramst_a';
	4	'.' 'example1';
	5	'.' 'example2';
	6	'.' 't_sgu_ex1';
	7	'arima' 'mod1';
	8	'arima' 'mod1a';
	9	'arima' 'mod1b';
	10	'arima' 'mod1c';
	11	'arima' 'mod2';
	12	'arima' 'mod2a';
	13	'arima' 'mod2b';
	14	'arima' 'mod2c';
	15	'fs2000' 'fs2000';
	16	'fs2000' 'fs2000a';
	17	}
	18
	19	results = cell(length(test_files),1);
	20
	21	for i=1:length(test_files)
	22	results{i}= run_test1(test_files{i,1},test_files{i,2});
	23	end
	24
	25	for i=1:length(test_files)
	26	disp(test_files{i,2})
	27	disp(results{i})
	28	end
	29	function msg=run_test1(path1,mod_file)
	30	global options_
	31	clear options_
	32	old_path = pwd;
	33	cd(path1);
	34	msg = 'OK';
	35	expr = ['disp(''error in ' mod_file ''');msg=lasterr;disp(msg)'];
	36	eval(['dynare ' mod_file ' noclearall'],'eval(expr)');
	37	cd(old_path)
	38

+202

-0

tests/run_test_matlab.m less more

	0	% Copyright (C) 2011 Dynare Team
	1	%
	2	% This file is part of Dynare.
	3	%
	4	% Dynare is free software: you can redistribute it and/or modify
	5	% it under the terms of the GNU General Public License as published by
	6	% the Free Software Foundation, either version 3 of the License, or
	7	% (at your option) any later version.
	8	%
	9	% Dynare is distributed in the hope that it will be useful,
	10	% but WITHOUT ANY WARRANTY; without even the implied warranty of
	11	% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	12	% GNU General Public License for more details.
	13	%
	14	% You should have received a copy of the GNU General Public License
	15	% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
	16
	17	% Implementation notes:
	18	%
	19	% Before every call to Dynare, the contents of the workspace is saved in
	20	% 'wsMat.mat', and reloaded after Dynare has finished (this is necessary since
	21	% Dynare does a 'clear -all'). Also note that we take care of clearing the
	22	% 'exception' variable in all 'catch' block, because otherwise the next 'load
	23	% wsMat' within a 'catch' block will overwrite the last exception.
	24
	25	top_test_dir = pwd;
	26	addpath(top_test_dir);
	27	addpath([top_test_dir '/../matlab']);
	28
	29	% Test Dynare Version
	30	if ~strcmp(dynare_version(), getenv('DYNARE_VERSION'))
	31	error('Incorrect version of Dynare is being tested')
	32	end
	33
	34	% Test MOD files listed in Makefile.am
	35	name=getenv('MODFILES');
	36	num_modfiles = 0;
	37
	38	failedBase = {};
	39
	40	while ~isempty(name)
	41	[modfile, name] = strtok(name);
	42	num_modfiles = num_modfiles + 1;
	43	[directory, testfile, ext] = fileparts([top_test_dir '/' modfile]);
	44	cd(directory);
	45	disp('');
	46	disp(['* TESTING: ' modfile ' *']);
	47	try
	48	old_path = path;
	49	save wsMat
	50	dynare([testfile ext],'console')
	51	clear -all
	52	load wsMat
	53	path(old_path);
	54	catch exception
	55	clear -all
	56	load wsMat
	57	path(old_path);
	58	failedBase{size(failedBase,2)+1} = modfile;
	59	printMakeCheckMatlabErrMsg(modfile, exception);
	60	clear exception
	61	end
	62	close all
	63	delete('wsMat.mat')
	64
	65	cd(top_test_dir);
	66	end
	67
	68	% Test block_bytecode/ls2003.mod with various combinations of
	69	% block/bytecode/solve_algo/stack_solve_algo
	70	failedBlock = {};
	71	num_block_tests = 0;
	72	cd([top_test_dir '/block_bytecode']);
	73	for blockFlag = 0:1
	74	for bytecodeFlag = 0:1
	75	default_solve_algo = 2;
	76	default_stack_solve_algo = 0;
	77	if ~blockFlag && ~bytecodeFlag
	78	solve_algos = 1:4;
	79	stack_solve_algos = 0;
	80	elseif blockFlag && ~bytecodeFlag
	81	solve_algos = [1:4 6:8];
	82	stack_solve_algos = 0:4;
	83	else
	84	solve_algos = 1:8;
	85	stack_solve_algos = 0:5;
	86	end
	87	if license('test', 'optimization_toolbox')
	88	solve_algos = [ solve_algos 0 ];
	89	end
	90
	91	for i = 1:length(solve_algos)
	92	num_block_tests = num_block_tests + 1;
	93	if ~blockFlag && ~bytecodeFlag && (i == 1)
	94	% This is the reference simulation path against which all
	95	% other simulations will be tested
	96	try
	97	old_path = path;
	98	save wsMat
	99	run_ls2003(blockFlag, bytecodeFlag, solve_algos(i), default_stack_solve_algo)
	100	load wsMat
	101	path(old_path);
	102	y_ref = oo_.endo_simul;
	103	save('test.mat','y_ref');
	104	catch exception
	105	load wsMat
	106	path(old_path);
	107	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'];
	108	printMakeCheckMatlabErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'], exception);
	109	clear exception
	110	end
	111	else
	112	try
	113	old_path = path;
	114	save wsMat
	115	run_ls2003(blockFlag, bytecodeFlag, solve_algos(i), default_stack_solve_algo)
	116	load wsMat
	117	path(old_path);
	118	% Test against the reference simulation path
	119	load('test.mat','y_ref');
	120	diff = oo_.endo_simul - y_ref;
	121	if(abs(diff) > options_.dynatol)
	122	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'];
	123	exception = MException('ERROR: simulation path differs from the reference path');
	124	printMakeCheckMatlabErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'], exception);
	125	clear exception
	126	end
	127	catch exception
	128	load wsMat
	129	path(old_path);
	130	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'];
	131	printMakeCheckMatlabErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'], exception);
	132	clear exception
	133	end
	134	end
	135	end
	136	for i = 1:length(stack_solve_algos)
	137	num_block_tests = num_block_tests + 1;
	138	try
	139	old_path = path;
	140	save wsMat
	141	run_ls2003(blockFlag, bytecodeFlag, default_solve_algo, stack_solve_algos(i))
	142	load wsMat
	143	path(old_path);
	144	% Test against the reference simulation path
	145	load('test.mat','y_ref');
	146	diff = oo_.endo_simul - y_ref;
	147	if(abs(diff) > options_.dynatol)
	148	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(default_solve_algo) ', ' num2str(stack_solve_algos(i)) ')'];
	149	exception = MException('ERROR: simulation path difers from the reference path');
	150	printMakeCheckMatlabErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(default_solve_algo) ', ' num2str(stack_solve_algos(i)) ')'], exception);
	151	clear exception
	152	end
	153	catch exception
	154	load wsMat
	155	path(old_path);
	156	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'];
	157	printMakeCheckMatlabErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'], exception);
	158	clear exception
	159	end
	160	end
	161	end
	162	end
	163	delete('wsMat.mat')
	164
	165	cd(top_test_dir);
	166
	167	total_tests = num_modfiles+num_block_tests;
	168
	169	% print output to screen and to file
	170	fid = fopen('run_test_matlab_output.txt', 'w');
	171
	172	fprintf('\n\n\n');
	173	fprintf(fid,'\n\n\n');
	174	disp('***************************************');
	175	fprintf(fid,'***************************************\n');
	176	disp('* DYNARE TEST RESULTS *');
	177	fprintf(fid,'* DYNARE TEST RESULTS *\n');
	178	disp('* for make check-matlab *');
	179	fprintf(fid,'* for make check-matlab *\n');
	180	disp('***************************************');
	181	fprintf(fid,'***************************************\n');
	182	disp([' ' num2str(total_tests-size(failedBase,2)-size(failedBlock,2)) ' tests PASSED out of ' num2str(total_tests) ' tests run']);
	183	fprintf(fid,' %d tests PASSED out of %d tests run\n', total_tests-size(failedBase,2)-size(failedBlock,2), total_tests);
	184	disp('***************************************');
	185	fprintf(fid,'***************************************\n');
	186	if size(failedBase,2) > 0 \|\| size(failedBlock,2) > 0
	187	disp(['List of ' num2str(size(failedBase,2)+size(failedBlock,2)) ' tests FAILED:']);
	188	fprintf(fid,'List of %d tests FAILED:\n', size(failedBase,2)+size(failedBlock,2));
	189	for i=1:size(failedBase,2)
	190	disp([' * ' failedBase{i}]);
	191	fprintf(fid,' * %s\n', failedBase{i});
	192	end
	193	for i=1:size(failedBlock,2)
	194	disp([' * ' failedBlock{i}]);
	195	fprintf(fid,' * %s\n', failedBlock{i});
	196	end
	197	fprintf('***************************************\n\n');
	198	fprintf(fid,'***************************************\n\n');
	199	end
	200	fclose(fid);
	201	exit;

+169

-13

tests/run_test_octave.m less more

0		## Copyright (C) 2009 Dynare Team
	0	## Copyright (C) 2009-2011 Dynare Team
1	1	##
2	2	## This file is part of Dynare.
3	3	##

14	14	## You should have received a copy of the GNU General Public License
15	15	## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
16	16
17		## First argument is MOD file (possibly with a path prefix)
18		## Second argument is path to Dynare installation to be checked
19		## Third argument is Dynare version to be checked
	17	## Implementation notes:
	18	##
	19	## Before every call to Dynare, the contents of the workspace is saved in
	20	## 'wsOct', and reloaded after Dynare has finished (this is necessary since
	21	## Dynare does a 'clear -all').
	22
	23	top_test_dir = pwd;
	24	addpath(top_test_dir);
	25	addpath([top_test_dir '/../matlab']);
	26
	27	## Test Dynare Version
	28	if !strcmp(dynare_version(), getenv("DYNARE_VERSION"))
	29	error("Incorrect version of Dynare is being tested")
	30	endif
20	31
21	32	## Ask gnuplot to create graphics in text mode
22	33	## Note that setenv() was introduced in Octave 3.0.2, for compatibility
23	34	## with MATLAB
24	35	putenv("GNUTERM", "dumb")
25	36
26		[directory, name, ext] = fileparts(argv(){1});
	37	## Test MOD files listed in Makefile.am
	38	name = strsplit(getenv("MODFILES"), " ");
27	39
28		printf("TEST: %s...\n", name)
	40	failedBase = {};
29	41
30		addpath(argv(){2})
	42	for i=1:size(name,2)
	43	[directory, testfile, ext] = fileparts([top_test_dir '/' name{i}]);
	44	cd(directory);
	45	printf("\n* TESTING: %s *\n", name{i});
	46	try
	47	old_path = path;
	48	save wsOct
	49	dynare([testfile ext])
	50	clear -all
	51	load wsOct
	52	path(old_path);
	53	catch
	54	clear -all
	55	load wsOct
	56	path(old_path);
	57	failedBase{size(failedBase,2)+1} = name{i};
	58	printMakeCheckOctaveErrMsg(name{i}, lasterror);
	59	end_try_catch
	60	delete('wsOct');
	61	cd(top_test_dir);
	62	end
31	63
32		if !strcmp(dynare_version(), argv(){3})
33		error("Incorrect version of Dynare is being tested")
	64	## Test block_bytecode/ls2003.mod with various combinations of
	65	## block/bytecode/solve_algo/stack_solve_algo
	66	failedBlock = {};
	67	num_block_tests = 0;
	68	cd([top_test_dir '/block_bytecode']);
	69	for blockFlag = 0:1
	70	for bytecodeFlag = 0:1
	71	## Recall that solve_algo=7 and stack_solve_algo=2 are not supported
	72	## under Octave
	73	default_solve_algo = 2;
	74	default_stack_solve_algo = 0;
	75	if !blockFlag && !bytecodeFlag
	76	solve_algos = 0:4;
	77	stack_solve_algos = 0;
	78	elseif blockFlag && !bytecodeFlag
	79	solve_algos = [0:4 6 8];
	80	stack_solve_algos = [0 1 3 4];
	81	else
	82	solve_algos = [0:6 8];
	83	stack_solve_algos = [0 1 3:5];
	84	endif
	85
	86	for i = 1:length(solve_algos)
	87	num_block_tests = num_block_tests + 1;
	88	if !blockFlag && !bytecodeFlag && (i == 1)
	89	## This is the reference simulation path against which all
	90	## other simulations will be tested
	91	try
	92	old_path = path;
	93	save wsOct
	94	run_ls2003(blockFlag, bytecodeFlag, solve_algos(i), default_stack_solve_algo)
	95	load wsOct
	96	path(old_path);
	97	y_ref = oo_.endo_simul;
	98	save('test.mat','y_ref');
	99	catch
	100	load wsOct
	101	path(old_path);
	102	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'];
	103	printMakeCheckOctaveErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'], lasterror);
	104	end_try_catch
	105	else
	106	try
	107	old_path = path;
	108	save wsOct
	109	run_ls2003(blockFlag, bytecodeFlag, solve_algos(i), default_stack_solve_algo)
	110	load wsOct
	111	path(old_path);
	112	## Test against the reference simulation path
	113	load('test.mat','y_ref');
	114	diff = oo_.endo_simul - y_ref;
	115	if(abs(diff) > options_.dynatol)
	116	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'];
	117	differr.message = ["ERROR: simulation path differs from the reference path" ];
	118	printMakeCheckOctaveErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'], differr);
	119	endif
	120	catch
	121	load wsOct
	122	path(old_path);
	123	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'];
	124	printMakeCheckOctaveErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'], lasterror);
	125	end_try_catch
	126	endif
	127	endfor
	128	for i = 1:length(stack_solve_algos)
	129	num_block_tests = num_block_tests + 1;
	130	try
	131	old_path = path;
	132	save wsOct
	133	run_ls2003(blockFlag, bytecodeFlag, default_solve_algo, stack_solve_algos(i))
	134	load wsOct
	135	path(old_path);
	136	## Test against the reference simulation path
	137	load('test.mat','y_ref');
	138	diff = oo_.endo_simul - y_ref;
	139	if(abs(diff) > options_.dynatol)
	140	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(default_solve_algo) ', ' num2str(stack_solve_algos(i)) ')'];
	141	differr.message = ["ERROR: simulation path differs from the reference path" ];
	142	printMakeCheckOctaveErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(default_solve_algo) ', ' num2str(stack_solve_algos(i)) ')'], differr);
	143	endif
	144	catch
	145	load wsOct
	146	path(old_path);
	147	failedBlock{size(failedBlock,2)+1} = ['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'];
	148	printMakeCheckOctaveErrMsg(['block_bytecode/run_ls2003.m(' num2str(blockFlag) ', ' num2str(bytecodeFlag) ', ' num2str(solve_algos(i)) ', ' num2str(default_stack_solve_algo) ')'], lasterror);
	149	end_try_catch
	150	endfor
	151	endfor
	152	endfor
	153
	154	delete('wsOct');
	155
	156	cd(top_test_dir);
	157
	158	total_tests = size(name,2)+num_block_tests;
	159
	160	% print output to screen and to file
	161	fid = fopen("run_test_octave_output.txt", "w");
	162
	163	printf("\n\n\n");
	164	fprintf(fid,'\n\n\n');
	165	printf("***************************************\n");
	166	fprintf(fid,"***************************************\n");
	167	printf("* DYNARE TEST RESULTS *\n");
	168	fprintf(fid,"* DYNARE TEST RESULTS *\n");
	169	printf("* for make check-octave *\n");
	170	fprintf(fid,"* for make check-octave *\n");
	171	printf("***************************************\n");
	172	fprintf(fid,"***************************************\n");
	173	printf(" %d tests PASSED out of %d tests run\n", total_tests-size(failedBase,2)-size(failedBlock,2), total_tests);
	174	fprintf(fid," %d tests PASSED out of %d tests run\n", total_tests-size(failedBase,2)-size(failedBlock,2), total_tests);
	175	printf("***************************************\n");
	176	fprintf(fid,"***************************************\n");
	177	if size(failedBase,2) > 0 \|\| size(failedBlock,2) > 0
	178	printf("List of %d tests FAILED:\n", size(failedBase,2)+size(failedBlock,2));
	179	fprintf(fid,"List of %d tests FAILED:\n", size(failedBase,2)+size(failedBlock,2));
	180	for i=1:size(failedBase,2)
	181	printf(" * %s\n",failedBase{i});
	182	fprintf(fid," * %s\n", failedBase{i});
	183	end
	184	for i=1:size(failedBlock,2)
	185	printf(" * %s\n",failedBlock{i});
	186	fprintf(fid," * %s\n", failedBlock{i});
	187	end
	188	printf("***************************************\n\n");
	189	fprintf(fid,"***************************************\n\n");
	190	fclose(fid);
	191	error("make check-octave FAILED");
	192	else
	193	fclose(fid);
34	194	endif
35
36		cd(directory)
37
38		dynare(name)
39	195
40	196	## Local variables:
41	197	## mode: Octave

+49

-0

tests/second_order/burnside_1.mod less more

	0	var y x;
	1	varexo e;
	2
	3	parameters beta theta rho xbar;
	4	xbar = 0.0179;
	5	rho = -0.139;
	6	theta = -1.5;
	7	theta = -10;
	8	beta = 0.95;
	9
	10	model;
	11	y = betaexp(thetax(+1))*(1+y(+1));
	12	x = (1-rho)xbar + rhox(-1)+e;
	13	end;
	14
	15	shocks;
	16	var e; stderr 0.0348;
	17	end;
	18
	19	initval;
	20	x = xbar;
	21	y = betaexp(thetaxbar)/(1-betaexp(thetaxbar));
	22	end;
	23
	24	resid(1);
	25
	26	steady;
	27
	28	stoch_simul(order=2,irf=0);
	29
	30	sigma2=M_.Sigma_e;
	31	i = linspace(1,800,800);
	32	a = thetaxbari+(theta^2sigma2)/(2(1-rho)^2)(i-2rho(1-rho.^i)/(1-rho)+rho^2(1-rho.^(2*i))/(1-rho^2));
	33	a1 = theta^2sigma2/(2(1-rho)^2)(i-2rho(1-rho.^i)/(1-rho)+rho^2(1-rho.^(2*i))/(1-rho^2));
	34	b = thetarho(1-rho.^i)/(1-rho);
	35
	36	dr = oo_.dr;
	37	x1 = [dr.ghx(2); dr.ghu(2); dr.ghxx(2); dr.ghuu(2); dr.ghxu(2); dr.ghs2(2)];
	38	x2 = [
	39	sum(beta.^i.exp(thetaxbari).b*rho)
	40	sum(beta.^i.exp(thetaxbari).b)
	41	sum(beta.^i.exp(thetaxbari).(b*rho).^2)
	42	sum(beta.^i.exp(thetaxbari).b.^2)
	43	sum(beta.^i.exp(thetaxbari).b.^2*rho)
	44	sum(beta.^i.exp(thetaxbari).a1*2)
	45	];
	46	if any(abs(x1-x2) > 1e-14);
	47	error('burnside_1 doesn''t reproduce the analytical solution');
	48	end;⏎

+69

-0

tests/second_order/ds1.mod less more

	0	@#define countries = 1:2
	1	@#define assets = 1:2
	2	var
	3	@#for c in countries
	4	yk_@{c} yl_@{c} c_@{c}
	5	@#endfor
	6	a ze_1 r_1
	7	;
	8
	9	varexo
	10	@#for c in countries
	11	ek_@{c} el_@{c}
	12	@#endfor
	13	;
	14
	15	parameters rho eta omega sig_k sig_l ykbar ylbar;
	16
	17	rho = 1;
	18	psi = 0.7;
	19	eta = 0.9;
	20	g = 0.25;
	21	ykbar = 1;
	22	ylbar = 1;
	23	cbar = ykbar+ylbar;
	24	nu = 0.5;
	25	omega = 0.75;
	26	sig_k = 0.02;
	27	sig_l = 0.01;
	28	sig_m = 0;
	29	betabar = omega*cbar^(-eta);
	30
	31
	32	model;
	33	@#for c in countries
	34	yk_@{c} = log(ykbar) + sig_k*ek_@{c};
	35	yl_@{c} = log(ylbar) + sig_l*el_@{c};
	36	exp(c_@{c})^(eta-rho) = omegaexp(c_@{c}(+1))^(-rho)r_1(+1);
	37	@#endfor
	38
	39	a = a(-1)*r_1 + exp(yk_1) + exp(yl_1) - exp(c_1);
	40	-a = -a(-1)*r_1 + exp(yk_2) + exp(yl_2) - exp(c_2);
	41	// r_1+ze_1 = ze_1+exp(yk_1-ze_1(-1));
	42	r_1 = exp(yk_1-ze_1(-1));
	43
	44
	45	end;
	46
	47	shocks;
	48	@#for c in countries
	49	var ek_@{c} = 1;
	50	var el_@{c} = 1;
	51	@#endfor
	52	end;
	53
	54	initval;
	55	@#for c in countries
	56	yk_@{c} = log(ykbar);
	57	yl_@{c} = log(ylbar);
	58	c_@{c} = log(ykbar+ylbar);
	59	@#endfor
	60	r_1 = 1/betabar;
	61	ze_1 = log(betabar)+yk_1;
	62	end;
	63
	64	resid(1);
	65	steady;
	66	model_diagnostics(M_,options_,oo_);
	67	check;
	68	stoch_simul(order=2,irf=0);

+77

-0

tests/second_order/ds2.mod less more

	0	@#define countries = 1:2
	1	@#define assets = 1:2
	2	var
	3	@#for c in countries
	4	yk_@{c} yl_@{c} c_@{c}
	5	@#endfor
	6	a ze_1 r_1
	7	;
	8
	9	varexo
	10	@#for c in countries
	11	ek_@{c} el_@{c}
	12	@#endfor
	13	;
	14
	15	parameters rho eta omega sig_k sig_l ykbar ylbar;
	16
	17	rho = 1;
	18	psi = 0.7;
	19	eta = 0.9;
	20	g = 0.25;
	21	ykbar = 1;
	22	ylbar = 1;
	23	cbar = ykbar+ylbar;
	24	nu = 0.5;
	25	omega = 0.75;
	26	sig_k = 0.02;
	27	sig_l = 0.01;
	28	sig_m = 0;
	29	betabar = omega*cbar^(-eta);
	30
	31
	32	model;
	33	@#for c in countries
	34	yk_@{c} = log(ykbar) + sig_k*ek_@{c};
	35	yl_@{c} = log(ylbar) + sig_l*el_@{c};
	36	exp(c_@{c})^(eta-rho) = omegaexp(c_@{c}(+1))^(-rho)r_1(+1);
	37	@#endfor
	38
	39	a = a(-1)*r_1 + exp(yk_1) + exp(yl_1) - exp(c_1);
	40	-a = -a(-1)*r_1 + exp(yk_2) + exp(yl_2) - exp(c_2);
	41	// r_1+ze_1 = ze_1+exp(yk_1-ze_1(-1));
	42	r_1 = exp(yk_1-ze_1(-1))+ze_1-ze_1;
	43
	44
	45	end;
	46
	47	shocks;
	48	@#for c in countries
	49	var ek_@{c} = 1;
	50	var el_@{c} = 1;
	51	@#endfor
	52	end;
	53
	54	initval;
	55	@#for c in countries
	56	yk_@{c} = log(ykbar);
	57	yl_@{c} = log(ylbar);
	58	c_@{c} = log(ykbar+ylbar);
	59	@#endfor
	60	r_1 = 1/betabar;
	61	ze_1 = log(betabar)+yk_1;
	62	end;
	63
	64	resid(1);
	65	steady;
	66	model_diagnostics(M_,options_,oo_);
	67	check;
	68	stoch_simul(order=2,irf=0);
	69
	70	o1 = load('ds1_results','oo_');
	71	oo1 = o1.oo_.dr;
	72	oo2 = oo_.dr;
	73	if any(abs(oo1.ghxx-oo2.ghxx) > 1e-14);error('ds1 with missing variable doesn''t reproduce ds2');end;
	74	if any(abs(oo1.ghuu-oo2.ghuu) > 1e-14);error('ds1 with missing variable doesn''t reproduce ds2');end;
	75	if any(abs(oo1.ghxu-oo2.ghxu) > 1e-14);error('ds1 with missing variable doesn''t reproduce ds2');end;
	76	if any(abs(oo1.ghs2-oo2.ghs2) > 1e-14);error('ds1 with missing variable doesn''t reproduce ds2');end;⏎

+27

-0

tests/steady_state/multi_leads.mod less more

	0	@#define N = 4
	1	var A B;
	2	varexo eB;
	3	parameters Bss rho;
	4
	5	Bss=1;
	6	rho=0.9;
	7
	8	model;
	9	A = B(@{N});
	10	B = (1-rho)Bss +rhoB(-1) +eB;
	11	end;
	12
	13	steady_state_model;
	14	A = Bss;
	15	B = Bss;
	16	end;
	17
	18	resid;
	19	steady;
	20	check;
	21
	22	shocks;
	23	var eB ; stderr 1 ;
	24	end;
	25
	26	stoch_simul(order=1,irf=0) ;

+68

-0

tests/steady_state/walsh1_initval.mod less more

	0	var y c k m n R pi z u;
	1	varexo e sigma;
	2	// sigma stands for phi in the eq 2.37 p.69
	3
	4	parameters alpha beta delta gamm phi1 eta a b rho phi2 Psi thetass;
	5	//phi1 stands for capital phi in eq.2.68 and 2.69
	6	//phi2 stands for lowercase phi in eq. 2.66
	7
	8	change_type(var) Psi;
	9	change_type(parameters) n;
	10
	11	alpha = 0.36;
	12	beta = 0.989;
	13	gamm = 0.5;
	14	delta = 0.019;
	15	phi1 = 2;
	16	phi2 = 0;
	17	eta = 1;
	18	a = 0.95;
	19	b = 2.56;
	20	rho = 0.95;
	21	//Psi = 1.47630583;
	22	thetass = 1.0125;
	23
	24	model;
	25
	26	(aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))aexp(c)^(-b) = (aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))(1-a)exp(m)^(-b)+beta(aexp(c(+1))^(1-b)+(1-a)exp(m(+1))^(1-b))^((b-phi1)/(1-b))a*exp(c(+1))^(-b)/(1+pi(+1));
	27
	28	Psi(1-exp(n))^(-eta)/(aexp(c)^(-b)(aexp(c)^(1-b) + (1-a)exp(m)^(1-b))^((b-phi1)/(1-b))) = (1-alpha)exp(y)/exp(n);
	29
	30	(aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))aexp(c)^(-b) = betaexp(R(+1))(aexp(c(+1))^(1-b)+(1-a)exp(m(+1))^(1-b))^((b-phi1)/(1-b))aexp(c(+1))^(-b);
	31
	32	exp(R) = alpha*exp(y)/exp(k(-1)) + 1-delta;
	33
	34	exp(k) = (1-delta)*exp(k(-1))+exp(y)-exp(c);
	35
	36	exp(y) = exp(z)exp(k(-1))^alphaexp(n)^(1-alpha);
	37
	38	exp(m) = exp(m(-1))*(u+thetass)/(1+pi);
	39
	40	z = rho*z(-1) + e;
	41
	42	u = gammu(-1) + phi2z(-1) + sigma;
	43
	44	end;
	45
	46	shocks;
	47	var e; stderr 0.007;
	48	var sigma;stderr 0.0089;
	49	end;
	50
	51	n=log(1/3);
	52
	53	initval;
	54	y = 0.2;
	55	c = 0.03;
	56	k = 2.7;
	57	m = 0.3;
	58	Psi = 1.5;
	59	R = 0.01;
	60	pi = 0.01;
	61	z = 0;
	62	u = 0;
	63	end;
	64
	65
	66	steady;
	67

+51

-0

tests/steady_state/walsh1_old_ss.mod less more

	0	var y c k m n R pi z u;
	1	varexo e sigma;
	2	// sigma stands for phi in the eq 2.37 p.69
	3
	4	parameters alpha beta delta gamm phi1 eta a b rho phi2 Psi thetass;
	5	//phi1 stands for capital phi in eq.2.68 and 2.69
	6	//phi2 stands for lowercase phi in eq. 2.66
	7
	8	alpha = 0.36;
	9	beta = 0.989;
	10	gamm = 0.5;
	11	delta = 0.019;
	12	phi1 = 2;
	13	phi2 = 0;
	14	eta = 1;
	15	a = 0.95;
	16	b = 2.56;
	17	rho = 0.95;
	18	Psi = 1.47630583;
	19	thetass = 1.0125;
	20
	21	model;
	22
	23	(aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))aexp(c)^(-b) = (aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))(1-a)exp(m)^(-b)+beta(aexp(c(+1))^(1-b)+(1-a)exp(m(+1))^(1-b))^((b-phi1)/(1-b))a*exp(c(+1))^(-b)/(1+pi(+1));
	24
	25	Psi(1-exp(n))^(-eta)/(aexp(c)^(-b)(aexp(c)^(1-b) + (1-a)exp(m)^(1-b))^((b-phi1)/(1-b))) = (1-alpha)exp(y)/exp(n);
	26
	27	(aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))aexp(c)^(-b) = betaexp(R(+1))(aexp(c(+1))^(1-b)+(1-a)exp(m(+1))^(1-b))^((b-phi1)/(1-b))aexp(c(+1))^(-b);
	28
	29	exp(R) = alpha*exp(y)/exp(k(-1)) + 1-delta;
	30
	31	exp(k) = (1-delta)*exp(k(-1))+exp(y)-exp(c);
	32
	33	exp(y) = exp(z)exp(k(-1))^alphaexp(n)^(1-alpha);
	34
	35	exp(m) = exp(m(-1))*(u+thetass)/(1+pi);
	36
	37	z = rho*z(-1) + e;
	38
	39	u = gammu(-1) + phi2z(-1) + sigma;
	40
	41	end;
	42
	43	shocks;
	44	var e; stderr 0.007;
	45	var sigma;stderr 0.0089;
	46	end;
	47
	48
	49	steady;
	50

+41

-0

tests/steady_state/walsh1_old_ss_steadystate.m less more

	0	function [ys,check] = wlash1_old_ss_steadystate(ys0,exo)
	1	global M_
	2
	3	check = 0;
	4
	5	params = M_.params;
	6	alpha = params(1);
	7	beta = params(2);
	8	delta = params(3);
	9	gamm = params(4);
	10	phi1 = params(5);
	11	eta = params(6);
	12	a = params(7);
	13	b = params(8);
	14	rho = params(9);
	15	phi2 = params(10);
	16	Psi = params(11);
	17	thetass = params(12);
	18
	19	pi = thetass-1;
	20	en = 1/3;
	21	eR = 1/beta;
	22	y_k = (1/alpha)*(1/beta-1+delta);
	23	ek = en*y_k^(-1/(1-alpha));
	24	ec = ek*(y_k-delta);
	25	em = ec(a/(1-a))^(-1/b)((thetass-beta)/thetass)^(-1/b);
	26	ey = ek*y_k;
	27	Xss = aec^(1-b)(1+(a/(1-a))^(-1/b)*((thetass-beta)/thetass)^((b-1)/b));
	28	Psi = (1-alpha)(ey/en)Xss^((b-phi1)/(1-b))aec^(-b)*(1-en)^eta;
	29	n = log(en);
	30	k = log(ek);
	31	m = log(em);
	32	c = log(ec);
	33	y = log(ey);
	34	R = log(eR);
	35	z = 0;
	36	u = 0;
	37
	38	ys = [y c k m n R pi z u]';
	39	M_.params(11) = Psi;
	40

+72

-0

tests/steady_state/walsh1_ssm.mod less more

	0	var y c k m n R pi z u;
	1	varexo e sigma;
	2	// sigma stands for phi in the eq 2.37 p.69
	3
	4	parameters alpha beta delta gamm phi1 eta a b rho phi2 Psi thetass;
	5	//phi1 stands for capital phi in eq.2.68 and 2.69
	6	//phi2 stands for lowercase phi in eq. 2.66
	7
	8	alpha = 0.36;
	9	beta = 0.989;
	10	gamm = 0.5;
	11	delta = 0.019;
	12	phi1 = 2;
	13	phi2 = 0;
	14	eta = 1;
	15	a = 0.95;
	16	b = 2.56;
	17	rho = 0.95;
	18	Psi = 1.47630583;
	19	thetass = 1.0125;
	20
	21	model;
	22
	23	(aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))aexp(c)^(-b) = (aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))(1-a)exp(m)^(-b)+beta(aexp(c(+1))^(1-b)+(1-a)exp(m(+1))^(1-b))^((b-phi1)/(1-b))a*exp(c(+1))^(-b)/(1+pi(+1));
	24
	25	Psi(1-exp(n))^(-eta)/(aexp(c)^(-b)(aexp(c)^(1-b) + (1-a)exp(m)^(1-b))^((b-phi1)/(1-b))) = (1-alpha)exp(y)/exp(n);
	26
	27	(aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))aexp(c)^(-b) = betaexp(R(+1))(aexp(c(+1))^(1-b)+(1-a)exp(m(+1))^(1-b))^((b-phi1)/(1-b))aexp(c(+1))^(-b);
	28
	29	exp(R) = alpha*exp(y)/exp(k(-1)) + 1-delta;
	30
	31	exp(k) = (1-delta)*exp(k(-1))+exp(y)-exp(c);
	32
	33	exp(y) = exp(z)exp(k(-1))^alphaexp(n)^(1-alpha);
	34
	35	exp(m) = exp(m(-1))*(u+thetass)/(1+pi);
	36
	37	z = rho*z(-1) + e;
	38
	39	u = gammu(-1) + phi2z(-1) + sigma;
	40
	41	end;
	42
	43	shocks;
	44	var e; stderr 0.007;
	45	var sigma;stderr 0.0089;
	46	end;
	47
	48	steady_state_model;
	49	// solving in levels
	50	// calibrating n = 1/3 and recovering the value of Psi
	51	// adapting solution Walsh (2003) p. 84
	52	pi = thetass-1;
	53	en = 1/3;
	54	eR = 1/beta;
	55	y_k = (1/alpha)*(1/beta-1+delta);
	56	ek = en*y_k^(-1/(1-alpha));
	57	ec = ek*(y_k-delta);
	58	em = ec(a/(1-a))^(-1/b)((thetass-beta)/thetass)^(-1/b);
	59	ey = ek*y_k;
	60	Xss = aec^(1-b)(1+(a/(1-a))^(-1/b)*((thetass-beta)/thetass)^((b-1)/b));
	61	Psi = (1-alpha)(ey/en)Xss^((b-phi1)/(1-b))aec^(-b)*(1-en)^eta;
	62	n = log(en);
	63	k = log(ek);
	64	m = log(em);
	65	c = log(ec);
	66	y = log(ey);
	67	R = log(eR);
	68	end;
	69
	70	steady;
	71

+72

-0

tests/steady_state/walsh1_ssm_block.mod less more

	0	var y c k m n R pi z u;
	1	varexo e sigma;
	2	// sigma stands for phi in the eq 2.37 p.69
	3
	4	parameters alpha beta delta gamm phi1 eta a b rho phi2 Psi thetass;
	5	//phi1 stands for capital phi in eq.2.68 and 2.69
	6	//phi2 stands for lowercase phi in eq. 2.66
	7
	8	alpha = 0.36;
	9	beta = 0.989;
	10	gamm = 0.5;
	11	delta = 0.019;
	12	phi1 = 2;
	13	phi2 = 0;
	14	eta = 1;
	15	a = 0.95;
	16	b = 2.56;
	17	rho = 0.95;
	18	Psi = 1.47630583;
	19	thetass = 1.0125;
	20
	21	model(block);
	22
	23	(aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))aexp(c)^(-b) = (aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))(1-a)exp(m)^(-b)+beta(aexp(c(+1))^(1-b)+(1-a)exp(m(+1))^(1-b))^((b-phi1)/(1-b))a*exp(c(+1))^(-b)/(1+pi(+1));
	24
	25	Psi(1-exp(n))^(-eta)/(aexp(c)^(-b)(aexp(c)^(1-b) + (1-a)exp(m)^(1-b))^((b-phi1)/(1-b))) = (1-alpha)exp(y)/exp(n);
	26
	27	(aexp(c)^(1-b)+(1-a)exp(m)^(1-b))^((b-phi1)/(1-b))aexp(c)^(-b) = betaexp(R(+1))(aexp(c(+1))^(1-b)+(1-a)exp(m(+1))^(1-b))^((b-phi1)/(1-b))aexp(c(+1))^(-b);
	28
	29	exp(R) = alpha*exp(y)/exp(k(-1)) + 1-delta;
	30
	31	exp(k) = (1-delta)*exp(k(-1))+exp(y)-exp(c);
	32
	33	exp(y) = exp(z)exp(k(-1))^alphaexp(n)^(1-alpha);
	34
	35	exp(m) = exp(m(-1))*(u+thetass)/(1+pi);
	36
	37	z = rho*z(-1) + e;
	38
	39	u = gammu(-1) + phi2z(-1) + sigma;
	40
	41	end;
	42
	43	shocks;
	44	var e; stderr 0.007;
	45	var sigma;stderr 0.0089;
	46	end;
	47
	48	steady_state_model;
	49	// solving in levels
	50	// calibrating n = 1/3 and recovering the value of Psi
	51	// adapting solution Walsh (2003) p. 84
	52	pi = thetass-1;
	53	en = 1/3;
	54	eR = 1/beta;
	55	y_k = (1/alpha)*(1/beta-1+delta);
	56	ek = en*y_k^(-1/(1-alpha));
	57	ec = ek*(y_k-delta);
	58	em = ec(a/(1-a))^(-1/b)((thetass-beta)/thetass)^(-1/b);
	59	ey = ek*y_k;
	60	Xss = aec^(1-b)(1+(a/(1-a))^(-1/b)*((thetass-beta)/thetass)^((b-1)/b));
	61	Psi = (1-alpha)(ey/en)Xss^((b-phi1)/(1-b))aec^(-b)*(1-en)^eta;
	62	n = log(en);
	63	k = log(ek);
	64	m = log(em);
	65	c = log(ec);
	66	y = log(ey);
	67	R = log(eR);
	68	end;
	69
	70	steady;
	71

+2

-1

windows/dynare.nsi less more

51	51	SectionIn RO
52	52	!insertmacro DETERMINE_CONTEXT
53	53	SetOutPath $INSTDIR
54		File README.txt mexopts-win32.bat mexopts-win64.bat ..\license.txt ..\dynare.el
	54	File README.txt ..\NEWS mexopts-win32.bat mexopts-win64.bat ..\license.txt ..\dynare.el
55	55
56	56	SetOutPath $INSTDIR\matlab
57	57	File /r ..\matlab\*.m

142	142	# First delete the uninstaller
143	143	Delete $INSTDIR\uninstall.exe
144	144	Delete $INSTDIR\README.txt
	145	Delete $INSTDIR\NEWS
145	146	Delete $INSTDIR\license.txt
146	147	Delete $INSTDIR\mexopts-win32.bat
147	148	Delete $INSTDIR\mexopts-win64.bat