Merge remote-tracking branch 'origin/master' into dr1break

configure.ac

@@ -1,6 +1,6 @@
 dnl Process this file with autoconf to produce a configure script.
 
-dnl Copyright (C) 2009-2011 Dynare Team
+dnl Copyright (C) 2009-2012 Dynare Team
 dnl
 dnl This file is part of Dynare.
 dnl
@@ -30,7 +30,7 @@ case ${host_os} in
   *mingw32*)
     # On mingw32, we don't want dynamic libgcc
     # Note that static-libstdc++ is only supported since GCC 4.5 (but generates no error on older versions)
-    LDFLAGS="$LDFLAGS -static-libgcc -static-libstdc++"
+    LDFLAGS="$LDFLAGS -static-libgcc -static-libstdc++ -static-libgfortran"
     have_windows="yes"
     ;;
   *cygwin*)
@@ -155,6 +155,15 @@ AC_CHECK_PROG([CWEAVE], [cweave], [cweave])
 AM_CONDITIONAL([HAVE_CWEAVE], [test "x$CWEAVE" != "x"])
 
 AC_PROG_F77
+AC_F77_LIBRARY_LDFLAGS
+# Hack to get static linking of libgfortran on MinGW
+# (-static-libgfortran doesn't act on gcc/g++)
+case ${host_os} in
+  *mingw32*)
+    FLIBS=`echo $FLIBS | sed 's/-lgfortran/-Wl,-Bstatic -lgfortran -Wl,-Bdynamic/'`
+    ;;
+esac
+
 if test "x$F77" != "x"; then
    AX_BLAS
    AX_LAPACK

doc/dynare.texi

@@ -295,15 +295,15 @@ Stéphane Adjemian (Université du Maine, Gains and Cepremap), Houtan
 Bastani (Cepremap), Michel Juillard (Banque de France), Frédéric Karamé
 (Université d'Évry, Epee and Cepremap), Junior Maih (Norges Bank),
 Ferhat Mihoubi (Université d'Évry, Epee and Cepremap), George Perendia,
-Marco Ratto (JRC) and Sébastien Villemot (Cepremap and Paris School of
+Johannes Pfeifer, Marco Ratto (JRC) and Sébastien Villemot (Cepremap and
-Economics). Increasingly, the developer base is expanding, as tools
+Paris School of Economics). Increasingly, the developer base is
-developed by researchers outside of Cepremap are integrated into
+expanding, as tools developed by researchers outside of Cepremap are
-Dynare. Financial support is provided by Cepremap, Banque de France and
+integrated into Dynare. Financial support is provided by Cepremap,
-DSGE-net (an international research network for DSGE modeling). The
+Banque de France and DSGE-net (an international research network for
-Dynare project also received funding through the Seventh Framework
+DSGE modeling). The Dynare project also received funding through the
-Programme for Research (FP7) of the European Commission's Socio-economic
+Seventh Framework Programme for Research (FP7) of the European
-Sciences and Humanities (SSH) Program from October 2008 to September
+Commission's Socio-economic Sciences and Humanities (SSH) Program from
-2011 under grant agreement SSH-CT-2009-225149.
+October 2008 to September 2011 under grant agreement SSH-CT-2009-225149.
 
 Interaction between developers and users of Dynare is central to the
 project. A @uref{http://www.dynare.org/phpBB3, web forum} is available
@@ -3888,6 +3888,9 @@ Uses Dynare implementation of the Nelder-Mead simplex based optimization
 routine (generally more efficient than the MATLAB or Octave implementation
 available with @code{mode_compute=7})
 
+@item 9
+Uses the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, an evolutionary algorithm for difficult non-linear non-convex optimization 
+
 @item @var{FUNCTION_NAME}
 It is also possible to give a @var{FUNCTION_NAME} to this option,
 instead of an @var{INTEGER}. In that case, Dynare takes the return
@@ -3988,6 +3991,7 @@ and graphics that can be later directly included in LaTeX files (not
 yet implemented)
 
 @item kalman_algo = @var{INTEGER}
+@anchor{kalman_algo}
 @dots{}
 
 @item kalman_tol = @var{DOUBLE}
@@ -5263,6 +5267,9 @@ Critical value for correlation @math{\rho}: plot couples of parmaters with
 @item mode_file = @var{FILENAME}
 @xref{mode_file}.
 
+@item kalman_algo = @var{INTEGER}
+@xref{kalman_algo}.
+
 @end table
 @customhead{Identification Analysis Options}
 @table @code
@@ -5290,6 +5297,9 @@ for identification analysis. Default: @code{0}
 @item ar = @var{INTEGER}
 Maximum number of lags for moments in identification analysis. Default: @code{1}
 
+@item lik_init = @var{INTEGER}
+@xref{lik_init}.
+
 @end table
 
 @end deffn
@@ -5357,6 +5367,9 @@ Specify the parameter set to use. Default: @code{prior_mean}
 @item lik_init = @var{INTEGER}
 @xref{lik_init}.
 
+@item kalman_algo = @var{INTEGER}
+@xref{kalman_algo}.
+
 @end table
 
 @end deffn
@@ -5841,20 +5854,17 @@ model. Output @code{.eps} files are contained in
 @xref{simulation_file_tag}.
 
 @item horizon = @var{INTEGER}
-The forecast horizon. Default: @code{12}
+@anchor{horizon} The forecast horizon. Default: @code{12}
 
 @item filtered_probabilities
 @anchor{filtered_probabilities} Uses filtered probabilities at the end
-of the sample as initial conditions for regime probabilities. Default:
+of the sample as initial conditions for regime probabilities. Only one
-@code{off}
+of @code{filtered_probabilities}, @code{regime} and @code{regimes} may
-
+be passed. Default: @code{off}
-@item no_error_bands
-@anchor{no_error_bands} Do not output error bands. Default: @code{off}
-(@i{i.e.} output error bands)
 
 @item error_band_percentiles = [@var{DOUBLE1} @dots{}]
 @anchor{error_band_percentiles} The percentiles to compute. Default:
-@code{[0.16 0.50 0.84]}. If @code{no_error_bands} is passed, the default
+@code{[0.16 0.50 0.84]}. If @code{median} is passed, the default
 is @code{[0.5]}
 
 @item shock_draws = @var{INTEGER}
@@ -5873,11 +5883,25 @@ draws in posterior draws file are used. Default: @code{1}
 @anchor{free_parameters} A vector of free parameters to initialize theta
 of the model. Default: use estimated parameters
 
-@item median
+@item parameter_uncertainty
-@anchor{median}
+@anchor{parameter_uncertainty} Calculate IRFs under parameter
+uncertainty. Requires that @command{ms_simulation} has been
+run. Default: @code{off}
 
-A shortcut to setting @code{error_band_percentiles=[0.5]}. Default:
+@item regime = @var{INTEGER}
-@code{off}
+@anchor{regime} Given the data and model parameters, what is the ergodic
+probability of being in the specified regime. Only one of
+@code{filtered_probabilities}, @code{regime} and @code{regimes} may be
+passed. Default: @code{off}
+
+@item regimes
+@anchor{regimes} Describes the evolution of regimes. Only one of
+@code{filtered_probabilities}, @code{regime} and @code{regimes} may be
+passed. Default: @code{off}
+
+@item median
+@anchor{median} A shortcut to setting
+@code{error_band_percentiles=[0.5]}. Default: @code{off}
 
 @end table
 
@@ -5909,9 +5933,6 @@ while data files are contained in @code{<output_file_tag/Forecast>}.
 @item data_obs_nbr = @var{INTEGER}
 The number of data points included in the output. Default: @code{0}
 
-@item no_error_bands
-@xref{no_error_bands}.
-
 @item error_band_percentiles = [@var{DOUBLE1} @dots{}]
 @xref{error_band_percentiles}.
 
@@ -5927,6 +5948,16 @@ The number of data points included in the output. Default: @code{0}
 @item free_parameters = @var{NUMERICAL_VECTOR}
 @xref{free_parameters}.
 
+@item parameter_uncertainty
+@xref{parameter_uncertainty}.
+
+@item regime = @var{INTEGER}
+@xref{regime}.
+
+@item regimes
+
+@xref{regimes}.
+
 @item median
 
 @xref{median}.
@@ -5959,11 +5990,15 @@ are contained in @code{<output_file_tag/Variance_Decomposition>}.
 @item simulation_file_tag = @var{FILENAME}
 @xref{simulation_file_tag}.
 
+@item horizon = @var{INTEGER}
+@xref{horizon}.
+
 @item filtered_probabilities
 @xref{filtered_probabilities}.
 
 @item no_error_bands
-@xref{no_error_bands}.
+Do not output percentile error bands (@i{i.e.} compute mean). Default:
+@code{off} (@i{i.e.} output error bands)
 
 @item error_band_percentiles = [@var{DOUBLE1} @dots{}]
 @xref{error_band_percentiles}.
@@ -5980,9 +6015,15 @@ are contained in @code{<output_file_tag/Variance_Decomposition>}.
 @item free_parameters = @var{NUMERICAL_VECTOR}
 @xref{free_parameters}.
 
-@item median
+@item parameter_uncertainty
+@xref{parameter_uncertainty}.
 
-@xref{median}.
+@item regime = @var{INTEGER}
+@xref{regime}.
+
+@item regimes
+
+@xref{regimes}.
 
 @end table
 
@@ -6075,7 +6116,7 @@ line. The main directives are:
 @item
 @code{@@#define}, for defining a macro-processor variable,
 @item
-@code{@@#if}, @code{@@#then}, @code{@@#else}, @code{@@#endif} for
+@code{@@#if}, @code{@@#ifdef}, @code{@@#else}, @code{@@#endif} for
 conditional statements,
 @item
 @code{@@#for}, @code{@@#endfor} for constructing loops.
@@ -6218,13 +6259,15 @@ end;
 @end deffn
 
 @deffn {Macro directive} @@#if @var{MACRO_EXPRESSION}
+@deffnx {Macro directive} @@#ifdef @var{MACRO_VARIABLE}
 @deffnx {Macro directive} @@#else
 @deffnx {Macro directive} @@#endif
 Conditional inclusion of some part of the @file{.mod} file.
-The lines between @code{@@#if} and the next @code{@@#else} or
+The lines between @code{@@#if} or @code{@@#ifdef} and the next
-@code{@@#end} is executed only if the condition evaluates to a
+@code{@@#else} or @code{@@#endif} is executed only if the condition
-non-null integer. The @code{@@#else} branch is optional and, if
+evaluates to a non-null integer. The @code{@@#else} branch is optional
-present, is only evaluated if the condition evaluates to @code{0}.
+and, if present, is only evaluated if the condition evaluates to
+@code{0}.
 
 @examplehead
 
@@ -6242,6 +6285,23 @@ model;
 end;
 @end example
 
+@examplehead
+
+Choose between two alternative monetary policy rules using a
+macro-variable. As @code{linear_mon_pol} was not previously defined in
+this example, the second equation will be chosen:
+
+@example
+model;
+@@#ifdef linear_mon_pol
+  i = w*i(-1) + (1-w)*i_ss + w2*(pie-piestar);
+@@#else
+  i = i(-1)^w * i_ss^(1-w) * (pie/piestar)^w2;
+@@#endif
+...
+end;
+@end example
+
 @end deffn
 
 @deffn {Macro directive} @@#for @var{MACRO_VARIABLE} in @var{MACRO_EXPRESSION}

doc/gsa/gsa.tex

@@ -8,6 +8,8 @@
 \usepackage{psfrag}
 \usepackage{setspace}
 \usepackage{rotating}
+\usepackage{hyperref}
+\hypersetup{breaklinks=true,pagecolor=white,colorlinks=true,linkcolor=blue,citecolor=blue,urlcolor=blue}
 %\singlespacing (interlinea singola)
 %\onehalfspacing (interlinea 1,5)
 %\doublespacing (interlinea doppia)
@@ -20,7 +22,13 @@
 \begin{document}
 
 % ----------------------------------------------------------------
-\title{Sensitivity Analysis Toolbox for DYNARE}%
+\title{Sensitivity Analysis Toolbox for DYNARE\thanks{Copyright \copyright~2012 Dynare
+    Team. Permission is granted to copy, distribute and/or modify
+    this document under the terms of the GNU Free Documentation
+    License, Version 1.3 or any later version published by the Free
+    Software Foundation; with no Invariant Sections, no Front-Cover
+    Texts, and no Back-Cover Texts. A copy of the license can be found
+    at: \url{http://www.gnu.org/licenses/fdl.txt}}}
 
 \author{Marco Ratto\\
 European Commission, Joint Research Centre \\

doc/macroprocessor/macroprocessor.tex

@@ -65,7 +65,7 @@
     \begin{itemize}
     \item file inclusion
     \item loops (\textit{for} structure)
-    \item conditional inclusion (\textit{if/then/else} structures)
+    \item conditional inclusion (\textit{if/else} structures)
     \item expression substitution
     \end{itemize}
   \item Implemented in Dynare starting from version 4.0
@@ -95,7 +95,7 @@
     \begin{itemize}
     \item file inclusion: \verb+@#include+
     \item definition a variable of the macro-processor: \verb+@#define+
-    \item conditional statements (\verb+@#if/@#then/@#else/@#endif+)
+    \item conditional statements (\verb+@#if/@#else/@#endif+)
     \item loop statements (\verb+@#for/@#endfor+)
     \end{itemize}
   \item In most cases, directives occupy exactly one line of text. In case of need, two anti-slashes (\verb+\\+) at the end of the line indicates that the directive is continued on the next line.

license.txt

@@ -1,31 +1,21 @@
-Format: http://dep.debian.net/deps/dep5/
+Format: http://www.debian.org/doc/packaging-manuals/copyright-format/1.0/
 Upstream-Name: Dynare
-Upstream-Contact: Dynare Team, whose members in 2011 are:
+Upstream-Contact: Dynare Team, whose members in 2012 are:
- Stéphane Adjemian <stephane.adjemian@ens.fr>
+                  Stéphane Adjemian <stephane.adjemian@ens.fr>
- Houtan Bastani <houtan.bastani@ens.fr>
+                  Houtan Bastani <houtan.bastani@ens.fr>
- Michel Juillard <michel.juillard@mjui.fr>
+                  Michel Juillard <michel.juillard@mjui.fr>
- Junior Maih <junior.maih@gmail.com>
+                  Frédéric Karamé <frederic.karame@univ-evry.fr>
- Ferhat Mihoubi <fmihoubi@univ-evry.fr>
+                  Junior Maih <junior.maih@gmail.com>
- George Perendia <george@perendia.orangehome.co.uk>
+                  Ferhat Mihoubi <fmihoubi@univ-evry.fr>
- Marco Ratto <marco.ratto@jrc.ec.europa.eu>
+                  George Perendia <george@perendia.orangehome.co.uk>
- Sébastien Villemot <sebastien.villemot@ens.fr>
+                  Johannes Pfeifer <jpfeifer@gmx.de>
+                  Marco Ratto <marco.ratto@jrc.ec.europa.eu>
+                  Sébastien Villemot <sebastien.villemot@ens.fr>
 Source: http://www.dynare.org
 
 Files: *
-Copyright: 1996-2011 Dynare Team
+Copyright: 1996-2012 Dynare Team
 License: GPL-3+
- Dynare is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- Dynare is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- You should have received a copy of the GNU General Public License
- along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 Files: matlab/AIM/SP*
 Copyright: public-domain
@@ -49,41 +39,18 @@ License: public-domain
  Journal of Economic Dynamics and Control, 2010, vol. 34, issue 3,
  pages 472-489
 
-Files: matlab/bfgsi.m
+Files: matlab/bfgsi.m matlab/csolve.m matlab/csminit1.m matlab/numgrad2.m
-Copyright: 1993-2009 Christopher Sims
+ matlab/numgrad3.m matlab/numgrad5.m matlab/csminwel.m matlab/bvar_density.m
-License: GPL-3+
+ matlab/bvar_toolbox.m matlab/partial_information/PI_gensys.m matlab/qzswitch.m
- Dynare is free software: you can redistribute it and/or modify
+ matlab/qzdiv.m
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- Dynare is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- You should have received a copy of the GNU General Public License
- along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-
-Files: matlab/csolve.m matlab/csminit1.m matlab/numgrad2.m matlab/numgrad3.m
- matlab/numgrad5.m matlab/csminwel.m matlab/bvar_density.m
- matlab/bvar_toolbox.m matlab/partial_information/PI_gensys.m
- matlab/qzswitch.m matlab/qzdiv.m
 Copyright: 1993-2009 Christopher Sims
            2006-2011 Dynare Team
 License: GPL-3+
- Dynare is free software: you can redistribute it and/or modify
+
- it under the terms of the GNU General Public License as published by
+Files: matlab/cmaes.m
- the Free Software Foundation, either version 3 of the License, or
+Copyright: 2001-2012 Nikolaus Hansen
- (at your option) any later version.
+           2012 Dynare Team
- .
+License: GPL-3+
- Dynare is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- You should have received a copy of the GNU General Public License
- along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 Files: matlab/missing/stats/normpdf.m matlab/missing/stats/gamcdf.m
  matlab/missing/stats/common_size.m matlab/missing/stats/chi2inv.m
@@ -95,173 +62,54 @@ Files: matlab/missing/stats/normpdf.m matlab/missing/stats/gamcdf.m
 Copyright: 1995-2007 Kurt Hornik
            2008-2009 Dynare Team
 License: GPL-3+
- Dynare is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- Dynare is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- You should have received a copy of the GNU General Public License
- along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 Files: matlab/missing/bicgstab/bicgstab.m
 Copyright: 2008 Radek Salac
 License: GPL-3+
- This file is part of Octave.
- .
- Octave is free software; you can redistribute it and/or modify it
- under the terms of the GNU General Public License as published by
- the Free Software Foundation; either version 3 of the License, or (at
- your option) any later version.
- .
- Octave is distributed in the hope that it will be useful, but
- WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- General Public License for more details.
- .
- You should have received a copy of the GNU General Public License
- along with Octave; see the file COPYING.  If not, see
- <http://www.gnu.org/licenses/>.
 
 Files: doc/dynare.texi doc/*.tex doc/*.svg doc/*.dia doc/*.pdf doc/*.bib
-Copyright: 1996-2011 Dynare Team
+Copyright: 1996-2012 Dynare Team
 License: GFDL-NIV-1.3+
- Permission is granted to copy, distribute and/or modify this document
- under the terms of the GNU Free Documentation License, Version 1.3 or
- any later version published by the Free Software Foundation; with no
- Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
- .
- A copy of the license can be found at <http://www.gnu.org/licenses/fdl.txt>
 
 Files: doc/userguide/*.tex doc/userguide/*.bib doc/userguide/*.pdf
 Copyright: 2007-2011 Tommaso Mancini Griffoli
 License: GFDL-NIV-1.3+
- Permission is granted to copy, distribute and/or modify this document
- under the terms of the GNU Free Documentation License, Version 1.3 or
- any later version published by the Free Software Foundation; with no
- Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
- .
- A copy of the license can be found at <http://www.gnu.org/licenses/fdl.txt>
 
 Files: doc/dr.tex doc/bvar_a_la_sims.tex
 Copyright: 2007-2011 Sébastien Villemot
 License: GFDL-NIV-1.3+
- Permission is granted to copy, distribute and/or modify this document
- under the terms of the GNU Free Documentation License, Version 1.3 or
- any later version published by the Free Software Foundation; with no
- Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
- .
- A copy of the license can be found at <http://www.gnu.org/licenses/fdl.txt>
 
 Files: dynare++/*.cweb dynare++/*.hweb dynare++/*.cpp dynare++/*.h
  dynare++/*.tex dynare++/*.mod dynare++/*.m dynare++/*.web dynare++/*.lex
  dynare++/*.y
 Copyright: 2004-2011 Ondra Kamenik
 License: GPL-3+
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- You should have received a copy of the GNU General Public License
- along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 Files: dynare++/utils/*.cpp dynare++/utils/*.h dynare++/parser/*.cpp
  dynare++/parser/*.h dynare++/parser/*.lex dynare++/parser/*.y
 Copyright: 2004-2011 Ondra Kamenik
 License: LGPL-3+
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU Lesser General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU Lesser General Public License for more details.
- .
- You should have received a copy of the GNU Lesser General Public License
- along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
 Files: m4/ax_blas.m4 m4/ax_lapack.m4
 Copyright: 2008 Steven G. Johnson <stevenj@alum.mit.edu>
 License: GPL-3+ with Autoconf exception
- This program is free software: you can redistribute it and/or modify it
- under the terms of the GNU General Public License as published by the
- Free Software Foundation, either version 3 of the License, or (at your
- option) any later version.
- .
- This program is distributed in the hope that it will be useful, but
- WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
- Public License for more details.
- .
- You should have received a copy of the GNU General Public License along
- with this program. If not, see <http://www.gnu.org/licenses/>.
- .
- As a special exception, the respective Autoconf Macro's copyright owner
- gives unlimited permission to copy, distribute and modify the configure
- scripts that are the output of Autoconf when processing the Macro. You
- need not follow the terms of the GNU General Public License when using
- or distributing such scripts, even though portions of the text of the
- Macro appear in them. The GNU General Public License (GPL) does govern
- all other use of the material that constitutes the Autoconf Macro.
- .
- This special exception to the GPL applies to versions of the Autoconf
- Macro released by the Autoconf Archive. When you make and distribute a
- modified version of the Autoconf Macro, you may extend this special
- exception to the GPL to apply to your modified version as well.
 
 Files: m4/ax_pthread.m4
 Copyright: 2008 Steven G. Johnson <stevenj@alum.mit.edu>
            2010 Dynare Team
 License: GPL-3+ with Autoconf exception
- This program is free software: you can redistribute it and/or modify it
- under the terms of the GNU General Public License as published by the
- Free Software Foundation, either version 3 of the License, or (at your
- option) any later version.
- .
- This program is distributed in the hope that it will be useful, but
- WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
- Public License for more details.
- .
- You should have received a copy of the GNU General Public License along
- with this program. If not, see <http://www.gnu.org/licenses/>.
- .
- As a special exception, the respective Autoconf Macro's copyright owner
- gives unlimited permission to copy, distribute and modify the configure
- scripts that are the output of Autoconf when processing the Macro. You
- need not follow the terms of the GNU General Public License when using
- or distributing such scripts, even though portions of the text of the
- Macro appear in them. The GNU General Public License (GPL) does govern
- all other use of the material that constitutes the Autoconf Macro.
- .
- This special exception to the GPL applies to versions of the Autoconf
- Macro released by the Autoconf Archive. When you make and distribute a
- modified version of the Autoconf Macro, you may extend this special
- exception to the GPL to apply to your modified version as well.
 
 Files: m4/ax_boost_base.m4
 Copyright: 2008 Thomas Porschberg <thomas@randspringer.de>
            2009 Dynare Team
-License:
+License: other
  Copying and distribution of this file, with or without modification, are
  permitted in any medium without royalty provided the copyright notice
  and this notice are preserved.
 
 Files: m4/ax_compare_version.m4
 Copyright: 2008 Tim Toolan <toolan@ele.uri.edu>
-License:
+License: other
  Copying and distribution of this file, with or without modification, are
  permitted in any medium without royalty provided the copyright notice
  and this notice are preserved.
@@ -270,22 +118,72 @@ Files: m4/ax_latex_bibtex_test.m4 m4/ax_latex_class.m4 m4/ax_tex_test.m4
 Copyright: 2008 Boretti Mathieu <boretti@eig.unige.ch>
            2009 Dynare Team
 License: LGPL-2.1+
- This library is free software; you can redistribute it and/or modify it
- under the terms of the GNU Lesser General Public License as published by
- the Free Software Foundation; either version 2.1 of the License, or (at
- your option) any later version.
- .
- This library is distributed in the hope that it will be useful, but
- WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser
- General Public License for more details.
- .
- You should have received a copy of the GNU Lesser General Public License
- along with this library. If not, see <http://www.gnu.org/licenses/>.
 
 Files: m4/ax_matlab_arch.m4 m4/ax_matlab.m4 m4/ax_mexext.m4
 Copyright: 2002-2003 Ralph Schleicher
            2009 Dynare Team
+License: GPL-2+ with Autoconf exception
+
+Files: dynare.el
+Copyright: 2010 Yannick Kalantzis
+License: GPL-3+
+
+Files: mex/sources/libslicot/*
+Copyright: 2002-2009 NICONET e.V.
+License: GPL-2+
+
+Files: mex/sources/sobol/sobol.hh mex/sources/sobol/initialize_v_array.hh
+ mex/sources/sobol/initialize_v_array.inc
+Copyright: 2009 John Burkardt
+           2010-2011 Dynare Team
+License: LGPL-3+
+
+Files: ms-sbvar/utilities_dw/*
+Copyright: 1996-2011 Daniel Waggoner
+License: GPL-3+
+
+Files: ms-sbvar/switch_dw/*
+Copyright: 1996-2011 Daniel Waggoner and Tao Zha
+License: GPL-3+
+
+Files: ms-sbvar/switch_dw/state_space/sbvar/dw_csminwel.c
+ state_space/sbvar/dw_csminwel.h
+Copyright: 1996 Christopher Sims
+           2003 Karibzhanov, Waggoner and Zha
+License: GPL-3+
+
+Files: matlab/ms-sbvar/cstz/*
+Copyright: 1993-2011 Tao Zha
+License: GPL-3+
+
+Files: matlab/ms-sbvar/cstz/bfgsi.m matlab/ms-sbvar/cstz/csminit.m
+ matlab/ms-sbvar/cstz/csminwel.m
+Copyright: 1993-2011 Tao Zha and Christopher Sims
+License: GPL-3+
+
+License: GFDL-NIV-1.3+
+ Permission is granted to copy, distribute and/or modify this document
+ under the terms of the GNU Free Documentation License, Version 1.3 or
+ any later version published by the Free Software Foundation; with no
+ Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
+ .
+ A copy of the license can be found at <http://www.gnu.org/licenses/fdl.txt>
+
+License: GPL-2+
+ This program is free software: you can redistribute it and/or
+ modify it under the terms of the GNU General Public License as
+ published by the Free Software Foundation, either version 2 of
+ the License, or (at your option) any later version.
+ .
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU General Public License for more details.
+ .
+ You should have received a copy of the GNU General Public License
+ along with this program.  If not, see
+ <http://www.gnu.org/licenses/>.
+
 License: GPL-2+ with Autoconf exception
  This program is free software; you can redistribute it and/or
  modify it under the terms of the GNU General Public License as
@@ -306,44 +204,61 @@ License: GPL-2+ with Autoconf exception
  it under the same distribution terms that you use for the rest
  of that program.
 
-Files: dynare.el
-Copyright: 2010 Yannick Kalantzis
 License: GPL-3+
- This program is free software; you can redistribute it and/or modify
+ Dynare is free software: you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation, either version 3 of the License, or
  (at your option) any later version.
  .
- This program is distributed in the hope that it will be useful,
+ Dynare is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.
  .
  You should have received a copy of the GNU General Public License
- along with this program.  If not, see
+ along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
- <http://www.gnu.org/licenses/>.
 
-Files: mex/sources/libslicot/*
+License: GPL-3+ with Autoconf exception
-Copyright: 2002-2009 NICONET e.V.
+ This program is free software: you can redistribute it and/or modify it
-License: GPL-2+
+ under the terms of the GNU General Public License as published by the
- This program is free software: you can redistribute it and/or
+ Free Software Foundation, either version 3 of the License, or (at your
- modify it under the terms of the GNU General Public License as
+ option) any later version.
- published by the Free Software Foundation, either version 2 of
- the License, or (at your option) any later version.
  .
- This program is distributed in the hope that it will be useful,
+ This program is distributed in the hope that it will be useful, but
- but WITHOUT ANY WARRANTY; without even the implied warranty of
+ WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
- GNU General Public License for more details.
+ Public License for more details.
  .
- You should have received a copy of the GNU General Public License
+ You should have received a copy of the GNU General Public License along
- along with this program.  If not, see
+ with this program. If not, see <http://www.gnu.org/licenses/>.
- <http://www.gnu.org/licenses/>.
+ .
+ As a special exception, the respective Autoconf Macro's copyright owner
+ gives unlimited permission to copy, distribute and modify the configure
+ scripts that are the output of Autoconf when processing the Macro. You
+ need not follow the terms of the GNU General Public License when using
+ or distributing such scripts, even though portions of the text of the
+ Macro appear in them. The GNU General Public License (GPL) does govern
+ all other use of the material that constitutes the Autoconf Macro.
+ .
+ This special exception to the GPL applies to versions of the Autoconf
+ Macro released by the Autoconf Archive. When you make and distribute a
+ modified version of the Autoconf Macro, you may extend this special
+ exception to the GPL to apply to your modified version as well.
+
+License: LGPL-2.1+
+ This library is free software; you can redistribute it and/or modify it
+ under the terms of the GNU Lesser General Public License as published by
+ the Free Software Foundation; either version 2.1 of the License, or (at
+ your option) any later version.
+ .
+ This library is distributed in the hope that it will be useful, but
+ WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser
+ General Public License for more details.
+ .
+ You should have received a copy of the GNU Lesser General Public License
+ along with this library. If not, see <http://www.gnu.org/licenses/>.
 
-Files: mex/sources/sobol/sobol.hh mex/sources/sobol/initialize_v_array.hh
- mex/sources/sobol/initialize_v_array.inc
-Copyright: 2009 John Burkardt
-           2010-2011 Dynare Team
 License: LGPL-3+
  This program is free software: you can redistribute it and/or modify
  it under the terms of the GNU Lesser General Public License as published by
@@ -357,86 +272,3 @@ License: LGPL-3+
  .
  You should have received a copy of the GNU Lesser General Public License
  along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
-Files: ms-sbvar/utilities_dw/*
-Copyright: 1996-2011 Daniel Waggoner
-License: GPL-3+
- This free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- It is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- If you did not received a copy of the GNU General Public License
- with this software, see <http://www.gnu.org/licenses/>.
-
-Files: ms-sbvar/switch_dw/*
-Copyright: 1996-2011 Daniel Waggoner and Tao Zha
-License: GPL-3+
- This free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- It is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- If you did not received a copy of the GNU General Public License
- with this software, see <http://www.gnu.org/licenses/>.
-
-Files: ms-sbvar/switch_dw/state_space/sbvar/dw_csminwel.c
- state_space/sbvar/dw_csminwel.h
-Copyright: 1996 Christopher Sims
-           2003 Karibzhanov, Waggoner and Zha
-License: GPL-3+
- This free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- It is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- If you did not received a copy of the GNU General Public License
- with this software, see <http://www.gnu.org/licenses/>.
-
-Files: matlab/ms-sbvar/cstz/*
-Copyright: 1993-2011 Tao Zha
-License: GPL-3+
- This free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- It is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- If you did not received a copy of the GNU General Public License
- with this software, see <http://www.gnu.org/licenses/>.
-
-Files: matlab/ms-sbvar/cstz/bfgsi.m matlab/ms-sbvar/cstz/csminit.m
- matlab/ms-sbvar/cstz/csminwel.m
-Copyright: 1993-2011 Tao Zha and Christopher Sims
-License: GPL-3+
- This free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
- .
- It is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU General Public License for more details.
- .
- If you did not received a copy of the GNU General Public License
- with this software, see <http://www.gnu.org/licenses/>.

matlab/distributions/inverse_gamma_specification.m

@@ -44,7 +44,7 @@ function [s,nu] = inverse_gamma_specification(mu,sigma,type,use_fzero_flag)
 %! @end deftypefn
 %@eod:
 
-% Copyright (C) 2003-2011 Dynare Team
+% Copyright (C) 2003-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -99,7 +99,7 @@ elseif type == 1;   % Inverse Gamma 1
                 end
             end
             % Solve for nu using the secant method.
-            while abs(nu2-nu1) > 1e-8
+            while abs(nu2/nu1-1) > 1e-14
                 if err > 0
                     nu1 = nu;
                     if nu < nu2
@@ -117,10 +117,10 @@ elseif type == 1;   % Inverse Gamma 1
         end
         s = (sigma2+mu2)*(nu-2);
         if check_solution_flag
-            if abs(mu-sqrt(s/2)*gamma((nu-1)/2)/gamma(nu/2))>1e-9
+            if abs(log(mu)-log(sqrt(s/2))-gammaln((nu-1)/2)+gammaln(nu/2))>1e-7
                 error('inverse_gamma_specification:: Failed in solving for the hyperparameters!');
             end
-            if abs(sigma-sqrt(s/(nu-2)-mu^2))>1e-9
+            if abs(sigma-sqrt(s/(nu-2)-mu^2))>1e-7
                 error('inverse_gamma_specification:: Failed in solving for the hyperparameters!');
             end
         end

matlab/dr1.m

@@ -1,4 +1,4 @@
-function [dr,info,M_,options_,oo_] = dr1(dr,task,M_,options_,oo_)
+function [dr,info] = dr1(dr,task,M_,options_,oo_)
 % function [dr,info,M_,options_,oo_] = dr1(dr,task,M_,options_,oo_)
 % computes the reduced form solution of a rational expectation model (first or second order
 % approximation of the stochastic model around the deterministic steady state). 
@@ -21,9 +21,6 @@ function [dr,info,M_,options_,oo_] = dr1(dr,task,M_,options_,oo_)
 %                                         indeterminacy.
 %                                 info=5: BK rank condition not satisfied.
 %                                 info=6: The jacobian matrix evaluated at the steady state is complex.        
-%   M_         [matlab structure]            
-%   options_   [matlab structure]
-%   oo_        [matlab structure]
 %  
 % ALGORITHM
 %   ...
@@ -61,7 +58,6 @@ end
 if options_.k_order_solver;
     dr = set_state_space(dr,M_);
     [dr,info] = k_order_pert(dr,M_,options_,oo_);
-    oo_.dr = dr;
     return;
 end
 
@@ -72,20 +68,12 @@ iyv = iyv(:);
 iyr0 = find(iyv) ;
 it_ = M_.maximum_lag + 1 ;
 
-if M_.exo_nbr == 0
-    oo_.exo_steady_state = [] ;
-end
-
 klen = M_.maximum_lag + M_.maximum_lead + 1;
 iyv = lead_lag_incidence';
 iyv = iyv(:);
 iyr0 = find(iyv) ;
 it_ = M_.maximum_lag + 1 ;
 
-if M_.exo_nbr == 0
-    oo_.exo_steady_state = [] ;
-end
-
 it_ = M_.maximum_lag + 1;
 z = repmat(dr.ys,1,klen);
 if ~options_.bytecode

matlab/dr_block.m

@@ -567,7 +567,9 @@ for i = 1:Size;
                 if block_type == 5
                     vghx_other = - inv(kron(eye(size(D_,2)), A_) + kron(C_', B_)) * vec(D_);
                     ghx_other = reshape(vghx_other, size(D_,1), size(D_,2));
-                else
+                elseif options_.sylvester_fp == 1
+                    ghx_other = gensylv_fp(A_, B_, C_, D_, i);
+                else 
                     [err, ghx_other] = gensylv(1, A_, B_, C_, -D_);
                 end;
                 if options_.aim_solver ~= 1 && options_.use_qzdiv
@@ -650,7 +652,7 @@ for i = 1:Size;
         end
     end;
     if task ~=1
-        if (maximum_lag > 0 && n_pred > 0)
+        if (maximum_lag > 0 && (n_pred > 0 || n_both > 0))
             sorted_col_dr_ghx = M_.block_structure.block(i).sorted_col_dr_ghx;
             dr.ghx(endo, sorted_col_dr_ghx) = dr.ghx(endo, sorted_col_dr_ghx) + ghx;
             data(i).ghx = ghx;

matlab/dsge_likelihood.m

@@ -127,17 +127,21 @@ function [fval,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,Dynar
 % AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT FR
 
 % Declaration of the penalty as a persistent variable.
-persistent penalty
 
-% Initialization of the persistent variable.
+% Persistent variable 'penalty' is used to compute an endogenous penalty to
-if ~nargin || isempty(penalty)
+% the value 'fval' when various conditions are encountered. These conditions
-    penalty = 1e8;
+% set also 'exit_flag' equal to 0 instead of 1.  It is only when
-    if ~nargin, return, end
+% dsge_likelihood() is called by an optimizer called by
-end
+% dynare_estimation_1() that 'exit_flag' is ignored and penalized 'fval' is
-if nargin==1
+% actually used.  
-    penalty = xparam1;
+% In that case, 'penalty' is properly initialized, at the very end of the
-    return
+% present function, by a call to dsge_likelihood() made in
-end
+% initial_estimation_checks(). If a condition triggers exit_flag ==
+% 0, initial_estimation_checks() triggers an error.
+% In summary, an initial call to the present function, without triggering
+% any condition, guarantees that 'penalty' is properly initialized when needed.
+
+persistent penalty
 
 % Initialization of the returned variables and others...
 fval        = [];
@@ -146,6 +150,21 @@ trend_coeff = [];
 exit_flag   = 1;
 info        = 0;
 singularity_flag = 0;
+DLIK        = [];
+AHess       = [];
+
+if DynareOptions.estimation_dll
+    [fval,exit_flag,ys,trend_coeff,info,params,H,Q] ...
+        = logposterior(xparam1,DynareDataset, DynareOptions,Model, ...
+                          EstimatedParameters,BayesInfo,DynareResults);
+    Model.params = params;
+    if ~isequal(Model.H,0)
+        Model.H = H;
+    end
+    Model.Sigma_e = Q;
+    DynareResults.dr.ys = ys;
+    return
+end
 
 % Set flag related to analytical derivatives.
 if nargout > 9
@@ -209,7 +228,7 @@ if EstimatedParameters.ncx
         a = diag(eig(Q));
         k = find(a < 0);
         if k > 0
-            fval = BayesInfo.penalty+sum(-a(k));
+            fval = penalty+sum(-a(k));
             exit_flag = 0;
             info = 43;
             return
@@ -233,7 +252,7 @@ if EstimatedParameters.ncn
         a = diag(eig(H));
         k = find(a < 0);
         if k > 0
-            fval = BayesInfo.penalty+sum(-a(k));
+            fval = penalty+sum(-a(k));
             exit_flag = 0;
             info = 44;
             return
@@ -339,13 +358,19 @@ end
 
 
 diffuse_periods = 0;
+correlated_errors_have_been_checked = 0;
+singular_diffuse_filter = 0;
 switch DynareOptions.lik_init
   case 1% Standard initialization with the steady state of the state equation.
     if kalman_algo~=2
         % Use standard kalman filter except if the univariate filter is explicitely choosen.
         kalman_algo = 1;
     end
-    Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
+    if DynareOptions.lyapunov_fp == 1
+        Pstar = lyapunov_symm(T,Q,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R);
+    else
+        Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
+    end;
     Pinf  = [];
     a     = zeros(mm,1);
     Zflag = 0;
@@ -359,10 +384,14 @@ switch DynareOptions.lik_init
     a     = zeros(mm,1);
     Zflag = 0;
   case 3% Diffuse Kalman filter (Durbin and Koopman)
-    if kalman_algo ~= 4
         % Use standard kalman filter except if the univariate filter is explicitely choosen.
+    if kalman_algo == 0
         kalman_algo = 3;
+    elseif ~((kalman_algo == 3) || (kalman_algo == 4)) 
+            error(['diffuse filter: options_.kalman_algo can only be equal ' ...
+                   'to 0 (default), 3 or 4'])
     end
+
     [Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
     Zflag = 1;
     % Run diffuse kalman filter on first periods.
@@ -383,39 +412,39 @@ switch DynareOptions.lik_init
         diffuse_periods = length(tmp);
         if isinf(dLIK)
             % Go to univariate diffuse filter if singularity problem.
-            kalman_algo = 4;
+            singular_diffuse_filter = 1;
-            singularity_flag = 1;
         end
     end
-    if (kalman_algo==4)
+    if singular_diffuse_filter || (kalman_algo==4)
         % Univariate Diffuse Kalman Filter
-        if singularity_flag
+        if isequal(H,0)
-            if isequal(H,0)
+            H1 = zeros(nobs,1);
-                H = zeros(nobs,1);
+            mmm = mm;
-                mmm = mm;
+        else
+            if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
+                H1 = diag(H);
+                mmm = mm; 
             else
-                if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
+                Z = [Z, eye(pp)];
-                    H = diag(H);
+                T = blkdiag(T,zeros(pp));
-                    mmm = mm; 
+                Q = blkdiag(Q,H);
-                else
+                R = blkdiag(R,eye(pp));
-                    Z = [Z, eye(pp)];
+                Pstar = blkdiag(Pstar,H);
-                    T = blkdiag(T,zeros(pp));
+                Pinf  = blckdiag(Pinf,zeros(pp));
-                    Q = blkdiag(Q,H);
+                H1 = zeros(nobs,1);
-                    R = blkdiag(R,eye(pp));
+                mmm   = mm+pp;
-                    Pstar = blkdiag(Pstar,H);
-                    Pinf  = blckdiag(Pinf,zeros(pp));
-                    H = zeros(nobs,1);
-                    mmm   = mm+pp;
-                end
             end
-            % no need to test again for correlation elements
-            singularity_flag = 0;
         end
-        [dLIK,tmp,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
+        % no need to test again for correlation elements
-                                                              Y, 1, size(Y,2), ...
+        correlated_errors_have_been_checked = 1;
-                                                              zeros(mmm,1), Pinf, Pstar, ...
+
-                                                              kalman_tol, riccati_tol, DynareOptions.presample, ...
+        [dLIK,tmp,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
-                                                              T,R,Q,H,Z,mmm,pp,rr);
+                                                        DynareDataset.missing.number_of_observations,...
+                                                        DynareDataset.missing.no_more_missing_observations, ...
+                                                        Y, 1, size(Y,2), ...
+                                                        zeros(mmm,1), Pinf, Pstar, ...
+                                                        kalman_tol, riccati_tol, DynareOptions.presample, ...
+                                                        T,R,Q,H1,Z,mmm,pp,rr);
         diffuse_periods = length(tmp);
     end
   case 4% Start from the solution of the Riccati equation.
@@ -586,7 +615,6 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
         else
             kalman_algo = 4;
         end
-        singularity_flag = 1;
     else
         if DynareOptions.lik_init==3
             LIK = LIK + dLIK;
@@ -594,10 +622,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
     end
 end
 
-if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) )
+if (kalman_algo==2) || (kalman_algo==4)
     % Univariate Kalman Filter
     % resetting measurement error covariance matrix when necessary                                                           % 
-    if singularity_flag
+    if ~correlated_errors_have_been_checked
         if isequal(H,0)
             H = zeros(nobs,1);
             mmm = mm;

matlab/dsge_likelihood_hh.m

@@ -1,4 +1,4 @@
-function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood_hh(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [fval,llik,cost_flag,ys,trend_coeff,info] = dsge_likelihood_hh(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
 % function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood_hh(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
 % Evaluates the posterior kernel of a dsge model.
 %
@@ -41,17 +41,20 @@ function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood_hh(xparam1,D
 
 
 % Declaration of the penalty as a persistent variable.
-persistent penalty
 
-% Initialization of the persistent variable.
+% Persistent variable 'penalty' is used to compute an endogenous penalty to
-if ~nargin || isempty(penalty)
+% the value 'fval' when various conditions are encountered. These conditions
-    penalty = 1e8;
+% set also 'exit_flag' equal to 0 instead of 1.  It is only when
-    if ~nargin, return, end
+% dsge_likelihood_hh() is called by an newrat() called by
-end
+% dynare_estimation_1() that 'exit_flag' is ignored and penalized 'fval' is
-if nargin==1
+% actually used.  
-    penalty = xparam1;
+% In that case, 'penalty' is properly initialized, at the very end of the
-    return
+% present function, by a call to dsge_likelihood_hh() made in
-end
+% initial_estimation_checks(). If a condition triggers exit_flag ==
+% 0, initial_estimation_checks() triggers an error.
+% In summary, an initial call to the present function, without triggering
+% any condition, guarantees that 'penalty' is properly initialized when needed.
+persistent penalty
 
 % Initialization of the returned variables and others...
 fval        = [];
@@ -121,7 +124,7 @@ if EstimatedParameters.ncx
         a = diag(eig(Q));
         k = find(a < 0);
         if k > 0
-            fval = BayesInfo.penalty+sum(-a(k));
+            fval = penalty+sum(-a(k));
             exit_flag = 0;
             info = 43;
             return
@@ -145,7 +148,7 @@ if EstimatedParameters.ncn
         a = diag(eig(H));
         k = find(a < 0);
         if k > 0
-            fval = BayesInfo.penalty+sum(-a(k));
+            fval = penalty+sum(-a(k));
             exit_flag = 0;
             info = 44;
             return
@@ -251,6 +254,8 @@ end
 
 
 diffuse_periods = 0;
+correlated_errors_have_been_checked = 0;
+singular_diffuse_filter = 0;
 switch DynareOptions.lik_init
   case 1% Standard initialization with the steady state of the state equation.
     if kalman_algo~=2
@@ -271,10 +276,14 @@ switch DynareOptions.lik_init
     a     = zeros(mm,1);
     Zflag = 0;
   case 3% Diffuse Kalman filter (Durbin and Koopman)
-    if kalman_algo ~= 4
         % Use standard kalman filter except if the univariate filter is explicitely choosen.
+    if kalman_algo == 0
         kalman_algo = 3;
+    elseif ~((kalman_algo == 3) || (kalman_algo == 4)) 
+        error(['diffuse filter: options_.kalman_algo can only be equal ' ...
+               'to 0 (default), 3 or 4'])
     end
+
     [Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
     Zflag = 1;
     % Run diffuse kalman filter on first periods.
@@ -282,9 +291,9 @@ switch DynareOptions.lik_init
         % Multivariate Diffuse Kalman Filter
         if no_missing_data_flag
             [dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
-                                                       zeros(mm,1), Pinf, Pstar, ...
+                                                  zeros(mm,1), Pinf, Pstar, ...
-                                                       kalman_tol, riccati_tol, DynareOptions.presample, ...
+                                                  kalman_tol, riccati_tol, DynareOptions.presample, ...
-                                                       T,R,Q,H,Z,mm,pp,rr);
+                                                  T,R,Q,H,Z,mm,pp,rr);
         else
             [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
                                                               Y, 1, size(Y,2), ...
@@ -295,39 +304,39 @@ switch DynareOptions.lik_init
         diffuse_periods = length(dlik);
         if isinf(dLIK)
             % Go to univariate diffuse filter if singularity problem.
-            kalman_algo = 4;
+            singular_diffuse_filter
-            singularity_flag = 1;
         end
     end
-    if (kalman_algo==4)
+    if singular_diffuse_filter || (kalman_algo==4)
         % Univariate Diffuse Kalman Filter
-        if singularity_flag
+        if isequal(H,0)
-            if isequal(H,0)
+            H1 = zeros(nobs,1);
-                H = zeros(nobs,1);
+            mmm = mm;
-                mmm = mm;
+        else
+            if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
+                H1 = diag(H);
+                mmm = mm; 
             else
-                if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
+                Z = [Z, eye(pp)];
-                    H = diag(H);
+                T = blkdiag(T,zeros(pp));
-                    mmm = mm; 
+                Q = blkdiag(Q,H);
-                else
+                R = blkdiag(R,eye(pp));
-                    Z = [Z, eye(pp)];
+                Pstar = blkdiag(Pstar,H);
-                    T = blkdiag(T,zeros(pp));
+                Pinf  = blckdiag(Pinf,zeros(pp));
-                    Q = blkdiag(Q,H);
+                H1 = zeros(nobs,1);
-                    R = blkdiag(R,eye(pp));
+                mmm   = mm+pp;
-                    Pstar = blkdiag(Pstar,H);
-                    Pinf  = blckdiag(Pinf,zeros(pp));
-                    H = zeros(nobs,1);
-                    mmm   = mm+pp;
-                end
             end
-            % no need to test again for correlation elements
-            singularity_flag = 0;
         end
-        [dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
+        % no need to test again for correlation elements
-                                                              Y, 1, size(Y,2), ...
+        correlated_errors_have_been_checked = 1;
-                                                              zeros(mmm,1), Pinf, Pstar, ...
+        
-                                                              kalman_tol, riccati_tol, DynareOptions.presample, ...
+        [dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
-                                                              T,R,Q,H,Z,mmm,pp,rr);
+                                                         DynareDataset.missing.number_of_observations,...
+                                                         DynareDataset.missing.no_more_missing_observations, ...
+                                                         Y, 1, size(Y,2), ...
+                                                         zeros(mmm,1), Pinf, Pstar, ...
+                                                         kalman_tol, riccati_tol, DynareOptions.presample, ...
+                                                         T,R,Q,H1,Z,mmm,pp,rr);
         diffuse_periods = length(dlik);
     end
   case 4% Start from the solution of the Riccati equation.
@@ -382,10 +391,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
     end
 end
 
-if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) )
+if (kalman_algo==2) || (kalman_algo==4)
     % Univariate Kalman Filter
     % resetting measurement error covariance matrix when necessary                                                           % 
-    if singularity_flag
+    if ~correlated_errors_have_been_checked
         if isequal(H,0)
             H = zeros(nobs,1);
             mmm = mm;

matlab/dyn_figure.m

@@ -0,0 +1,36 @@
+function h=dyn_figure(DynareOptions,varargin)
+%function h=dyn_figure(DynareOptions,varargin)
+% initializes figures for DYNARE
+%
+% INPUTS
+%    DynareOptions: dynare options
+%    varargin: the same list of possible inputs of the MATLAB function figure
+%
+% OUTPUTS
+%    h     : figure handle
+%
+% SPECIAL REQUIREMENTS
+%    none
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+if DynareOptions.nodisplay,
+    h = figure(varargin{:},'visible','off');
+else
+    h = figure(varargin{:});
+end

matlab/dyn_ramsey_static.m

@@ -56,12 +56,12 @@ if options_.steadystate_flag
     [xx,params,check] = evaluate_steady_state_file(ys,exo_ss,params,...
                                                  M.fname,options_.steadystate_flag);
 else
-    n_var = M.orig_endo_nbr+min(find([M.aux_vars.type] == 6)) - 1;
+    n_var = M.orig_endo_nbr;
     xx = oo.steady_state(1:n_var);
     [xx,info1] = dynare_solve(nl_func,xx,0);
     steady_state = nl_func(xx);
 end
-steady_state = nl_func(xx);
+[junk,junk,steady_state] = nl_func(xx);
 
 
 
@@ -71,13 +71,17 @@ rJ = [];
 mult = [];
 
 % recovering usefull fields
+params = M.params;
 endo_nbr = M.endo_nbr;
+endo_names = M.endo_names;
 exo_nbr = M.exo_nbr;
 orig_endo_nbr = M.orig_endo_nbr;
+aux_vars_type = [M.aux_vars.type];
+orig_endo_aux_nbr = orig_endo_nbr + min(find(aux_vars_type == 6)) - 1; 
 orig_eq_nbr = M.orig_eq_nbr;
-inst_nbr = orig_endo_nbr - orig_eq_nbr;
+inst_nbr = orig_endo_aux_nbr - orig_eq_nbr;
 % indices of Lagrange multipliers
-i_mult = [orig_endo_nbr+(1:orig_eq_nbr)]';
+i_mult = [orig_endo_aux_nbr+(1:orig_eq_nbr)]';
 fname = M.fname;
 max_lead = M.maximum_lead;
 max_lag = M.maximum_lag;
@@ -86,24 +90,26 @@ max_lag = M.maximum_lag;
 i_endo = [1:endo_nbr]';
 % indices of endogenous variable except instruments
 % i_inst = M.instruments;
-% lead_lag incidence matrix for endogenous variables
+% lead_lag incidence matrix
 i_lag = M.lead_lag_incidence;
 
 if options_.steadystate_flag
     k_inst = [];
     instruments = options_.instruments;
     for i = 1:size(instruments,1)
-        k_inst = [k_inst; strmatch(options_.instruments(i,:), ...
+        k_inst = [k_inst; strmatch(instruments(i,:), ...
-                                   M.endo_names,'exact')];
+                                   endo_names,'exact')];
     end
     oo.steady_state(k_inst) = x;
     [x,params,check] = evaluate_steady_state_file(oo.steady_state,...
                                                   [oo.exo_steady_state; ...
                                                   oo.exo_det_steady_state] ...
-                                                  ,M.params,M.fname,...
+                                                  ,params,fname,...
                                                   options_.steadystate_flag);
 end
 
+xx = zeros(endo_nbr,1);
+xx(1:length(x)) = x;
 % setting steady state of auxiliary variables
 % that depends on original endogenous variables
 if any([M.aux_vars.type] ~= 6)
@@ -112,44 +118,43 @@ if any([M.aux_vars.type] ~= 6)
     s_a_v_func = @(z) fh(z,... 
                          [oo.exo_steady_state,...
                         oo.exo_det_steady_state],...
-                         M.params);
+                         params);
-    x = s_a_v_func(x);
+    xx = s_a_v_func(xx);
 else
     needs_set_auxiliary_variables = 0;
 end
 
 % value and Jacobian of objective function
 ex = zeros(1,M.exo_nbr);
-[U,Uy,Uyy] = feval([fname '_objective_static'],x,ex, M.params);
+[U,Uy,Uyy] = feval([fname '_objective_static'],x,ex, params);
 Uy = Uy';
 Uyy = reshape(Uyy,endo_nbr,endo_nbr);
 
 % set multipliers and auxiliary variables that
 % depends on multipliers to 0 to compute residuals
-xx = [x; zeros(M.endo_nbr - M.orig_eq_nbr,1)];
 [res,fJ] = feval([fname '_static'],xx,[oo.exo_simul oo.exo_det_simul], ...
                M.params);
 
 % index of multipliers and corresponding equations
 % the auxiliary variables before the Lagrange multipliers are treated
 % as ordinary endogenous variables
-n_var = M.orig_endo_nbr + min(find([M.aux_vars.type] == 6)) - 1;
+aux_eq = [1:orig_endo_aux_nbr, orig_endo_aux_nbr+orig_eq_nbr+1:size(fJ,1)];
-aux_eq = [1:n_var orig_endo_nbr+orig_eq_nbr+1:size(fJ,1)];
+A = fJ(aux_eq,orig_endo_aux_nbr+1:end);
-A = fJ(aux_eq,n_var+1:end);
 y = res(aux_eq);
-mult = -A\y;
+aux_vars = -A\y;
 
-resids1 = y+A*mult;
+resids1 = y+A*aux_vars;
 if inst_nbr == 1
     r1 = sqrt(resids1'*resids1);
 else
     [q,r,e] = qr([A y]');
-    r1 = r(end,(orig_endo_nbr-inst_nbr+1:end))';
+    k = size(A,1)+(1-inst_nbr:0);
+    r1 = r(end,k)';
 end
 if options_.steadystate_flag
     resids = r1;
 else
-    resids = [res; r1];
+    resids = [res(orig_endo_nbr+(1:orig_endo_nbr-inst_nbr)); r1];
 end
 rJ = [];
-steady_state = [x(1:n_var); mult];
+steady_state = [xx(1:orig_endo_aux_nbr); aux_vars];

matlab/dyn_saveas.m

@@ -0,0 +1,49 @@
+function dyn_saveas(h,fname,DynareOptions)
+%function dyn_saveas(h,fname,DynareOptions)
+% save figures for DYNARE
+%
+% INPUTS
+%    h     : figure handle
+%    fname : name of the saved figure
+%    DynareOptions: dynare options
+%
+% OUTPUTS
+%    none
+%
+% SPECIAL REQUIREMENTS
+%    none
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+if strmatch('eps',DynareOptions.graph_format)
+    eval(['print -depsc2 ' fname '.eps']);
+end
+if ~exist('OCTAVE_VERSION')
+    if strmatch('pdf',DynareOptions.graph_format)
+        eval(['print -dpdf ' fname]);
+    end
+    if strmatch('fig',DynareOptions.graph_format)
+        if DynareOptions.nodisplay
+            set(h, 'Visible','on');
+        end
+        saveas(h,[fname '.fig']);
+    end
+end
+if DynareOptions.nodisplay
+    close(h);
+end

matlab/dynare_config.m

@@ -15,7 +15,7 @@ function dynareroot = dynare_config(path_to_dynare,verbose)
 % SPECIAL REQUIREMENTS
 %   none
 
-% Copyright (C) 2001-2011 Dynare Team
+% Copyright (C) 2001-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -70,8 +70,7 @@ if ~exist('OCTAVE_VERSION')
     addpath([dynareroot '/missing/rows_columns'])
     % Replacement for vec() (inexistent under MATLAB)
     addpath([dynareroot '/missing/vec'])
-    [has_statistics_toolbox junk] = license('checkout','statistics_toolbox');
+    if ~user_has_matlab_license('statistics_toolbox')
-    if ~has_statistics_toolbox
         % Replacements for functions of the stats toolbox
         addpath([dynareroot '/missing/stats/'])
     end
@@ -101,8 +100,7 @@ if exist('OCTAVE_VERSION')
         addpath([dynareroot '/missing/nanmean'])
     end
 else
-    [has_statistics_toolbox junk] = license('checkout','statistics_toolbox');
+    if ~user_has_matlab_license('statistics_toolbox')
-    if ~has_statistics_toolbox
         addpath([dynareroot '/missing/nanmean'])
     end
 end
@@ -113,8 +111,13 @@ if exist('OCTAVE_VERSION')
 else
     % Add win32 specific paths for Dynare Windows package
     if strcmp(computer, 'PCWIN')
-        if matlab_ver_less_than('7.5')
+        if matlab_ver_less_than('7.1')
-            mexpath = [dynareroot '../mex/matlab/win32-7.0-7.4'];
+            mexpath = [dynareroot '../mex/matlab/win32-7.0-7.0.4'];
+            if exist(mexpath, 'dir')
+                addpath(mexpath)
+            end
+        elseif matlab_ver_less_than('7.5')
+            mexpath = [dynareroot '../mex/matlab/win32-7.1-7.4'];
             if exist(mexpath, 'dir')
                 addpath(mexpath)
             end
@@ -190,6 +193,9 @@ mex_status(3,3) = {'Kronecker products'};
 mex_status(4,1) = {'sparse_hessian_times_B_kronecker_C'};
 mex_status(4,2) = {'kronecker'};
 mex_status(4,3) = {'Sparse kronecker products'};
+mex_status(5,1) = {'local_state_space_iteration_2'};
+mex_status(5,2) = {'particle/local_state_space_iteration'};
+mex_status(5,3) = {'Local state space iteraton (second order)'};
 number_of_mex_files = size(mex_status,1);
 %% Remove some directories from matlab's path. This is necessary if the user has
 %% added dynare_v4/matlab with the subfolders. Matlab has to ignore these

matlab/dynare_estimation_1.m

@@ -12,7 +12,7 @@ function dynare_estimation_1(var_list_,dname)
 % SPECIAL REQUIREMENTS
 %   none
 
-% Copyright (C) 2003-2011 Dynare Team
+% Copyright (C) 2003-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -31,8 +31,29 @@ function dynare_estimation_1(var_list_,dname)
 
 global M_ options_ oo_ estim_params_ bayestopt_ dataset_
 
+% Set particle filter flag.
+if options_.order > 1
+    if options_.particle.status && options_.order==2
+        disp(' ')
+        disp('Estimation using a non linear filter!')
+        disp(' ')
+    elseif options_.particle.status && options_.order>2
+        error(['Non linear filter are not implemented with order ' int2str(options_.order) ' approximation of the model!'])
+    elseif ~options_.particle.status && options_.order==2
+        disp('If you want to estimate the model with a second order approximation using a non linear filter, set options_.particle.status=1;')
+        disp('I set order=1!')
+        options_.order=1;
+    else
+        error(['Cannot estimate a model with an order ' int2str(options_.order) ' approximation!'])
+    end
+end
+
 if ~options_.dsge_var
-    objective_function = str2func('dsge_likelihood');
+    if options_.particle.status
+        objective_function = str2func('non_linear_dsge_likelihood');
+    else
+        objective_function = str2func('dsge_likelihood');
+    end
 else
     objective_function = str2func('DsgeVarLikelihood');
 end
@@ -267,7 +288,14 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         [xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
       case 8
         % Dynare implementation of the simplex algorithm.
-        [xparam1,fval,exitflag] = simplex_optimization-routine(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        [xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,options_.simplex,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+      case 9
+        H0 = 1e-4*ones(nx,1);
+        warning('off','CMAES:NonfinitenessRange');
+        warning('off','CMAES:InitialSigma');
+        [x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),xparam1,H0,options_.cmaes,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        xparam1=BESTEVER.x;
+        disp(sprintf('\n Objective function at mode: %f',fval))
       case 101
         myoptions=soptions;
         myoptions(2)=1e-6; %accuracy of argument

matlab/dynare_estimation_init.m

@@ -58,12 +58,8 @@ for i = 1:size(M_.endo_names,1)
     end
 end
 
-% Set the order of approximation to one (if needed).
+if options_.order>2
-if options_.order > 1 && isempty(options_.nonlinear_filter)
+    error(['I cannot estimate a model with a ' int2str(options_.order) ' order approximation of the model!'])
-    disp('This version of Dynare cannot estimate non linearized models!')
-    disp('Set "order" equal to 1.')
-    disp(' ')
-    options_.order = 1;
 end
 
 % Set options_.lik_init equal to 3 if diffuse filter is used or
@@ -214,6 +210,7 @@ end
 k2 = union(var_obs_index,[dr.nstatic+1:dr.nstatic+dr.npred]', 'rows');
 % Set restrict_state to postion of observed + state variables in expanded state vector.
 oo_.dr.restrict_var_list = k2;
+bayestopt_.restrict_var_list = k2;
 % set mf0 to positions of state variables in restricted state vector for likelihood computation.
 [junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
 % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
@@ -327,8 +324,9 @@ nvx = estim_params_.nvx;
 ncx = estim_params_.ncx;
 nvn = estim_params_.nvn;
 ncn = estim_params_.ncn;
-M.params(estim_params_.param_vals(:,1)) = xparam1(nvx+ncx+nvn+ncn+1:end);
+if isfield(estim_params_,'param_vals')
-
+  M.params(estim_params_.param_vals(:,1)) = xparam1(nvx+ncx+nvn+ncn+1:end);
+end;
 oo_.steady_state = evaluate_steady_state(oo_.steady_state,M,options_,oo_,steadystate_check_flag);
 if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
     options_.noconstant = 1;

matlab/dynare_solve.m

@@ -16,7 +16,7 @@ function [x,info] = dynare_solve(func,x,jacobian_flag,varargin)
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright (C) 2001-2011 Dynare Team
+% Copyright (C) 2001-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -39,8 +39,7 @@ options_ = set_default_option(options_,'solve_algo',2);
 info = 0;
 if options_.solve_algo == 0
     if ~exist('OCTAVE_VERSION')
-        [has_optimization_toolbox junk] = license('checkout','optimization_toolbox');
+        if ~user_has_matlab_license('optimization_toolbox')
-        if ~has_optimization_toolbox
             error('You can''t use solve_algo=0 since you don''t have MATLAB''s Optimization Toolbox')
         end
     end
@@ -106,7 +105,7 @@ elseif options_.solve_algo == 2 || options_.solve_algo == 4
 
     if ~jacobian_flag
         fjac = zeros(nn,nn) ;
-        dh = max(abs(x),options_.gstep*ones(nn,1))*eps^(1/3);
+        dh = max(abs(x),options_.gstep(1)*ones(nn,1))*eps^(1/3);
         for j = 1:nn
             xdh = x ;
             xdh(j) = xdh(j)+dh(j) ;

matlab/ep/extended_path.m

@@ -1,17 +1,17 @@
 function time_series = extended_path(initial_conditions,sample_size)
 % Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
-% series of size T  is obtained by solving T perfect foresight models. 
+% series of size T  is obtained by solving T perfect foresight models.
-%    
+%
 % INPUTS
 %  o initial_conditions     [double]    m*nlags array, where m is the number of endogenous variables in the model and
 %                                       nlags is the maximum number of lags.
 %  o sample_size            [integer]   scalar, size of the sample to be simulated.
-%   
+%
 % OUTPUTS
 %  o time_series            [double]    m*sample_size array, the simulations.
-%    
+%
 % ALGORITHM
-%  
+%
 % SPECIAL REQUIREMENTS
 
 % Copyright (C) 2009, 2010, 2011 Dynare Team
@@ -31,15 +31,66 @@ function time_series = extended_path(initial_conditions,sample_size)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 global M_ options_ oo_
-    
-debug = 0;
-options_.verbosity = options_.ep.verbosity;
-verbosity = options_.ep.verbosity+debug;
 
-% Test if bytecode and block options are used (these options are mandatory)
+options_.verbosity = options_.ep.verbosity;
-if ~( options_.bytecode && options_.block )
+verbosity = options_.ep.verbosity+options_.ep.debug;
-    error('extended_path:: Options bytecode and block are mandatory!')
+
+% Prepare a structure needed by the matlab implementation of the perfect foresight model solver
+pfm.lead_lag_incidence = M_.lead_lag_incidence;
+pfm.ny = M_.endo_nbr;
+pfm.Sigma_e = M_.Sigma_e;
+max_lag = M_.maximum_endo_lag;
+pfm.max_lag = max_lag;
+if pfm.max_lag > 0
+    pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
+    pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
+else
+    pfm.nyp = 0;
+    pfm.iyp = [];
 end
+pfm.ny0 = nnz(pfm.lead_lag_incidence(max_lag+1,:));
+pfm.iy0 = find(pfm.lead_lag_incidence(max_lag+1,:)>0);
+if M_.maximum_endo_lead
+    pfm.nyf = nnz(pfm.lead_lag_incidence(max_lag+2,:));
+    pfm.iyf = find(pfm.lead_lag_incidence(max_lag+2,:)>0);
+else
+    pfm.nyf = 0;
+    pfm.iyf = [];
+end
+pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
+pfm.nrc = pfm.nyf+1;
+pfm.isp = [1:pfm.nyp];
+pfm.is = [pfm.nyp+1:pfm.ny+pfm.nyp];
+pfm.isf = pfm.iyf+pfm.nyp;
+pfm.isf1 = [pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1];
+pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
+pfm.periods = options_.ep.periods;
+pfm.steady_state = oo_.steady_state;
+pfm.params = M_.params;
+if M_.maximum_endo_lead
+    pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(max_lag+(1:2),:)');
+    pfm.i_cols_A1 = find(pfm.lead_lag_incidence(max_lag+(1:2),:)');
+else
+    pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(max_lag+1,:)');
+    pfm.i_cols_A1 = find(pfm.lead_lag_incidence(max_lag+1,:)');
+end
+if max_lag > 0
+    pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1:2,:)');
+else
+    pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1,:)');
+end
+pfm.i_cols_j = 1:pfm.nd;
+pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
+pfm.dynamic_model = str2func([M_.fname,'_dynamic']);
+pfm.verbose = options_.ep.verbosity;
+pfm.maxit_ = options_.maxit_;
+pfm.tolerance = options_.dynatol.f;
+
+exo_nbr = M_.exo_nbr;
+periods = options_.periods;
+ep = options_.ep;
+steady_state = oo_.steady_state;
+dynatol = options_.dynatol;
 
 % Set default initial conditions.
 if isempty(initial_conditions)
@@ -50,28 +101,32 @@ end
 options_.maxit_ = options_.ep.maxit;
 
 % Set the number of periods for the perfect foresight model
-options_.periods = options_.ep.periods;
+periods = options_.ep.periods;
+pfm.periods = options_.ep.periods;
+pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
+
+% keep a copy of pfm.i_upd
+i_upd = pfm.i_upd;
 
 % Set the algorithm for the perfect foresight solver
 options_.stack_solve_algo = options_.ep.stack_solve_algo;
 
+% Set check_stability flag
+do_not_check_stability_flag = ~options_.ep.check_stability;
+
 % Compute the first order reduced form if needed.
 %
 % REMARK. It is assumed that the user did run the same mod file with stoch_simul(order=1) and save
 % all the globals in a mat file called linear_reduced_form.mat;
+
+dr = struct();
 if options_.ep.init
-    lrf = load('linear_reduced_form','oo_');
+    options_.order = 1;
-    oo_.dr = lrf.oo_.dr; clear('lrf');
+    [dr,Info,M_,options_,oo_] = resol(1,M_,options_,oo_);
-    if options_.ep.init==2
-        lambda = .8;
-    end
 end
 
 % Do not use a minimal number of perdiods for the perfect foresight solver (with bytecode and blocks)
-options_.minimal_solving_period = options_.ep.periods;
+options_.minimal_solving_period = 100;%options_.ep.periods;
-
-% Get indices of variables with non zero steady state
-idx = find(abs(oo_.steady_state)>0);
 
 % Initialize the exogenous variables.
 make_ex_;
@@ -83,247 +138,250 @@ make_y_;
 time_series = zeros(M_.endo_nbr,sample_size);
 
 % Set the covariance matrix of the structural innovations.
-variances = diag(M_.Sigma_e); 
+variances = diag(M_.Sigma_e);
-positive_var_indx = find(variances>0); 
+positive_var_indx = find(variances>0);
-covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx); 
+effective_number_of_shocks = length(positive_var_indx);
-number_of_structural_innovations = length(covariance_matrix); 
+stdd = sqrt(variances(positive_var_indx));
-covariance_matrix_upper_cholesky = chol(covariance_matrix); 
+covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx);
+covariance_matrix_upper_cholesky = chol(covariance_matrix);
+
+% (re)Set exo_nbr
+%exo_nbr = effective_number_of_shocks;
 
 % Set seed.
 if options_.ep.set_dynare_seed_to_default
     set_dynare_seed('default');
 end
 
+% Set bytecode flag
+bytecode_flag = options_.ep.use_bytecode;
+
 % Simulate shocks.
 switch options_.ep.innovation_distribution
   case 'gaussian'
-      oo_.ep.shocks = randn(sample_size,number_of_structural_innovations)*covariance_matrix_upper_cholesky; 
+      oo_.ep.shocks = randn(sample_size,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
   otherwise
     error(['extended_path:: ' options_.ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
 end
-    
-% Set future shocks (Stochastic Extended Path approach)
-if options_.ep.stochastic.status
-    switch options_.ep.stochastic.method
-      case 'tensor'
-        [r,w] = gauss_hermite_weights_and_nodes(options_.ep.stochastic.nodes);
-        if options_.ep.stochastic.order*M_.exo_nbr>1
-            for i=1:options_.ep.stochastic.order*M_.exo_nbr
-                rr(i) = {r};
-                ww(i) = {w};
-            end
-            rrr = cartesian_product_of_sets(rr{:});
-            www = cartesian_product_of_sets(ww{:});
-        else
-            rrr = r;
-            www = w;
-        end
-        www = prod(www,2);
-        number_of_nodes = length(www);
-        relative_weights = www/max(www);
-        switch options_.ep.stochastic.pruned.status
-          case 1
-            jdx = find(relative_weights>options_.ep.stochastic.pruned.relative);
-            www = www(jdx);
-            www = www/sum(www);
-            rrr = rrr(jdx,:);
-          case 2
-            jdx = find(weights>options_.ep.stochastic.pruned.level);
-            www = www(jdx);
-            www = www/sum(www);
-            rrr = rrr(jdx,:);
-          otherwise
-            % Nothing to be done!
-        end
-        nnn = length(www);
-      otherwise
-        error('extended_path:: Unknown stochastic_method option!')
-    end
-else
-    rrr = zeros(1,number_of_structural_innovations);
-    www = 1;
-    nnn = 1;
-end
 
 % Initializes some variables.
 t  = 0;
 
 % Set waitbar (graphic or text  mode)
-graphic_waitbar_flag = ~( options_.console_mode || exist('OCTAVE_VERSION') );
+hh = dyn_waitbar(0,'Please wait. Extended Path simulations...');
-
+set(hh,'Name','EP simulations.');
-if graphic_waitbar_flag
-    hh = waitbar(0,['Please wait. Extended Path simulations...']);
-    set(hh,'Name','EP simulations');
-else
-    for i=1:2, disp(' '), end
-    if ~exist('OCTAVE_VERSION')
-       back = [];
-    end
-end
-
 
 % Main loop.
 while (t<sample_size)
     if ~mod(t,10)
-        if graphic_waitbar_flag
+        dyn_waitbar(t/sample_size,hh,'Please wait. Extended Path simulations...');
-            waitbar(t/sample_size);
-        else
-            if exist('OCTAVE_VERSION')
-                printf('Please wait. Extended Path simulations... %3.f%%\r done', 100*t/sample_size);
-            else
-                str = sprintf('Please wait. Extended Path simulations... %3.f%% done.', 100*t/sample_size);
-                fprintf([back '%s'],str);
-                back=repmat('\b',1,length(str));
-            end
-        end
     end
     % Set period index.
     t = t+1;
     shocks = oo_.ep.shocks(t,:);
     % Put it in oo_.exo_simul (second line).
     oo_.exo_simul(2,positive_var_indx) = shocks;
-    for s = 1:nnn
+    periods1 = periods;
-        for u=1:options_.ep.stochastic.order
+    exo_simul_1 = zeros(periods1+2,exo_nbr);
-            oo_.exo_simul(2+u,positive_var_indx) = rrr(s,(((u-1)*M_.exo_nbr)+1):(u*M_.exo_nbr))*covariance_matrix_upper_cholesky;
+    exo_simul_1(2,:) = oo_.exo_simul(2,:);
+    pfm1 = pfm;
+    info_convergence = 0;
+    if ep.init% Compute first order solution (Perturbation)...
+        ex = zeros(size(endo_simul_1,2),size(exo_simul_1,2));
+        ex(1:size(exo_simul_1,1),:) = exo_simul_1;
+        exo_simul_1 = ex;
+        initial_path = simult_(initial_conditions,dr,exo_simul_1(2:end,:),1);
+        endo_simul_1(:,1:end-1) = initial_path(:,1:end-1)*ep.init+endo_simul_1(:,1:end-1)*(1-ep.init);
+    else
+        if t==1
+            endo_simul_1 = repmat(steady_state,1,periods1+2);
         end
-        if options_.ep.init% Compute first order solution... 
+    end
-            initial_path = simult_(initial_conditions,oo_.dr,oo_.exo_simul(2:end,:),1);
+    % Solve a perfect foresight model.
-            if options_.ep.init==1
+    increase_periods = 0;
-                oo_.endo_simul(:,1:end-1) = initial_path(:,1:end-1);% Last column is the steady state.
+    % Keep a copy of endo_simul_1
-            elseif options_.ep.init==2
+    endo_simul = endo_simul_1;
-                oo_.endo_simul(:,1:end-1) = initial_path(:,1:end-1)*lambda+oo_.endo_simul(:,1:end-1)*(1-lambda);
+    while 1
-            end
+        if ~increase_periods
-        end
+            if bytecode_flag
-        % Solve a perfect foresight model (using bytecoded version).
+                oo_.endo_simul = endo_simul_1;
-        increase_periods = 0;
+                oo_.exo_simul = exo_simul_1;
-        endo_simul = oo_.endo_simul;
-        while 1
-            if ~increase_periods
-                t0 = tic;
-                [flag,tmp] = bytecode('dynamic'); 
-                ctime = toc(t0);
-                info.convergence = ~flag;
-                info.time = ctime;
-            end
-            if verbosity
-                if info.convergence
-                    if t<10
-                        disp(['Time:    ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
-                    elseif t<100
-                        disp(['Time:   ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
-                    elseif t<1000
-                        disp(['Time:  ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
-                    else
-                        disp(['Time: ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
-                    end
-                else
-                    if t<10
-                        disp(['Time:    ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
-                    elseif t<100
-                        disp(['Time:   ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
-                    elseif t<1000
-                        disp(['Time:  ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
-                    else
-                        disp(['Time: ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
-                    end
-                end
-            end
-            % Test if periods is big enough.
-            if ~increase_periods &&  max(max(abs(tmp(idx,end-options_.ep.lp:end)./tmp(idx,end-options_.ep.lp-1:end-1)-1)))<options_.dynatol.x
-                break
-            else
-                options_.periods = options_.periods + options_.ep.step;
-                options_.minimal_solving_period = options_.periods;
-                increase_periods = increase_periods + 1;
-                if verbosity
-                    if t<10
-                        disp(['Time:    ' int2str(t)  '. I increase the number of periods to ' int2str(options_.periods) '.'])
-                    elseif t<100
-                        disp(['Time:   ' int2str(t) '. I increase the number of periods to ' int2str(options_.periods) '.'])
-                    elseif t<1000
-                        disp(['Time:  ' int2str(t)  '. I increase the number of periods to ' int2str(options_.periods) '.'])
-                    else
-                        disp(['Time: ' int2str(t)  '. I increase the number of periods to ' int2str(options_.periods) '.'])
-                    end
-                end
-                if info.convergence
-                    oo_.endo_simul = [ tmp , repmat(oo_.steady_state,1,options_.ep.step) ];
-                    oo_.exo_simul  = [ oo_.exo_simul ; zeros(options_.ep.step,size(shocks,2)) ];
-                    tmp_old = tmp;
-                else
-                    oo_.endo_simul = [ oo_.endo_simul , repmat(oo_.steady_state,1,options_.ep.step) ];
-                    oo_.exo_simul  = [ oo_.exo_simul ; zeros(options_.ep.step,size(shocks,2)) ];
-                end
-                t0 = tic;
                 [flag,tmp] = bytecode('dynamic');
-                ctime = toc(t0);
+            else
-                info.time = info.time+ctime;
+                flag = 1;
-                if info.convergence
+            end
-                    maxdiff = max(max(abs(tmp(:,2:options_.ep.fp)-tmp_old(:,2:options_.ep.fp))));
+            if flag
-                    if maxdiff<options_.dynatol.x
+                if options_.ep.stochastic.order == 0
-                        options_.periods = options_.ep.periods;
+                    [flag,tmp,err] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
-                        options_.minimal_solving_period = options_.periods;
-                        oo_.exo_simul = oo_.exo_simul(1:(options_.periods+2),:);
-                        break
-                    end
                 else
-                    info.convergence = ~flag;
+                    [flag,tmp] = solve_stochastic_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1,options_.ep.stochastic.nodes,options_.ep.stochastic.order);
-                    if info.convergence
+                end
-                        continue
+            end
-                    else
+            info_convergence = ~flag;
-                        if increase_periods==10;
+        end
-                            if verbosity
+        if verbosity
-                                if t<10
+            if info_convergence
-                                    disp(['Time:    ' int2str(t)  '. Even with ' int2str(options_.periods) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                if t<10
-                                elseif t<100
+                    disp(['Time:    ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
-                                    disp(['Time:   ' int2str(t)  '. Even with ' int2str(options_.periods) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                elseif t<100
-                                elseif t<1000
+                    disp(['Time:   ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
-                                    disp(['Time:  ' int2str(t)  '. Even with ' int2str(options_.periods) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                elseif t<1000
-                                else
+                    disp(['Time:  ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
-                                    disp(['Time: ' int2str(t)  '. Even with ' int2str(options_.periods) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                else
-                                end
+                    disp(['Time: ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
-                            end
+                end
-                            break
+            else
+                if t<10
+                    disp(['Time:    ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
+                elseif t<100
+                    disp(['Time:   ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
+                elseif t<1000
+                    disp(['Time:  ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
+                else
+                    disp(['Time: ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
+                end
+            end
+        end
+        if do_not_check_stability_flag
+            % Exit from the while loop.
+            endo_simul_1 = tmp;
+            break
+        else
+            % Test if periods is big enough.
+            % Increase the number of periods.
+            periods1 = periods1 + ep.step;
+            pfm1.periods = periods1;
+            pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
+            % Increment the counter.
+            increase_periods = increase_periods + 1;
+            if verbosity
+                if t<10
+                    disp(['Time:    ' int2str(t)  '. I increase the number of periods to ' int2str(periods1) '.'])
+                elseif t<100
+                    disp(['Time:   ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
+                elseif t<1000
+                    disp(['Time:  ' int2str(t)  '. I increase the number of periods to ' int2str(periods1) '.'])
+                else
+                    disp(['Time: ' int2str(t)  '. I increase the number of periods to ' int2str(periods1) '.'])
+                end
+            end
+            if info_convergence
+                % If the previous call to the perfect foresight model solver exited
+                % announcing that the routine converged, adapt the size of endo_simul_1
+                % and exo_simul_1.
+                endo_simul_1 = [ tmp , repmat(steady_state,1,ep.step) ];
+                exo_simul_1  = [ exo_simul_1 ; zeros(ep.step,exo_nbr)];
+                tmp_old = tmp;
+            else
+                % If the previous call to the perfect foresight model solver exited
+                % announcing that the routine did not converge, then tmp=1... Maybe
+                % should change that, because in some circonstances it may usefull
+                % to know where the routine did stop, even if convergence was not
+                % achieved.
+                endo_simul_1 = [ endo_simul_1 , repmat(steady_state,1,ep.step) ];
+                exo_simul_1  = [ exo_simul_1 ; zeros(ep.step,exo_nbr)];
+            end
+            % Solve the perfect foresight model with an increased number of periods.
+            if bytecode_flag
+                oo_.endo_simul = endo_simul_1;
+                oo_.exo_simul = exo_simul_1;
+                [flag,tmp] = bytecode('dynamic');
+            else
+                flag = 1;
+            end
+            if flag
+                if options_.ep.stochastic.order == 0
+                    [flag,tmp,err] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
+                else
+                    [flag,tmp] = solve_stochastic_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1,options_.ep.stochastic.nodes,options_.ep.stochastic.order);
+                end
+            end
+            info_convergence = ~flag;
+            if info_convergence
+                % If the solver achieved convergence, check that simulated paths did not
+                % change during the first periods.
+                % Compute the maximum deviation between old path and new path over the
+                % first periods
+                delta = max(max(abs(tmp(:,2)-tmp_old(:,2))));
+                if delta < dynatol.x
+                    % If the maximum deviation is close enough to zero, reset the number
+                    % of periods to ep.periods
+                    periods1 = ep.periods;
+                    pfm1.periods = periods1;
+                    pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
+                    % Cut exo_simul_1 and endo_simul_1 consistently with the resetted
+                    % number of periods and exit from the while loop.
+                    exo_simul_1 = exo_simul_1(1:(periods1+2),:);
+                    endo_simul_1 = endo_simul_1(:,1:(periods1+2));
+                    break
+                end
+            else
+                % The solver did not converge... Try to solve the model again with a bigger
+                % number of periods, except if the number of periods has been increased more
+                % than 10 times.
+                if increase_periods==10;
+                    if verbosity
+                        if t<10
+                            disp(['Time:    ' int2str(t)  '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                        elseif t<100
+                            disp(['Time:   ' int2str(t)  '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                        elseif t<1000
+                            disp(['Time:  ' int2str(t)  '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                        else
+                            disp(['Time: ' int2str(t)  '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
                         end
                     end
+                    % Exit from the while loop.
+                    break
                 end
-            end
+            end% if info_convergence
         end
-        if ~info.convergence% If the previous step was unsuccesfull, use an homotopic approach
+    end% while
-            [INFO,tmp] = homotopic_steps(.5,.01,t);
+    if ~info_convergence% If exited from the while loop without achieving convergence, use an homotopic approach
-            % Cumulate time.
+        if ~do_not_check_stability_flag
-            info.time = ctime+INFO.time;
+            periods1 = ep.periods;
-            if (~isstruct(INFO) && isnan(INFO)) || ~INFO.convergence
+            pfm1.periods = periods1;
+            pfm1.i_upd = i_upd;
+            exo_simul_1 = exo_simul_1(1:(periods1+2),:);
+            endo_simul_1 = endo_simul_1(:,1:(periods1+2));
+        end
+        [INFO,tmp] = homotopic_steps(endo_simul,exo_simul_1,.5,.01,pfm1);
+        if isstruct(INFO)
+            info_convergence = INFO.convergence;
+        else
+            info_convergence = 0;
+        end
+        if ~info_convergence
+            [INFO,tmp] = homotopic_steps(endo_simul,exo_simul_1,0,.01,pfm1);
+            if isstruct(INFO)
+                info_convergence = INFO.convergence;
+            else
+                info_convergence = 0;
+            end
+            if ~info_convergence
                 disp('Homotopy:: No convergence of the perfect foresight model solver!')
                 error('I am not able to simulate this model!');
             else
-                info.convergence = 1;
+                endo_simul_1 = tmp;
-                oo_.endo_simul = tmp;
+                if verbosity && info_convergence
-                if verbosity && info.convergence
                     disp('Homotopy:: Convergence of the perfect foresight model solver!')
                 end
             end
         else
-            oo_.endo_simul = tmp;
+            info_convergence = 1;
+            endo_simul_1 = tmp;
+            if verbosity && info_convergence
+                disp('Homotopy:: Convergence of the perfect foresight model solver!')
+            end
         end
-        % Save results of the perfect foresight model solver.
-        time_series(:,t) = time_series(:,t)+ www(s)*oo_.endo_simul(:,2);
-        %save('simulated_paths.mat','time_series');
-        % Set initial condition for the nex round.
-        %initial_conditions = oo_.endo_simul(:,2);
     end
-    %oo_.endo_simul = oo_.endo_simul(:,1:options_.periods+M_.maximum_endo_lag+M_.maximum_endo_lead);
+    % Save results of the perfect foresight model solver.
-    oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
+    time_series(:,t) = endo_simul_1(:,2);
-    oo_.endo_simul(:,1) = time_series(:,t);
+    endo_simul_1(:,1:end-1) = endo_simul_1(:,2:end);
-    oo_.endo_simul(:,end) = oo_.steady_state;
+    endo_simul_1(:,1) = time_series(:,t);
-end
+    endo_simul_1(:,end) = oo_.steady_state;
+end% (while) loop over t
 
-if graphic_waitbar_flag
+dyn_waitbar_close(hh);
-    close(hh);
-else
-    if ~exist('OCTAVE_VERSION')
-        fprintf(back);
-    end
-end
 
-oo_.endo_simul = oo_.steady_state;
+oo_.endo_simul = oo_.steady_state;

matlab/ep/extended_path_parfor.m

@@ -0,0 +1,405 @@
+function time_series = extended_path(initial_conditions,sample_size)
+% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
+% series of size T  is obtained by solving T perfect foresight models.
+%
+% INPUTS
+%  o initial_conditions     [double]    m*nlags array, where m is the number of endogenous variables in the model and
+%                                       nlags is the maximum number of lags.
+%  o sample_size            [integer]   scalar, size of the sample to be simulated.
+%
+% OUTPUTS
+%  o time_series            [double]    m*sample_size array, the simulations.
+%
+% ALGORITHM
+%
+% SPECIAL REQUIREMENTS
+
+% Copyright (C) 2009, 2010, 2011 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+global M_ options_ oo_
+
+options_.verbosity = options_.ep.verbosity;
+verbosity = options_.ep.verbosity+options_.ep.debug;
+
+% Prepare a structure needed by the matlab implementation of the perfect foresight model solver
+pfm.lead_lag_incidence = M_.lead_lag_incidence;
+pfm.ny = M_.endo_nbr;
+pfm.max_lag = M_.maximum_endo_lag;
+pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
+pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
+pfm.ny0 = nnz(pfm.lead_lag_incidence(2,:));
+pfm.iy0 = find(pfm.lead_lag_incidence(2,:)>0);
+pfm.nyf = nnz(pfm.lead_lag_incidence(3,:));
+pfm.iyf = find(pfm.lead_lag_incidence(3,:)>0);
+pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
+pfm.nrc = pfm.nyf+1;
+pfm.isp = [1:pfm.nyp];
+pfm.is = [pfm.nyp+1:pfm.ny+pfm.nyp];
+pfm.isf = pfm.iyf+pfm.nyp;
+pfm.isf1 = [pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1];
+pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
+pfm.periods = options_.ep.periods;
+pfm.steady_state = oo_.steady_state;
+pfm.params = M_.params;
+pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(2:3,:)');
+pfm.i_cols_A1 = find(pfm.lead_lag_incidence(2:3,:)');
+pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1:2,:)');
+pfm.i_cols_j = 1:pfm.nd;
+pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
+pfm.dynamic_model = str2func([M_.fname,'_dynamic']);
+pfm.verbose = options_.ep.verbosity;
+pfm.maxit_ = options_.maxit_;
+pfm.tolerance = options_.dynatol.f;
+
+exo_nbr = M_.exo_nbr;
+periods = options_.periods;
+ep = options_.ep;
+steady_state = oo_.steady_state;
+dynatol = options_.dynatol;
+
+% Set default initial conditions.
+if isempty(initial_conditions)
+    initial_conditions = oo_.steady_state;
+end
+
+% Set maximum number of iterations for the deterministic solver.
+options_.maxit_ = options_.ep.maxit;
+
+% Set the number of periods for the perfect foresight model
+periods = options_.ep.periods;
+pfm.periods = options_.ep.periods;
+pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
+
+% Set the algorithm for the perfect foresight solver
+options_.stack_solve_algo = options_.ep.stack_solve_algo;
+
+% Set check_stability flag
+do_not_check_stability_flag = ~options_.ep.check_stability;
+
+
+% Compute the first order reduced form if needed.
+%
+% REMARK. It is assumed that the user did run the same mod file with stoch_simul(order=1) and save
+% all the globals in a mat file called linear_reduced_form.mat;
+
+dr = struct();
+if options_.ep.init
+    options_.order = 1;
+    [dr,Info,M_,options_,oo_] = resol(1,M_,options_,oo_);
+end
+
+% Do not use a minimal number of perdiods for the perfect foresight solver (with bytecode and blocks)
+options_.minimal_solving_period = 100;%options_.ep.periods;
+
+% Initialize the exogenous variables.
+make_ex_;
+
+% Initialize the endogenous variables.
+make_y_;
+
+% Initialize the output array.
+time_series = zeros(M_.endo_nbr,sample_size);
+
+% Set the covariance matrix of the structural innovations.
+variances = diag(M_.Sigma_e);
+positive_var_indx = find(variances>0);
+effective_number_of_shocks = length(positive_var_indx);
+stdd = sqrt(variances(positive_var_indx));
+covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx);
+covariance_matrix_upper_cholesky = chol(covariance_matrix);
+
+% Set seed.
+if options_.ep.set_dynare_seed_to_default
+    set_dynare_seed('default');
+end
+
+% Set bytecode flag
+bytecode_flag = options_.ep.use_bytecode;
+
+% Simulate shocks.
+switch options_.ep.innovation_distribution
+  case 'gaussian'
+      oo_.ep.shocks = randn(sample_size,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
+  otherwise
+    error(['extended_path:: ' options_.ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
+end
+
+% Set future shocks (Stochastic Extended Path approach)
+if options_.ep.stochastic.status
+    switch options_.ep.stochastic.method
+      case 'tensor'
+        switch options_.ep.stochastic.ortpol
+          case 'hermite'
+            [r,w] = gauss_hermite_weights_and_nodes(options_.ep.stochastic.nodes);
+          otherwise
+            error('extended_path:: Unknown orthogonal polynomial option!')
+        end
+        if options_.ep.stochastic.order*M_.exo_nbr>1
+            for i=1:options_.ep.stochastic.order*M_.exo_nbr
+                rr(i) = {r};
+                ww(i) = {w};
+            end
+            rrr = cartesian_product_of_sets(rr{:});
+            www = cartesian_product_of_sets(ww{:});
+        else
+            rrr = r;
+            www = w;
+        end
+        www = prod(www,2);
+        number_of_nodes = length(www);
+        relative_weights = www/max(www);
+        switch options_.ep.stochastic.pruned.status
+          case 1
+            jdx = find(relative_weights>options_.ep.stochastic.pruned.relative);
+            www = www(jdx);
+            www = www/sum(www);
+            rrr = rrr(jdx,:);
+          case 2
+            jdx = find(weights>options_.ep.stochastic.pruned.level);
+            www = www(jdx);
+            www = www/sum(www);
+            rrr = rrr(jdx,:);
+          otherwise
+            % Nothing to be done!
+        end
+        nnn = length(www);
+      otherwise
+        error('extended_path:: Unknown stochastic_method option!')
+    end
+else
+    rrr = zeros(1,effective_number_of_shocks);
+    www = 1;
+    nnn = 1;
+end
+
+% Initializes some variables.
+t  = 0;
+
+% Set waitbar (graphic or text  mode)
+hh = dyn_waitbar(0,'Please wait. Extended Path simulations...');
+set(hh,'Name','EP simulations.');
+
+if options_.ep.memory
+    mArray1 = zeros(M_.endo_nbr,100,nnn,sample_size);
+    mArray2 = zeros(M_.exo_nbr,100,nnn,sample_size);
+end
+
+% Main loop.
+while (t<sample_size)
+    if ~mod(t,10)
+        dyn_waitbar(t/sample_size,hh,'Please wait. Extended Path simulations...');
+    end
+    % Set period index.
+    t = t+1;
+    shocks = oo_.ep.shocks(t,:);
+    % Put it in oo_.exo_simul (second line).
+    oo_.exo_simul(2,positive_var_indx) = shocks;
+    parfor s = 1:nnn
+        periods1 = periods;
+        exo_simul_1 = zeros(periods1+2,exo_nbr);
+        pfm1 = pfm;
+        info_convergence = 0;
+        switch ep.stochastic.ortpol
+          case 'hermite'
+            for u=1:ep.stochastic.order
+                exo_simul_1(2+u,positive_var_indx) = rrr(s,(((u-1)*effective_number_of_shocks)+1):(u*effective_number_of_shocks))*covariance_matrix_upper_cholesky;
+            end
+          otherwise
+            error('extended_path:: Unknown orthogonal polynomial option!')
+        end
+        if ep.stochastic.order && ep.stochastic.scramble
+            exo_simul_1(2+ep.stochastic.order+1:2+ep.stochastic.order+ep.stochastic.scramble,positive_var_indx) = ...
+                randn(ep.stochastic.scramble,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
+        end
+        if ep.init% Compute first order solution (Perturbation)...
+            ex = zeros(size(endo_simul_1,2),size(exo_simul_1,2));
+            ex(1:size(exo_simul_1,1),:) = exo_simul_1;
+            exo_simul_1 = ex;
+            initial_path = simult_(initial_conditions,dr,exo_simul_1(2:end,:),1);
+            endo_simul_1(:,1:end-1) = initial_path(:,1:end-1)*ep.init+endo_simul_1(:,1:end-1)*(1-ep.init);
+        else 
+            endo_simul_1 = repmat(steady_state,1,periods1+2);
+        end
+        % Solve a perfect foresight model.
+        increase_periods = 0;
+        endo_simul = endo_simul_1;
+        while 1
+            if ~increase_periods
+                if bytecode_flag
+                    [flag,tmp] = bytecode('dynamic');
+                else
+                    flag = 1;
+                end
+                if flag
+                    [flag,tmp] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
+                end
+                info_convergence = ~flag;
+            end
+            if verbosity
+                if info_convergence
+                    if t<10
+                        disp(['Time:    ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
+                    elseif t<100
+                        disp(['Time:   ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
+                    elseif t<1000
+                        disp(['Time:  ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
+                    else
+                        disp(['Time: ' int2str(t)  '. Convergence of the perfect foresight model solver!'])
+                    end
+                else
+                    if t<10
+                        disp(['Time:    ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
+                    elseif t<100
+                        disp(['Time:   ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
+                    elseif t<1000
+                        disp(['Time:  ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
+                    else
+                        disp(['Time: ' int2str(t)  '. No convergence of the perfect foresight model solver!'])
+                    end
+                end
+            end
+            if do_not_check_stability_flag
+                % Exit from the while loop.
+                endo_simul_1 = tmp;
+                break
+            else
+                % Test if periods is big enough.
+                % Increase the number of periods.
+                periods1 = periods1 + ep.step;
+                pfm1.periods = periods1;
+                pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
+                % Increment the counter.
+                increase_periods = increase_periods + 1;
+                if verbosity
+                    if t<10
+                        disp(['Time:    ' int2str(t)  '. I increase the number of periods to ' int2str(periods1) '.'])
+                    elseif t<100
+                        disp(['Time:   ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
+                    elseif t<1000
+                        disp(['Time:  ' int2str(t)  '. I increase the number of periods to ' int2str(periods1) '.'])
+                    else
+                        disp(['Time: ' int2str(t)  '. I increase the number of periods to ' int2str(periods1) '.'])
+                    end
+                end
+                if info_convergence
+                    % If the previous call to the perfect foresight model solver exited
+                    % announcing that the routine converged, adapt the size of endo_simul_1
+                    % and exo_simul_1.
+                    endo_simul_1 = [ tmp , repmat(steady_state,1,ep.step) ];
+                    exo_simul_1  = [ exo_simul_1 ; zeros(ep.step,size(shocks,2)) ];
+                    tmp_old = tmp;
+                else
+                    % If the previous call to the perfect foresight model solver exited
+                    % announcing that the routine did not converge, then tmp=1... Maybe
+                    % should change that, because in some circonstances it may usefull
+                    % to know where the routine did stop, even if convergence was not
+                    % achieved.
+                    endo_simul_1 = [ endo_simul_1 , repmat(steady_state,1,ep.step) ];
+                    exo_simul_1  = [ exo_simul_1 ; zeros(ep.step,size(shocks,2)) ];
+                end
+                % Solve the perfect foresight model with an increased number of periods.
+                if bytecode_flag
+                    [flag,tmp] = bytecode('dynamic');
+                else
+                    flag = 1;
+                end
+                if flag
+                    [flag,tmp] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
+                end
+                info_convergence = ~flag;
+                if info_convergence
+                    % If the solver achieved convergence, check that simulated paths did not
+                    % change during the first periods.
+                    % Compute the maximum deviation between old path and new path over the
+                    % first periods
+                    delta = max(max(abs(tmp(:,2:ep.fp)-tmp_old(:,2:ep.fp))));
+                    if delta < dynatol.x
+                        % If the maximum deviation is close enough to zero, reset the number
+                        % of periods to ep.periods
+                        periods1 = ep.periods;
+                        pfm1.periods = periods1;
+                        pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
+                        % Cut exo_simul_1 and endo_simul_1 consistently with the resetted
+                        % number of periods and exit from the while loop.
+                        exo_simul_1 = exo_simul_1(1:(periods1+2),:);
+                        endo_simul_1 = endo_simul_1(:,1:(periods1+2));
+                        break
+                    end
+                else
+                    % The solver did not converge... Try to solve the model again with a bigger
+                    % number of periods, except if the number of periods has been increased more
+                    % than 10 times.
+                    if increase_periods==10;
+                        if verbosity
+                            if t<10
+                                disp(['Time:    ' int2str(t)  '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                            elseif t<100
+                                disp(['Time:   ' int2str(t)  '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                            elseif t<1000
+                                disp(['Time:  ' int2str(t)  '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                            else
+                                disp(['Time: ' int2str(t)  '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
+                            end
+                        end
+                        % Exit from the while loop.
+                        break
+                    end
+                end% if info_convergence
+            end
+        end% while
+        if ~info_convergence% If exited from the while loop without achieving convergence, use an homotopic approach
+            [INFO,tmp] = homotopic_steps(.5,.01,pfm1);
+            if (~isstruct(INFO) && isnan(INFO)) || ~info_convergence
+                [INFO,tmp] = homotopic_steps(0,.01,pfm1);
+                if ~info_convergence
+                    disp('Homotopy:: No convergence of the perfect foresight model solver!')
+                    error('I am not able to simulate this model!');
+                else
+                    info_convergence = 1;
+                    endo_simul_1 = tmp;
+                    if verbosity && info_convergence
+                        disp('Homotopy:: Convergence of the perfect foresight model solver!')
+                    end
+                end
+            else
+                info_convergence = 1;
+                endo_simul_1 = tmp;
+                if verbosity && info_convergence
+                    disp('Homotopy:: Convergence of the perfect foresight model solver!')
+                end
+            end
+        end
+        % Save results of the perfect foresight model solver.
+        if ep.memory
+            mArray1(:,:,s,t) = endo_simul_1(:,1:100);
+            mArrat2(:,:,s,t) = transpose(exo_simul_1(1:100,:));
+        end
+        results(:,s) = www(s)*endo_simul_1(:,2);
+    end
+    time_series(:,t) = sum(results,2);
+    oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
+    oo_.endo_simul(:,1) = time_series(:,t);
+    oo_.endo_simul(:,end) = oo_.steady_state;
+end% (while) loop over t
+
+dyn_waitbar_close(hh);
+
+oo_.endo_simul = oo_.steady_state;
+
+if ep.memory
+    save([M_.fname '_memory'],'mArray1','mArray2','www');
+end

matlab/ep/homotopic_steps.m

@@ -1,20 +1,21 @@
-function [info,tmp] = homotopic_steps(initial_weight,step_length,time)
+function [info,tmp] = homotopic_steps(endo_simul0,exo_simul0,initial_weight,step_length,pfm)
-global oo_ options_ M_
+global options_ oo_
+
+%Set bytecode flag
+bytecode_flag = options_.ep.use_bytecode;
+
+% Set increase and decrease factors.
+increase_factor = 5.0;
+decrease_factor = 0.2;
 
 % Save current state of oo_.endo_simul and oo_.exo_simul.
-endo_simul = oo_.endo_simul;
+endo_simul = endo_simul0;
-exxo_simul = oo_.exo_simul;
+exxo_simul = exo_simul0;
-
-% Reset exo_simul to zero.
-oo_.exo_simul = zeros(size(oo_.exo_simul));
-
 
 initial_step_length = step_length;
 max_iter = 1000/step_length;
 weight   = initial_weight;
-verbose  = 1;
+verbose  = options_.ep.debug;
-iter     = 0;
-ctime    = 0;
 
 reduce_step_flag = 0;
 
@@ -22,183 +23,114 @@ if verbose
     format long
 end
 
-homotopy_1 = 1; % Only innovations are rescaled. Starting from weight equal to initial_weight.
+% (re)Set iter.
-homotopy_2 = 0; % Only innovations are rescaled. Starting from weight equal to zero.
+iter = 0;
-
+% (re)Set iter.
-disp(' ')
+jter = 0;
-
+% (re)Set weight.
-if homotopy_1
+weight = initial_weight;
-    while weight<1
+% (re)Set exo_simul to zero.
-        iter = iter+1;
+exo_simul0 = zeros(size(exo_simul0));
-        oo_.exo_simul(2,:) = weight*exxo_simul(2,:);
+while weight<1
-        t0 = tic;
+    iter = iter+1;
+    exo_simul0(2,:) = weight*exxo_simul(2,:);
+    if bytecode_flag
+        oo_.endo_simul = endo_simul_1;
+        oo_.exo_simul = exo_simul_1;
         [flag,tmp] = bytecode('dynamic');
-        TeaTime = toc(t0);
+    else
-        ctime = ctime+TeaTime;
+        flag = 1;
-        %old_weight = weight;
+    end
-        info.convergence = ~flag;
+    if flag
-        if verbose
+        [flag,tmp] = solve_perfect_foresight_model(endo_simul0,exo_simul0,pfm);
-            if ~info.convergence
+    end
-                disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Convergence problem!' ])
+    info.convergence = ~flag;% Equal to one if the perfect foresight solver converged for the current value of weight.
-            else
+    if verbose
-                disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Ok!' ])
+        if info.convergence
-            end
+            disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Ok!' ])
-        end
-        if ~info.convergence
-            if abs(weight-initial_weight)<1e-12% First iterations.
-                if verbose
-                    disp('I am reducing the initial weight!')
-                end
-                initial_weight = initial_weight/1.1;
-                weight = initial_weight;
-                if weight<1/4
-                    homotopy_1 = 0;
-                    homotopy_2 = 1;
-                    break
-                end
-                continue
-            else% A good initial weight has been obtained. In case of convergence problem we have to reduce the step length.
-                if verbose
-                    disp('I am reducing the step length!')
-                end
-                weight = weight-step_length;
-                step_length=step_length/10;
-                weight = weight+step_length;
-                if 10*step_length<options_.dynatol.x
-                    homotopy_1 = 0;
-                    homotopy_2 = 0;
-                    break
-                end
-                continue
-            end
         else
-            oo_.endo_simul = tmp;
+            disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Convergence problem!' ])
-            info.time = ctime;
-            if abs(1-weight)<=1e-12;
-                homotopy_1 = 0;
-                homotopy_2 = 0;
-                break
-            end
-            weight = weight+step_length;
-            %step_length = initial_step_length;
-        end
-        if iter>max_iter
-            info = NaN;
-            return
         end
     end
-    if weight<1 && homotopy_1
+    if info.convergence
-        oo_.exo_simul(2,:) = exxo_simul(2,:);
+        %if d<stochastic_extended_path_depth
-        t0 = tic; 
+            endo_simul0 = tmp;
+            %end
+        jter = jter + 1;
+        if jter>3
+            if verbose
+                disp('I am increasing the step length!')
+            end
+            step_length=step_length*increase_factor;
+            jter = 0;
+        end
+        if abs(1-weight)<options_.dynatol.x;
+            break
+        end
+        weight = weight+step_length;
+    else% Perfect foresight solver failed for the current value of weight.
+        if initial_weight>0 && abs(weight-initial_weight)<1e-12% First iteration, the initial weight is too high.
+            if verbose
+                disp('I am reducing the initial weight!')
+            end
+            initial_weight = initial_weight/2;
+            weight = initial_weight;
+            if weight<1e-12
+                endo_simul0 = endo_simul;
+                exo_simul0 = exxo_simul;
+                info.convergence = 0;
+                info.depth = d;
+                tmp = [];
+                return
+            end
+            continue
+        else% Initial weight is OK, but the perfect foresight solver failed on some subsequent iteration.
+            if verbose
+                disp('I am reducing the step length!')
+            end
+            jter = 0;
+            if weight>0
+                weight = weight-step_length;
+            end
+            step_length=step_length*decrease_factor;
+            weight = weight+step_length;
+            if step_length<options_.dynatol.x
+                break
+            end
+            continue
+        end
+    end
+    if iter>max_iter
+        info = NaN;
+        return
+    end
+end
+if weight<1
+    exo_simul0 = exxo_simul;
+    if bytecode_flag
+        oo_.endo_simul = endo_simul_1;
+        oo_.exo_simul = exo_simul_1;
         [flag,tmp] = bytecode('dynamic');
-        TeaTime = toc(t0);
+    else
-        ctime = ctime+TeaTime;
+        flag = 1;
-        info.convergence = ~flag;
+    end
-        info.time = ctime;
+    if flag
-        if info.convergence
+        [flag,tmp] = solve_perfect_foresight_model(endo_simul0,exo_simul0,pfm);
-            oo_.endo_simul = tmp;
+    end
-            homotopy_1 = 0;
+    info.convergence = ~flag;
-            homotopy_2 = 0;
+    if info.convergence
+        endo_simul0 = tmp;
+        return
+    else
+        if step_length>options_.dynatol.x
+            endo_simul0 = endo_simul;
+            exo_simul0 = exxo_simul;
+            info.convergence = 0;
+            info.depth = d;
+            tmp = [];
             return
         else
-            if step_length>1e-12
+            error('extended_path::homotopy: Oups! I did my best, but I am not able to simulate this model...')
-                if verbose
-                    disp('I am reducing step length!')
-                end
-                step_length=step_length/2;
-            else
-                weight = initial_weight;
-                step_length = initial_step_length;
-                info = NaN;
-                homotopy_2 = 1;
-                homotopy_1 = 0;
-            end
         end
     end
 end
-
-iter   = 0;
-weight = 0; 
-
-if homotopy_2
-    while weight<1
-        iter = iter+1;
-        oo_.exo_simul(2,:) = weight*exxo_simul(2,:);
-        if time==1
-            oo_.endo_simul = repmat(oo_.steady_state,1,size(oo_.endo_simul,2));
-        else
-            oo_.endo_simul = endo_simul;
-        end
-        t0 = tic;
-        [flag,tmp] = bytecode('dynamic');
-        TeaTime = toc(t0);
-        ctime = ctime+TeaTime;
-        old_weight = weight;
-        info.convergence = ~flag;
-        if verbose
-            if ~info.convergence
-                disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(old_weight,8) ', Convergence problem!' ])
-            else
-                disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(old_weight,8) ', Ok!' ])
-            end
-        end
-        if ~info.convergence
-            if iter==1
-                disp('I am not able to simulate this model!')
-                disp('There is something wrong with the initial condition of the homotopic')
-                disp('approach...')
-                error(' ')
-            else
-                if verbose
-                    disp('I am reducing the step length!')
-                end
-                step_length=step_length/10;
-                if 10*step_length<options_.dynatol.x
-                    homotopy_1 = 0;
-                    homotopy_2 = 0;
-                    break
-                end
-                weight = old_weight+step_length;
-            end
-        else
-            oo_.endo_simul = tmp;
-            info.time = ctime;
-            if abs(1-weight)<=1e-12;
-                homotopy_2 = 1;
-                break
-            end
-            weight = weight+step_length;
-            step_length = initial_step_length;
-        end
-        if iter>max_iter
-            info = NaN;
-            return
-        end
-    end
-    if weight<1 && homotopy_2
-        oo_.exo_simul(2,:) = exxo_simul(2,:);
-        t0 = tic; 
-        [flag,tmp] = bytecode('dynamic');
-        TeaTime = toc(t0);
-        ctime = ctime+TeaTime;
-        info.convergence = ~flag;
-        info.time = ctime;
-        if info.convergence
-            oo_.endo_simul = tmp;
-            homotopy_1 = 0;
-        else
-            if step_length>1e-12
-                if verbose
-                    disp('I am reducing step length!')
-                end
-                step_length=step_length/2;
-            else
-                weight = initial_weight;
-                step_length = initial_step_length;
-                info = NaN;
-                homotopy_2 = 1;
-                homotopy_1 = 0;
-            end
-        end
-    end
-end

matlab/evaluate_static_model.m

@@ -53,6 +53,10 @@ function [residuals,check1,jacob] = evaluate_static_model(ys,exo_ss,params,M,opt
                 % have zero residuals by construction
                 if ~isempty(mfsb)
                     residuals(mfsb) = feval(fh_static,b,ys,exo_ss,params);
+                else
+                    %need to evaluate the recursive blocks to compute the
+                    %temporary terms
+                    feval(fh_static,b,ys,exo_ss,params);
                 end
             end
         else

matlab/evaluate_steady_state.m

@@ -1,7 +1,7 @@
 function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadystate_check_flag)
 % function [ys,info] = evaluate_steady_state(M,options,oo)
-% Computes the steady state 
+% Computes the steady state
-%  
+%
 % INPUTS
 %   ys_init                   vector           initial values used to compute the steady
 %                                                 state
@@ -10,8 +10,8 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
 %   oo                        struct           output results
 %   steadystate_check_flag    boolean          if true, check that the
 %                                              steadystate verifies the
-%                                              static model         
+%                                              static model
-%  
+%
 % OUTPUTS
 %   ys                        vector           steady state
 %   params                    vector           model parameters possibly
@@ -41,23 +41,27 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
 
     info = 0;
     check = 0;
-    
+
     steadystate_flag = options.steadystate_flag;
     params = M.params;
     exo_ss = [oo.exo_steady_state; oo.exo_det_steady_state];
     updated_params_flag = 0;
-    
+
     if length(M.aux_vars) > 0
-        h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);                       
+        h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
         ys_init = h_set_auxiliary_variables(ys_init,exo_ss,M.params);
     end
-    
+
     if options.ramsey_policy
         [ys,params] = dyn_ramsey_static(ys_init,M,options,oo);
     elseif steadystate_flag
         % explicit steady state file
         [ys,params1,check] = evaluate_steady_state_file(ys_init,exo_ss,params,M.fname,steadystate_flag);
-        updated_params_flag = max(abs(params1-params)) > 1e-12;
+        if ~length(find(isnan(params))) && ~length(find(isnan(params1)))
+            updated_params_flag = max(abs(params1-params))>1e-12;
+        elseif length(find(isnan(params))) && ~length(find(isnan(params1)))
+            updated_params_flag = 1;
+        end
         if updated_params_flag
             params = params1;
         end
@@ -88,7 +92,7 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
             end
             [ys,check] = dynare_solve('restricted_steadystate',...
                                                     ys(indv),...
-                                                    options.jacobian_flag, ...         
+                                                    options.jacobian_flag, ...
                                                     exo_ss,indv);
         end
     elseif (options.bytecode == 0 && options.block == 0)
@@ -96,7 +100,7 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
             % non linear model
             [ys,check] = dynare_solve([M.fname '_static'],...
                                       ys_init,...
-                                      options.jacobian_flag, ...     
+                                      options.jacobian_flag, ...
                                       exo_ss, params);
         else
             % linear model
@@ -140,13 +144,13 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
         return
     end
 
-    if options.steadystate_flag && updated_params_flag && ~isreal(M.params)
+    if options.steadystate_flag && updated_params_flag && ~isreal(params)
         info(1) = 23;
-        info(2) = sum(imag(M.params).^2);
+        info(2) = sum(imag(params).^2);
         return
     end
 
-    if options.steadystate_flag && updated_params_flag  && ~isempty(find(isnan(M.params)))
+    if options.steadystate_flag && updated_params_flag  && ~isempty(find(isnan(params)))
         info(1) = 24;
         info(2) = NaN;
         return

matlab/gauss_legendre_weights_and_nodes.m

@@ -0,0 +1,132 @@
+function [nodes,weights] = gauss_legendre_weights_and_nodes(n,a,b)
+% Computes the weights and nodes for a Legendre Gaussian quadrature rule.
+
+%@info:
+%! @deftypefn {Function File} {@var{nodes}, @var{weights} =} gauss_hermite_weights_and_nodes (@var{n})
+%! @anchor{gauss_legendre_weights_and_nodes}
+%! @sp 1
+%! Computes the weights and nodes for a Legendre Gaussian quadrature rule. designed to approximate integrals
+%! on the finite interval (-1,1) of an unweighted smooth function.
+%! @sp 2
+%! @strong{Inputs}
+%! @sp 1
+%! @table @ @var
+%! @item n
+%! Positive integer scalar, number of nodes (order of approximation).
+%! @item a
+%! Double scalar, lower bound.
+%! @item b
+%! Double scalar, upper bound.
+%! @end table
+%! @sp 1
+%! @strong{Outputs}
+%! @sp 1
+%! @table @ @var
+%! @item nodes
+%! n*1 vector of doubles, the nodes (roots of an order n Legendre polynomial)
+%! @item weights
+%! n*1 vector of doubles, the associated weights.
+%! @end table
+%! @sp 2
+%! @strong{Remarks:}
+%! Only the first input argument (the number of nodes) is mandatory. The second and third input arguments
+%! are used if a change of variables is necessary (ie if we need nodes over the interval [a,b] instead of
+%! of the default interval [-1,1]).
+%! @sp 2
+%! @strong{This function is called by:}
+%! @sp 2
+%! @strong{This function calls:}
+%! @sp 2
+%! @end deftypefn
+%@eod:
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
+
+bb = sqrt(1./(4-(1./transpose(1:n-1)).^2));
+aa = zeros(n,1);
+JacobiMatrix = diag(bb,1)+diag(aa)+diag(bb,-1);
+[JacobiEigenVectors,JacobiEigenValues] = eig(JacobiMatrix);
+[nodes,idx] = sort(diag(JacobiEigenValues));
+JacobiEigenVector = JacobiEigenVectors(1,:);
+JacobiEigenVector = transpose(JacobiEigenVector(idx));
+weights = 2*JacobiEigenVector.^2;
+
+if nargin==3
+    weights = .5*(b-a)*weights;
+    nodes = .5*(nodes+1)*(b-a)+a;
+end
+
+%@test:1
+%$ [n2,w2] = gauss_legendre_weights_and_nodes(2);
+%$ [n3,w3] = gauss_legendre_weights_and_nodes(3);
+%$ [n4,w4] = gauss_legendre_weights_and_nodes(4);
+%$ [n5,w5] = gauss_legendre_weights_and_nodes(5);
+%$ [n7,w7] = gauss_legendre_weights_and_nodes(7);
+%$
+%$
+%$ % Expected nodes (taken from Judd (1998, table 7.2)).
+%$ e2 = .5773502691; e2 = [-e2; e2];
+%$ e3 = .7745966692; e3 = [-e3; 0 ; e3];
+%$ e4 = [.8611363115; .3399810435]; e4 = [-e4; flipud(e4)];
+%$ e5 = [.9061798459; .5384693101]; e5 = [-e5; 0; flipud(e5)];
+%$ e7 = [.9491079123; .7415311855; .4058451513]; e7 = [-e7; 0; flipud(e7)];
+%$
+%$ % Expected weights (taken from Judd (1998, table 7.2) and http://en.wikipedia.org/wiki/Gaussian_quadrature).
+%$ f2 = [1; 1];
+%$ f3 = [5; 8; 5]/9;
+%$ f4 = [18-sqrt(30); 18+sqrt(30)]; f4 = [f4; flipud(f4)]/36;
+%$ f5 = [322-13*sqrt(70); 322+13*sqrt(70)]/900; f5 = [f5; 128/225; flipud(f5)];
+%$ f7 = [.1294849661; .2797053914; .3818300505]; f7 = [f7; .4179591836; flipud(f7)];
+%$
+%$ % Check the results.
+%$ t(1) = dyn_assert(e2,n2,1e-9);
+%$ t(2) = dyn_assert(e3,n3,1e-9);
+%$ t(3) = dyn_assert(e4,n4,1e-9);
+%$ t(4) = dyn_assert(e5,n5,1e-9);
+%$ t(5) = dyn_assert(e7,n7,1e-9);
+%$ t(6) = dyn_assert(w2,f2,1e-9);
+%$ t(7) = dyn_assert(w3,f3,1e-9);
+%$ t(8) = dyn_assert(w4,f4,1e-9);
+%$ t(9) = dyn_assert(w5,f5,1e-9);
+%$ t(10) = dyn_assert(w7,f7,1e-9);
+%$ T = all(t);
+%@eof:1
+
+%@test:2
+%$ nmax = 50;
+%$
+%$ t = zeros(nmax,1);
+%$
+%$ for i=1:nmax
+%$     [n,w] = gauss_legendre_weights_and_nodes(i);
+%$     t(i) = dyn_assert(sum(w),2,1e-12);
+%$ end
+%$
+%$ T = all(t);
+%@eof:2
+
+%@test:3
+%$ [n,w] = gauss_legendre_weights_and_nodes(9,pi,2*pi);
+%$ % Check that the 
+%$ t(1) = all(n>pi);
+%$ t(2) = all(n<2*pi);
+%$ t(3) = dyn_assert(sum(w),pi,1e-12);
+%$ T = all(t);
+%@eof:3

matlab/gensylv_fp.m

@@ -0,0 +1,73 @@
+function X = gensylv_fp(A, B, C, D, block)
+% function X = gensylv_fp(A, B, C, D)
+% Solve the Sylvester equation:
+% A * X + B * X * C + D = 0
+% INPUTS
+%   A
+%   B
+%   C
+%   D
+%   block : block number (for storage purpose) 
+% OUTPUTS
+%   X solution
+%    
+% ALGORITHM
+%   fixed point method
+%   MARLLINY MONSALVE (2008): "Block linear method for large scale
+%   Sylvester equations", Computational & Applied Mathematics, Vol 27, n°1,
+%   p47-59
+%   ||A^-1||.||B||.||C|| < 1 is a suffisant condition:
+%    - to get a unique solution for the Sylvester equation
+%    - to get a convergent fixed-point algorithm
+%
+% SPECIAL REQUIREMENTS
+%   none.  
+% Copyright (C) 1996-2010 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+%tol = 1e-07;
+%tol = 1e-13;
+tol = 1e-12;
+evol = 100;
+A1 = inv(A);
+eval(['persistent hxo_' int2str(block) ';']);
+hxo = eval(['hxo_' int2str(block) ';']);
+if isempty(hxo)
+    X = zeros(size(B, 2), size(C, 1));
+else
+    X = hxo;
+end;
+it_fp = 0;
+maxit_fp = 1000;
+Z = - (B * X * C + D);
+while it_fp < maxit_fp && evol > tol;
+    %X_old = X;
+    %X = - A1 * ( B * X * C + D);
+    %evol = max(max(abs(X - X_old)));
+    X = A1 * Z;
+    Z_old = Z;
+    Z = - (B * X * C + D);
+    evol = max(sum(abs(Z - Z_old))); %norm_1
+    %evol = max(sum(abs(Z - Z_old)')); %norm_inf
+    it_fp = it_fp + 1;
+end;
+%fprintf('sylvester it_fp=%d evol=%g | ',it_fp,evol);
+if evol < tol
+    eval(['hxo_' int2str(block) ' = X;']);
+else
+    error(['convergence not achieved in fixed point solution of Sylvester equation after ' int2str(it_fp) ' iterations']);
+end;

matlab/global_initialization.m

@@ -11,7 +11,7 @@ function global_initialization()
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright (C) 2003-2011 Dynare Team
+% Copyright (C) 2003-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -41,11 +41,13 @@ options_.terminal_condition = 0;
 options_.rplottype = 0;
 options_.smpl = 0;
 options_.dynatol.f = 1e-5;
-options_.dynatol.x = 1e-5; 
+options_.dynatol.x = 1e-5;
 options_.maxit_ = 10;
 options_.slowc = 1;
 options_.timing = 0;
-options_.gstep = 1e-2;
+options_.gstep = ones(2,1);
+options_.gstep(1) = 1e-2;
+options_.gstep(2) = 1.0;
 options_.scalv = 1;
 options_.debug = 0;
 options_.initval_file = 0;
@@ -57,9 +59,12 @@ options_.solve_tolf = eps^(1/3);
 options_.solve_tolx = eps^(2/3);
 options_.solve_maxit = 500;
 
-% Default number of threads for parallelized mex files.  
+options_.mode_check_neighbourhood_size = 0.5;
+
+% Default number of threads for parallelized mex files.
 options_.threads.kronecker.A_times_B_kronecker_C = 1;
 options_.threads.kronecker.sparse_hessian_times_B_kronecker_C = 1;
+options_.threads.local_state_space_iteration_2 = 1;
 
 % steady state
 options_.jacobian_flag = 1;
@@ -109,10 +114,16 @@ options_.SpectralDensity = 0;
 
 % Extended path options
 %
+% Set debug flag
+ep.debug = 0;
+% Set memory flag
+ep.memory = 0;
 % Set verbose mode
 ep.verbosity = 0;
+% Set bytecode flag
+ep.use_bytecode = 0;
 % Initialization of the perfect foresight equilibrium paths
-% * init=0, previous solution is used.  
+% * init=0, previous solution is used.
 % * init=1, a path generated with the first order reduced form is used.
 % * init=2, mix of cases 0 and 1.
 ep.init = 0;
@@ -122,10 +133,12 @@ ep.maxit = 500;
 ep.periods = 200;
 % Default step for increasing the number of periods if needed
 ep.step = 50;
+% Set check_stability flag
+ep.check_stability = 0;
 % Define last periods used to test if the solution is stable with respect to an increase in the number of periods.
 ep.lp = 5;
 % Define first periods used to test if the solution is stable with respect to an increase in the number of periods.
-ep.fp = 100;
+ep.fp = 2;
 % Define the distribution for the structural innovations.
 ep.innovation_distribution = 'gaussian';
 % Set flag for the seed
@@ -133,9 +146,9 @@ ep.set_dynare_seed_to_default = 1;
 % Set algorithm for the perfect foresight solver
 ep.stack_solve_algo = 4;
 % Stochastic extended path related options.
-ep.stochastic.status = 0;
 ep.stochastic.method = 'tensor';
-ep.stochastic.order = 1;
+ep.stochastic.ortpol = 'hermite';
+ep.stochastic.order = 0;
 ep.stochastic.nodes = 5;
 ep.stochastic.pruned.status = 0;
 ep.stochastic.pruned.relative = 1e-5;
@@ -146,37 +159,39 @@ options_.ep = ep;
 
 % Particle filter
 %
-% Default is that we do not use the non linear kalman filter 
+% Default is that we do not use the non linear kalman filter
 particle.status = 0;
 % How do we initialize the states?
 particle.initialization = 1;
-particle_filter.initial_state_prior_std = .0001;
+particle.initial_state_prior_std = .0001;
-% Set the default order of approximation of the model (perturbation). 
+% Set the default order of approximation of the model (perturbation).
-particle_filter.perturbation = 2;
+particle.perturbation = 2;
 % Set the default number of particles.
-particle_filter.number_of_particles = 500;
+particle.number_of_particles = 500;
 % Set the default approximation order (Smolyak)
-particle_filter.smolyak_accuracy = 3;
+particle.smolyak_accuracy = 3;
 % By default we don't use pruning
-particle_filter.pruning = 0;
+particle.pruning = 0;
 % Set default algorithm
-particle_filter.algorithm = 'sequential_importance_particle_filter';
+particle.algorithm = 'sequential_importance_particle_filter';
-% Set the Gaussian approximation method 
+% Set the Gaussian approximation method
-particle_filter.approximation_method = 'unscented';
+particle.approximation_method = 'unscented';
 % Set unscented parameters alpha, beta and kappa for gaussian approximation
-particle_filter.unscented.alpha = 1 ;
+particle.unscented.alpha = 1;
-particle_filter.unscented.beta = 2 ;
+particle.unscented.beta = 2;
-particle_filter.unscented.kappa = 1 ;
+particle.unscented.kappa = 1;
 % Configuration of resampling in case of particles
-particle_filter.resampling = 'systematic' ;
+particle.resampling.status = 'systematic'; % 'generic'
-% Choice of the resampling method 
+particle.resampling.neff_threshold = .5;
-particle_filter.resampling_method = 'traditional' ;
+% Choice of the resampling method
-% Configuration of the mixture filters 
+particle.resampling.method1 = 'traditional' ;
-particle_filter.mixture_method = 'particles' ;
+particle.resampling.method2 = 'kitagawa';
+% Configuration of the mixture filters
+particle.mixture_method = 'particles' ;
 % Size of the different mixtures
-particle_filter.mixture_state_variables = 5 ;
+particle.mixture_state_variables = 5 ;
-particle_filter.mixture_structural_shocks = 1 ;
+particle.mixture_structural_shocks = 1 ;
-particle_filter.mixture_measurement_shocks = 1 ;
+particle.mixture_measurement_shocks = 1 ;
 % Copy ep structure in options_ global structure
 options_.particle = particle;
 
@@ -205,9 +220,9 @@ options_.solve_algo = 2;
 options_.linear = 0;
 options_.replic = 50;
 options_.drop = 100;
-% if mjdgges.dll (or .mexw32 or ....) doesn't exist, matlab/qz is added to the path. 
+% if mjdgges.dll (or .mexw32 or ....) doesn't exist, matlab/qz is added to the path.
-% There exists now qz/mjdgges.m that contains the calls to the old Sims code 
+% There exists now qz/mjdgges.m that contains the calls to the old Sims code
-% Hence, if mjdgges.m is visible exist(...)==2, 
+% Hence, if mjdgges.m is visible exist(...)==2,
 % this means that the DLL isn't avaiable and use_qzdiv is set to 1
 if exist('mjdgges','file')==2
     options_.use_qzdiv = 1;
@@ -225,9 +240,9 @@ options_.ramsey_policy = 0;
 options_.timeless = 0;
 
 % estimation
-estimation_info.prior = struct('name', {}, 'shape', {}, 'mean', {}, ...
+estimation_info.parameters.prior = struct('name', {}, 'shape', {}, 'mean', {}, ...
-                               'mode', {}, 'stdev', {}, 'date1', {}, ...
+                                          'mode', {}, 'stdev', {}, 'date1', {}, ...
-                               'date2', {}, 'shift', {}, 'variance', {});
+                                          'date2', {}, 'shift', {}, 'variance', {});
 estimation_info.structural_innovation.prior = struct('name', {}, 'shape', {}, 'mean', {}, ...
                                                   'mode', {}, 'stdev', {}, 'date1', {}, ...
                                                   'date2', {}, 'shift', {}, 'variance', {});
@@ -240,10 +255,12 @@ estimation_info.measurement_error.prior = struct('name', {}, 'shape', {}, 'mean'
 estimation_info.measurement_error_corr.prior = struct('name', {}, 'shape', {}, 'mean', {}, ...
                                                   'mode', {}, 'stdev', {}, 'date1', {}, ...
                                                   'date2', {}, 'shift', {}, 'variance', {});
+estimation_info.parameters.prior_index = {};
 estimation_info.measurement_error.prior_index = {};
 estimation_info.structural_innovation.prior_index = {};
 estimation_info.measurement_error_corr.prior_index = {};
 estimation_info.structural_innovation_corr.prior_index = {};
+estimation_info.parameters.options_index = {};
 estimation_info.measurement_error.options_index = {};
 estimation_info.structural_innovation.options_index = {};
 estimation_info.measurement_error_corr.options_index = {};
@@ -322,6 +339,7 @@ options_.filter_covariance = 0;
 options_.filter_decomposition = 0;
 options_.selected_variables_only = 0;
 options_.initialize_estimated_parameters_with_the_prior_mode = 0;
+options_.estimation_dll = 0;
 % Misc
 options_.conf_sig = 0.6;
 oo_.exo_simul = [];
@@ -341,9 +359,19 @@ M_.bvar = [];
 options_.homotopy_mode = 0;
 options_.homotopy_steps = 1;
 
-% Simplex routine (variation on Nelder Mead algorithm)
+% Simplex optimization routine (variation on Nelder Mead algorithm).
 options_.simplex = [];
 
+% CMAES optimization routine.
+cmaes.SaveVariables='off';
+cmaes.DispFinal='on';
+cmaes.WarnOnEqualFunctionValues='no';
+cmaes.DispModulo='10';
+cmaes.LogModulo='0';
+cmaes.LogTime='0';
+options_.cmaes = cmaes;
+
+
 % prior analysis
 options_.prior_mc = 20000;
 options_.prior_analysis_endo_var_list = [];
@@ -357,6 +385,14 @@ options_.use_dll = 0;
 % model evaluated using bytecode.dll
 options_.bytecode = 0;
 
+% use a fixed point method to solve Sylvester equation (for large scale
+% models)
+options_.sylvester_fp = 0;
+
+% use a fixed point method to solve Lyapunov equation (for large scale
+% models)
+options_.lyapunov_fp = 0;
+
 % dates for historical time series
 options_.initial_date.freq = 1;
 options_.initial_date.period = 1;

matlab/gsa/Morris_Measure_Groups.m

@@ -1,6 +1,4 @@
-
 function [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group)
-
 % [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group)
 %
 % Given the Morris sample matrix, the output values and the group matrix compute the Morris measures
@@ -11,12 +9,12 @@ function [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p,
 % Each column represents one group. 
 % The element of each column are zero if the factor is not in the
 % group. Otherwise it is 1.
-
+%
 % Sample := Matrix of the Morris sampled trajectories 
-
+%
 % Output := Matrix of the output(s) values in correspondence of each point
 % of each trajectory
-
+%
 % k = Number of factors
 % -------------------------------------------------------------------------
 % OUTPUTS 
@@ -24,6 +22,23 @@ function [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p,
 % for each output it gives the three measures of each factor
 % -------------------------------------------------------------------------
 
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 if nargin==0,
   disp(' ')
   disp('[SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group);')

matlab/gsa/Sampling_Function_2.m

@@ -50,8 +50,23 @@ function [Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
 %
 %   F. Campolongo, J. Cariboni, JRC - IPSC Ispra, Varese, IT
 %   Last Update: 15 November 2005 by J.Cariboni
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 % Parameters and initialisation of the output matrix
 sizea = k;

matlab/gsa/cumplot.m

@@ -1,7 +1,22 @@
 function h = cumplot(x);
 %function h =cumplot(x)
-% Copyright (C) 2005 Marco Ratto
 
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 n=length(x);
 x=[-inf; sort(x); Inf];

matlab/gsa/dat_fil_.m

@@ -1,20 +1,28 @@
 function c = dat_fil_(data_file);
-%
+% Written by Marco Ratto
-% Part of the Sensitivity Analysis Toolbox for DYNARE
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 try
   eval(data_file);

matlab/gsa/filt_mc_.m

@@ -1,6 +1,5 @@
 function [rmse_MC, ixx] = filt_mc_(OutDir,data_info)
 % function [rmse_MC, ixx] = filt_mc_(OutDir)
-% copyright Marco Ratto 2006
 % inputs (from opt_gsa structure)
 % vvarvecm = options_gsa_.var_rmse;
 % loadSA   = options_gsa_.load_rmse;
@@ -10,21 +9,29 @@ function [rmse_MC, ixx] = filt_mc_(OutDir,data_info)
 % istart   = options_gsa_.istart_rmse;
 % alphaPC  = 0.5;
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it
-%
-% Disclaimer: This software is not subject to copyright protection and is in the public domain.
-% It is an experimental system. The Joint Research Centre of European Commission
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 global bayestopt_ estim_params_ M_ options_ oo_
 

matlab/gsa/ghx2transition.m

@@ -1,24 +1,30 @@
 function [A,B] = ghx2transition(mm,iv,ic,aux)
 % [A,B] = ghx2transition(mm,iv,ic,aux)
 %
-% Adapted by M. Ratto from kalman_transition_matrix.m 
+% Adapted by M. Ratto (from kalman_transition_matrix.m)
-% (kalman_transition_matrix.m is part of DYNARE, copyright M. Juillard)
-%
-% Part of the Sensitivity Analysis Toolbox for DYNARE
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 global oo_ M_
 

matlab/gsa/gsa_plotmatrix.m

@@ -2,7 +2,7 @@ function gsa_plotmatrix(type,varargin)
 % function gsa_plotmatrix(type,varargin)
 % extended version of the standard MATLAB plotmatrix 
 
-% Copyright (C) 2011-2011 Dynare Team
+% Copyright (C) 2011-2012 Dynare Team
 %
 % This file is part of Dynare.
 %

matlab/gsa/gsa_skewness.m

@@ -1,5 +1,22 @@
 function s=gsa_skewness(y),
 
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 % y=stand_(y);
 % s=mean(y.^3);
     m2=mean((y-mean(y)).^2);

matlab/gsa/gsa_speed.m

@@ -1,21 +1,30 @@
 function [tadj, iff] = gsa_speed(A,B,mf,p),
 % [tadj, iff] = gsa_speed(A,B,mf,p),
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 nvar=length(mf);
 nstate= size(A,1);

matlab/gsa/log_trans_.m

@@ -1,5 +1,22 @@
 function [yy, xdir, isig, lam]=log_trans_(y0,xdir0)
 
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 if nargin==1,
   xdir0='';
 end

matlab/gsa/map_ident_.m

@@ -1,4 +1,22 @@
 function map_ident_(OutputDirectoryName)
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 global bayestopt_ M_ options_ estim_params_ oo_
 
 opt_gsa = options_.opt_gsa;

matlab/gsa/mc_moments.m

@@ -1,4 +1,22 @@
 function [vdec, cc, ac] = mc_moments(mm, ss, dr)
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 global options_ M_
 
   [nr1, nc1, nsam] = size(mm);

matlab/gsa/myboxplot.m

@@ -1,8 +1,23 @@
-
 function sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
-
 %  sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
 
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 % % % % endif
 if nargin < 5 | isempty(maxwhisker), maxwhisker = 1.5; end
 if nargin < 4 | isempty(vertical), vertical = 1; end

matlab/gsa/myprctilecol.m

@@ -1,4 +1,22 @@
 function y = myprctilecol(x,p);
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 xx = sort(x);
 [m,n] = size(x);
 

matlab/gsa/prior_draw_gsa.m

@@ -17,22 +17,30 @@ function pdraw = prior_draw_gsa(init,rdraw)
 % SPECIAL REQUIREMENTS
 %   MATLAB Statistics Toolbox
 %  
-%  
+% Written by Marco Ratto
-% Part of the Sensitivity Analysis Toolbox for DYNARE
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 % global M_ options_ estim_params_  bayestopt_
 global bayestopt_

matlab/gsa/priorcdf.m

@@ -8,6 +8,23 @@ function [xcum] = priorcdf(para, pshape, p6, p7, p3, p4)
 %         5 is UNIFORM [p1,p2]
 % Adapted by M. Ratto from MJ priordens.m
 
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 nprio 	= length(pshape);
 
 i = 1;

matlab/gsa/read_data.m

@@ -1,19 +1,28 @@
 function [gend, data] = read_data
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 global options_ bayestopt_
 

matlab/gsa/redform_map.m

@@ -11,21 +11,31 @@ function redform_map(dirname)
 % ksstat      = options_gsa_.ksstat_redform;
 % alpha2      = options_gsa_.alpha2_redform;
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 
 global M_ oo_ estim_params_ options_ bayestopt_
 

matlab/gsa/redform_screen.m

@@ -6,21 +6,30 @@ function redform_screen(dirname)
 % anamexo     = options_gsa_.namexo;
 % iload       = options_gsa_.load_redform;
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain.
-% It is an experimental system. The Joint Research Centre of European Commission
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 global M_ oo_ estim_params_ options_ bayestopt_
 

matlab/gsa/set_shocks_param.m

@@ -1,5 +1,23 @@
 function set_shocks_param(xparam1)
-  global estim_params_ M_
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+global estim_params_ M_
   
   nvx = estim_params_.nvx;
   ncx = estim_params_.ncx;

matlab/gsa/smirnov.m

@@ -2,23 +2,30 @@ function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
 % Smirnov test for 2 distributions
 %   [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
-
+% This file is part of Dynare.
-
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 if nargin<3
     alpha  =  0.05;

matlab/gsa/stab_map_.m

@@ -30,21 +30,30 @@ function x0 = stab_map_(OutputDirectoryName)
 %
 % USES qmc_sequence, stab_map_1, stab_map_2
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain.
-% It is an experimental system. The Joint Research Centre of European Commission
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 %global bayestopt_ estim_params_ dr_ options_ ys_ fname_
 global bayestopt_ estim_params_ options_ oo_ M_
@@ -79,6 +88,7 @@ nshock = nshock + estim_params_.nvn;
 nshock = nshock + estim_params_.ncx;
 nshock = nshock + estim_params_.ncn;
 lpmat0=[];
+xparam1=[];
 
 pshape = bayestopt_.pshape(nshock+1:end);
 p1 = bayestopt_.p1(nshock+1:end);
@@ -387,6 +397,9 @@ else
     end
     load(filetoload,'lpmat','lpmat0','iunstable','istable','iindeterm','iwrong','egg','yys','nspred','nboth','nfwrd')
     Nsam = size(lpmat,1);
+    if pprior==0,
+        eval(['load ' options_.mode_file '.mat;']);
+    end
 
 
     if prepSA & isempty(strmatch('T',who('-file', filetoload),'exact')),
@@ -522,18 +535,18 @@ if length(iunstable)>0 & length(iunstable)<Nsam,
     c0=corrcoef(lpmat(istable,:));
     c00=tril(c0,-1);
 
-    stab_map_2(lpmat(istable,:),alpha2, pvalue_corr, asname, OutputDirectoryName);
+    stab_map_2(lpmat(istable,:),alpha2, pvalue_corr, asname, OutputDirectoryName,xparam1);
     if length(iunstable)>10,
-        stab_map_2(lpmat(iunstable,:),alpha2, pvalue_corr, auname, OutputDirectoryName);
+        stab_map_2(lpmat(iunstable,:),alpha2, pvalue_corr, auname, OutputDirectoryName,xparam1);
     end
     if length(iindeterm)>10,
-        stab_map_2(lpmat(iindeterm,:),alpha2, pvalue_corr, aindname, OutputDirectoryName);
+        stab_map_2(lpmat(iindeterm,:),alpha2, pvalue_corr, aindname, OutputDirectoryName,xparam1);
     end
     if length(ixun)>10,
-        stab_map_2(lpmat(ixun,:),alpha2, pvalue_corr, aunstname, OutputDirectoryName);
+        stab_map_2(lpmat(ixun,:),alpha2, pvalue_corr, aunstname, OutputDirectoryName,xparam1);
     end
     if length(iwrong)>10,
-        stab_map_2(lpmat(iwrong,:),alpha2, pvalue_corr, awrongname, OutputDirectoryName);
+        stab_map_2(lpmat(iwrong,:),alpha2, pvalue_corr, awrongname, OutputDirectoryName,xparam1);
     end
 
     x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock);

matlab/gsa/stab_map_1.m

@@ -16,21 +16,30 @@ function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, i
 %        solid lines for NON BEHAVIOURAL
 % USES smirnov
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 global estim_params_ bayestopt_ M_ options_
 

matlab/gsa/stab_map_2.m

@@ -1,21 +1,30 @@
-function stab_map_2(x,alpha2, pvalue, fnam, dirname)
+function stab_map_2(x,alpha2, pvalue, fnam, dirname,xparam1)
 % function stab_map_2(x, alpha2, pvalue, fnam, dirname)
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 %global bayestopt_ estim_params_ dr_ options_ ys_ fname_
 global bayestopt_ estim_params_ options_ oo_ M_
@@ -29,6 +38,9 @@ end
 if nargin<5,
   dirname='';
 end
+if nargin<6,
+  xparam1=[];
+end
 
 ys_ = oo_.dr.ys;
 dr_ = oo_.dr;
@@ -46,6 +58,9 @@ ifig=0;
 j2=0;
 if ishock==0
   npar=estim_params_.np;
+  if ~isempty(xparam1),
+      xparam1=xparam1(nshock+1:end);
+  end
 else
   npar=estim_params_.np+nshock;
 end
@@ -68,6 +83,9 @@ for j=1:npar,
       %plot(stock_par(ixx(nfilt+1:end,i),j),stock_par(ixx(nfilt+1:end,i),i2(jx)),'.k')
       %hold on, 
       plot(x(:,j),x(:,i2(jx)),'.')
+      if ~isempty(xparam1)
+          hold on, plot(xparam1(j),xparam1(i2(jx)),'ro')
+      end
       %             xlabel(deblank(estim_params_.param_names(j,:)),'interpreter','none'), 
       %             ylabel(deblank(estim_params_.param_names(i2(jx),:)),'interpreter','none'), 
       if ishock,

matlab/gsa/stand_.m

@@ -9,9 +9,24 @@ function [y, meany, stdy] = stand_(x)
 % my: Vector of mean values for each column of y
 % sy: Vector of standard deviations for each column of y
 %
-% Copyright (c) 2006 by JRC, European Commission, United Kingdom
 % Author : Marco Ratto
 
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 if nargin==0,
     return;

matlab/gsa/tcrit.m

@@ -1,19 +1,15 @@
 function t_crit = tcrit(n,pval0)
-
 % function t_crit = tcrit(n,pval0)
 %
 % given the p-value pval0, the function givese the 
 % critical value t_crit of the t-distribution with n degress of freedom 
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
-%
 % Written by Marco Ratto
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
-%
 
-% Copyright (C) 2011-2011 Dynare Team
+% Copyright (C) 2011-2012 Dynare Team
 %
 % This file is part of Dynare.
 %

matlab/gsa/teff.m

@@ -1,22 +1,30 @@
 function [yt, j0, ir, ic]=teff(T,Nsam,istable)
-%
 % [yt, j0, ir, ic]=teff(T,Nsam,istable)
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 ndim = (length(size(T)));
 if ndim==3,

matlab/gsa/th_moments.m

@@ -1,6 +1,22 @@
-% Copyright (C) 2001 Michel Juillard
-%
 function [vdec, corr, autocorr, z, zz] = th_moments(dr,var_list)
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
   global M_ oo_ options_
   
   nvar = size(var_list,1);

matlab/gsa/thet2tau.m

@@ -1,4 +1,22 @@
 function tau = thet2tau(params, indx, indexo, flagmoments,mf,nlags,useautocorr)
+
+% Copyright (C) 2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
 global M_ oo_ options_
 
 if nargin==1,

matlab/gsa/trank.m

@@ -2,21 +2,30 @@ function yr = trank(y);
 % yr = trank(y);
 % yr is the rank transformation of y
 %
-% Part of the Sensitivity Analysis Toolbox for DYNARE
+% Written by Marco Ratto
-%
-% Written by Marco Ratto, 2006
 % Joint Research Centre, The European Commission,
 % (http://eemc.jrc.ec.europa.eu/),
 % marco.ratto@jrc.it 
 %
-% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
-% It is an experimental system. The Joint Research Centre of European Commission 
-% assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
 % Reference:
 % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
+
+% Copyright (C) 2012 Dynare Team
 %
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 [nr, nc] = size(y);
 for j=1:nc,

matlab/hessian.m

@@ -39,7 +39,7 @@ if ~isa(func, 'function_handle')
     func = str2func(func);
 end
 n=size(x,1);
-h1=max(abs(x),sqrt(gstep)*ones(n,1))*eps^(1/6);
+h1=max(abs(x),sqrt(gstep(1))*ones(n,1))*eps^(1/6)*gstep(2);
 h_1=h1;
 xh1=x+h1;
 h1=xh1-x;

matlab/homotopy1.m

@@ -77,5 +77,5 @@ for i=1:step_nbr+1
     oo_.exo_steady_state(values(ix,2)) = points(ix,i);
     oo_.exo_det_steady_state(values(ixd,2)) = points(ixd,i);
 
-    steady_(M_,options_,oo_);
+    oo_.steady_state = steady_(M_,options_,oo_);
 end

matlab/homotopy2.m

@@ -100,6 +100,6 @@ for i = 1:nv
 
         disp([ 'HOMOTOPY mode 2: lauching solver with ' deblank(varname) ' = ' num2str(v) ' ...'])
         
-        steady_(M_,options_,oo_);
+        oo_.steady_state = steady_(M_,options_,oo_);
     end
 end

matlab/homotopy3.m

@@ -91,7 +91,7 @@ while iter < step_nbr
     old_ss = oo_.steady_state;
 
     try
-        steady_(M_,options_,oo_);
+        oo_.steady_state = steady_(M_,options_,oo_);
         
         if length([kplus; kminus]) == nv
             return

matlab/initial_estimation_checks.m

@@ -42,6 +42,12 @@ if DynareOptions.dsge_var
     [fval,cost_flag,info] = DsgeVarLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 else
     [fval,cost_flag,ys,trend_coeff,info] = dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    if DynareOptions.mode_compute == 5
+        % this call is necessary to initialized persistent variable
+        % 'penalty' in dsge_likelihood_hh
+        [fval,llik,cost_flag,ys,trend_coeff,info] = ...
+            dsge_likelihood_hh(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    end
 end
 
 if info(1) > 0

matlab/k_order_pert.m

@@ -1,7 +1,7 @@
 function [dr,info] = k_order_pert(dr,M,options,oo)
 % Compute decision rules using the k-order DLL from Dynare++
 
-% Copyright (C) 2009-2010 Dynare Team
+% Copyright (C) 2009-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -26,20 +26,17 @@ order = options.order;
 
 switch(order)
   case 1
-    [err, g_1] = k_order_perturbation(dr,M,options, ['.' ...
+    [err, g_1] = k_order_perturbation(dr,M,options);
-                        mexext]);
     mexErrCheck('k_order_perturbation', err);
     dr.g_1 = g_1;
   case 2
-    [err, g_0, g_1, g_2] = k_order_perturbation(dr,M,options, ['.' ...
+    [err, g_0, g_1, g_2] = k_order_perturbation(dr,M,options);
-                        mexext]);
     mexErrCheck('k_order_perturbation', err);
     dr.g_0 = g_0;
     dr.g_1 = g_1;
     dr.g_2 = g_2;
   case 3
-    [err, g_0, g_1, g_2, g_3] = k_order_perturbation(dr,M,options, ['.' ...
+    [err, g_0, g_1, g_2, g_3] = k_order_perturbation(dr,M,options);
-                        mexext]);
     mexErrCheck('k_order_perturbation', err);
     dr.g_0 = g_0;
     dr.g_1 = g_1;

matlab/kalman/likelihood/kalman_filter.m

@@ -169,9 +169,8 @@ if t<last
 end
 
 % Compute minus the log-likelihood.
-if presample
+if presample > diffuse_periods
-    if presample>=diffuse_periods
+    LIK = sum(likk(1+presample-diffuse_periods:end));
-        likk = likk(1+(presample-diffuse_periods):end);
+else
-    end
+    LIK = sum(likk);
-end
+end
-LIK = sum(likk);

matlab/kalman/likelihood/kalman_filter_d.m

@@ -111,14 +111,4 @@ end
 dlik = dlik(1:s);
 dlik = .5*(dlik + pp*log(2*pi));
 
-if presample 
+dLIK = sum(dlik(1+presample:end));
-    if presample>=length(dlik)
-        dLIK = 0;
-        dlik = [];
-    else
-        dlik = dlik(1+presample:end);
-        dLIK = sum(dlik);% Minus the log-likelihood (for the initial periods).
-    end
-else
-    dLIK = sum(dlik);
-end

matlab/kalman/likelihood/missing_observations_kalman_filter.m

@@ -139,9 +139,8 @@ if t<last
 end
 
 % Compute minus the log-likelihood.
-if presample
+if presample>=diffuse_periods
-    if presample>=diffuse_periods
+    LIK = sum(lik(1+presample-diffuse_periods:end));
-        lik = lik(1+(presample-diffuse_periods):end);
+else
-    end
+    LIK = sum(lik);
-end
+end
-LIK = sum(lik);

matlab/kalman/likelihood/missing_observations_kalman_filter_d.m

@@ -125,14 +125,4 @@ end
 
 dlik = .5*dlik(1:s);
 
-if presample 
+dLIK = sum(dlik(1+presample:end));
-    if presample>=length(dlik)
-        dLIK = 0;
-        dlik = [];
-    else
-        dlik = dlik(1+presample:end);
-        dLIK = sum(dlik);% Minus the log-likelihood (for the initial periods).
-    end
-else
-    dLIK = sum(dlik);
-end

matlab/kalman/likelihood/univariate_kalman_filter.m

@@ -167,9 +167,8 @@ if t<last
 end
 
 % Compute minus the log-likelihood.
-if presample
+if presample > diffuse_periods
-    if presample>=diffuse_periods
+    LIK = sum(lik(1+presample-diffuse_periods:end));
-        lik = lik(1+(presample-diffuse_periods):end);
+else
-    end
+    LIK = sum(lik);
-end
+end
-LIK = sum(lik);

matlab/kalman/likelihood/univariate_kalman_filter_d.m

@@ -164,16 +164,4 @@ end
 dlikk = .5*dlikk(1:s);
 llik  = .5*llik(1:s,:);
 
-if presample
+dLIK = sum(dlikk(1+presample:end));
-    if presample>=length(dlik)
-        dLIK = 0;
-        dlikk= [];
-        llik = [];
-    else
-        dlikk= dlikk(1+presample:end);
-        llik = llik(1+presample:end,:);
-        dLIK = sum(dlikk);% Minus the log-likelihood (for the initial periods).
-    end
-else
-    dLIK = sum(dlikk);
-end

matlab/lyapunov_symm.m

@@ -1,4 +1,4 @@
-function [x,u] = lyapunov_symm(a,b,qz_criterium,lyapunov_complex_threshold,method)
+function [x,u] = lyapunov_symm(a,b,qz_criterium,lyapunov_complex_threshold,method, R)
 % Solves the Lyapunov equation x-a*x*a' = b, for b and x symmetric matrices.
 % If a has some unit roots, the function computes only the solution of the stable subsystem.
 %  
@@ -12,6 +12,7 @@ function [x,u] = lyapunov_symm(a,b,qz_criterium,lyapunov_complex_threshold,metho
 %                                                               variables and the schur decomposition is triggered.    
 %                                                      method=2 then U, T, n and k are declared as persistent 
 %                                                               variables and the schur decomposition is not performed.
+%                                                      method=3 fixed point method
 % OUTPUTS
 %   x:      [double]    m*m solution matrix of the lyapunov equation, where m is the dimension of the stable subsystem.
 %   u:      [double]    Schur vectors associated with unit roots  
@@ -38,10 +39,55 @@ function [x,u] = lyapunov_symm(a,b,qz_criterium,lyapunov_complex_threshold,metho
 %
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-
 if nargin<5
     method = 0;
 end
+
+if method == 3
+    persistent X method1;
+    if ~isempty(method1)
+        method = method1;
+    end;
+    fprintf(' [methode=%d] ',method);
+    if method == 3
+        %tol = 1e-8;
+        tol = 1e-10;
+        it_fp = 0;
+        evol = 100;
+        if isempty(X)
+            X = b;
+            max_it_fp = 2000;
+        else
+            max_it_fp = 300;
+        end;
+        at = a';
+        %fixed point iterations
+        while evol > tol && it_fp < max_it_fp;
+            X_old = X;
+            X = a * X * at + b;
+            evol = max(sum(abs(X - X_old))); %norm_1
+            %evol = max(sum(abs(X - X_old)')); %norm_inf
+            it_fp = it_fp + 1;
+        end;
+        fprintf('lyapunov it_fp=%d evol=%g\n',it_fp,evol);
+        if it_fp >= max_it_fp
+            disp(['convergence not achieved in solution of Lyapunov equation after ' int2str(it_fp) ' iterations, switching method from 3 to 0']);
+            method1 = 0;
+            method = 0;
+        else
+            method1 = 3;
+            x = X;
+            return;
+        end;
+    end;
+elseif method == 4
+    % works only with Matlab System Control toolbox
+    chol_b = R*chol(b,'lower');
+    Rx = dlyapchol(a,chol_b);
+    x = Rx' * Rx;
+    return;
+end;
+
 if method
     persistent U T k n
 else
@@ -113,4 +159,4 @@ if i == 1
     x(1,1) = (B(1,1)+c)/(1-T(1,1)*T(1,1));
 end
 x = U(:,k+1:end)*x*U(:,k+1:end)';
-u = U(:,1:k);
+u = U(:,1:k);

matlab/mode_check.m

@@ -1,24 +1,46 @@
 function mode_check(fun,x,hessian,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+% Checks the estimated ML mode or Posterior mode.
 
-% function mode_check(x,fval,hessian,gend,data,lb,ub)
+%@info:
-% Checks the maximum likelihood mode
+%! @deftypefn {Function File} mode_check (@var{fun}, @var{x}, @var{hessian}, @var{DynareDataset}, @var{DynareOptions}, @var{Model}, @var{EstimatedParameters}, @var{BayesInfo}, @var{DynareResults})
-%
+%! @anchor{mode_check}
-% INPUTS
+%! @sp 1
-%    x:       mode
+%! Checks the estimated ML mode or Posterior mode by plotting sections of the likelihood/posterior kernel.
-%    fval:    value at the maximum likelihood mode
+%! Each plot shows the variation of the likelihood implied by the variations of a single parameter, ceteris paribus)
-%    hessian: matrix of second order partial derivatives
+%! @sp 2
-%    gend:    scalar specifying the number of observations
+%! @strong{Inputs}
-%    data:    matrix of data
+%! @sp 1
-%    lb:      lower bound
+%! @table @ @var
-%    ub:      upper bound
+%! @item fun
-%
+%! Objective function.
-% OUTPUTS
+%! @item x
-%    none
+%! Estimated mode.
-%
+%! @item start
-% SPECIAL REQUIREMENTS
+%! Hessian of the objective function at the estimated mode @var{x}.
-%    none
+%! @item DynareDataset
+%! Structure specifying the dataset used for estimation (dataset_).
+%! @item DynareOptions
+%! Structure defining dynare's options (options_).
+%! @item Model
+%! Structure specifying the (estimated) model (M_).
+%! @item EstimatedParameters
+%! Structure specifying the estimated parameters (estimated_params_).
+%! @item BayesInfo
+%! Structure containing information about the priors used for estimation (bayestopt_).
+%! @item DynareResults
+%! Structure gathering the results (oo_).
+%! @end table
+%! @sp 2
+%! @strong{Outputs}
+%! @sp 2
+%! @strong{This function is called by:}
+%! @sp 2
+%! @strong{This function calls:}
+%! The objective function (@var{func}).
+%! @end deftypefn
+%@eod:
 
-% Copyright (C) 2003-2010 Dynare Team
+% Copyright (C) 2003-2010, 2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -62,6 +84,8 @@ if TeX
     fprintf(fidTeX,' \n');
 end
 
+ll = DynareOptions.mode_check_neighbourhood_size;
+
 for plt = 1:nbplt,
     if TeX
         NAMES = [];
@@ -82,9 +106,18 @@ for plt = 1:nbplt,
             end
         end
         xx = x;
-        l1 = max(BayesInfo.lb(kk),0.5*x(kk));
+        l1 = max(BayesInfo.lb(kk),(1-ll)*x(kk)); m1 = 0;
-        l2 = min(BayesInfo.ub(kk),1.5*x(kk));
+        l2 = min(BayesInfo.ub(kk),(1+ll)*x(kk));
-        z = [l1:(l2-l1)/20:l2];
+        if l2<(1+ll)*x(kk)
+            l1 = x(kk) - (l2-x(kk));
+            m1 = 1;
+        end
+        if ~m1 && (l1>(1-ll)*x(kk)) && (x(kk)+(x(kk)-l1)<BayesInfo.ub(kk))
+            l2 = x(kk) + (x(kk)-l1);
+        end
+        z1 = l1:((x(kk)-l1)/10):x(kk);
+        z2 = x(kk):((l2-x(kk))/10):l2;
+        z  = union(z1,z2);
         if DynareOptions.mode_check_nolik==0,
             y = zeros(length(z),2);
             dy = priordens(xx,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);

matlab/mr_gstep.m

@@ -59,6 +59,7 @@ while i<n
         if gg(i)*(hh(i)*gg(i))/2 > htol
             [f0 x fc retcode] = csminit(func0,x,f0,gg,0,diag(hh),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
             ig(i)=1;
+            fprintf(['Done for param %s = %8.4f\n'],BayesInfo.name{i},x(i))
         end
         xh1=x;
     end
@@ -67,4 +68,3 @@ end
 
 save gstep.mat x h1 f0
 
-

matlab/ms-sbvar/cstz/fn_foregraph.m

@@ -39,8 +39,7 @@ hornum = cell(length(vyrs),1);    % horizontal year (number)
 count=0;
 for k=vyrs'
    count=count+1;
-   jnk=num2str(k);
+   hornum{count}=num2str(k);
-   hornum{count}=jnk(3:4);   % e.g., with '1990', we have '90'
 end
 
 count=0;

matlab/ms-sbvar/cstz/fn_rnrprior_covres_dobs.m

@@ -141,6 +141,8 @@ for i = 1:lags
          sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j);  % ith equation
       elseif (q_m==4)
          sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j);  % ith equation
+      elseif (q_m==1)
+         sgpbid((i-1)*nvar+j) = (1/(i*4)^mu(4))^2/sgsh(j);  % ith equation
       else
 			error('Incompatibility with lags, check the possible errors!!!')
          %warning('Incompatibility with lags, check the possible errors!!!')

matlab/ms-sbvar/cstz/fn_rnrprior_covres_dobs_tv2.m

@@ -163,6 +163,8 @@ for i = 1:lags
          sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j);  % ith equation
       elseif (q_m==4)
          sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j);  % ith equation
+      elseif (q_m==1)
+         sgpbid((i-1)*nvar+j) = (1/(i*4)^mu(4))^2/sgsh(j);  % ith equation
       else
 			error('Incompatibility with lags, check the possible errors!!!')
          %warning('Incompatibility with lags, check the possible errors!!!')

matlab/ms-sbvar/initialize_ms_sbvar_options.m

@@ -12,7 +12,7 @@ function options_=initialize_ms_sbvar_options(M_, options_)
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -100,12 +100,15 @@ options_.ms.real_time_smoothed_probabilities = 0;
 % irf
 options_.ms.horizon = 12;
 options_.ms.filtered_probabilities = 0;
-options_.ms.error_bands = 1;
 options_.ms.percentiles = [.16 .5 .84];
 options_.ms.parameter_uncertainty = 0;
 options_.ms.shock_draws = 10000;
 options_.ms.shocks_per_parameter = 10;
 options_.ms.median = 0;
+options_.ms.regime = 0;
+options_.ms.regimes = 0;
 % forecast
 options_.ms.forecast_data_obs = 0;
+% variance decomposition
+options_.ms.error_bands = 1;
 end

matlab/ms-sbvar/ms_forecast.m

@@ -14,7 +14,7 @@ function [options_, oo_]=ms_forecast(M_, options_, oo_)
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -34,46 +34,86 @@ function [options_, oo_]=ms_forecast(M_, options_, oo_)
 disp('MS-SBVAR Forecasts');
 options_ = set_file_tags(options_);
 [options_, oo_] = set_ms_estimation_file(options_.ms.file_tag, options_, oo_);
-options_ = set_ms_simulation_file(options_);
+clean_ms_forecast_files(options_.ms.output_file_tag);
-clean_files_for_second_type_of_mex(M_, options_, 'forecast')
 forecastdir = [options_.ms.output_file_tag filesep 'Forecast'];
 create_dir(forecastdir);
 
-opt = { ...
+% setup command line options
-    {'file_tag', options_.ms.file_tag}, ...
+opt = ['-forecast -nodate -seed ' num2str(options_.DynareRandomStreams.seed)];
-    {'seed', options_.DynareRandomStreams.seed}, ...
+opt = [opt ' -do ' forecastdir];
-    {'horizon', options_.ms.horizon}, ...
+opt = [opt ' -ft ' options_.ms.file_tag];
-    {'number_observations', options_.ms.forecast_data_obs}, ...
+opt = [opt ' -fto ' options_.ms.output_file_tag];
-    {'error_bands', options_.ms.error_bands}, ...
+opt = [opt ' -horizon ' num2str(options_.ms.horizon)];
-    {'percentiles', options_.ms.percentiles}, ...
+opt = [opt ' -thin ' num2str(options_.ms.thinning_factor)];
-    {'thin', options_.ms.thinning_factor}
+opt = [opt ' -data ' num2str(options_.ms.forecast_data_obs)];
-    };
 
+if options_.ms.regimes
+    opt = [opt ' -regimes'];
+elseif options_.ms.regime
+    % regime-1 since regime is 0-indexed in C but 1-indexed in Matlab
+    opt = [opt ' -regime ' num2str(options_.ms.regime-1)];
+end
+
+if options_.ms.parameter_uncertainty
+    options_ = set_ms_simulation_file(options_);
+    opt = [opt ' -parameter_uncertainty'];
+    opt = [opt ' -shocks_per_parameter ' num2str(options_.ms.shocks_per_parameter)];
+else
+    opt = [opt ' -shocks_per_parameter ' num2str(options_.ms.shock_draws)];
+end
+
+percentiles_size = 0;
 if options_.ms.median
-    opt = [opt(:)' {{'median'}}];
+    percentiles_size = 1;
+    opt = [opt ' -percentiles ' num2str(percentiles_size) ' 0.5'];
+else
+    percentiles_size = size(options_.ms.percentiles,2);
+    opt = [opt ' -percentiles ' num2str(percentiles_size)];
+    for i=1:size(options_.ms.percentiles,2)
+        opt = [opt ' ' num2str(options_.ms.percentiles(i))];
+    end
 end
 
-[err, forecast] = mex_ms_forecast([opt(:)', {{'free_parameters',oo_.ms.maxparams}, ...
+% forecast
-    {'shocks_per_parameter', options_.ms.shock_draws}}]);
+[err] = ms_sbvar_command_line(opt);
-mexErrCheck('mex_ms_forecast ergodic ', err);
+mexErrCheck('ms_forecast',err);
-plot_ms_forecast(M_,options_,forecast,'Forecast',options_.graph_save_formats,options_.TeX);
 
-[err, regime_forecast] = mex_ms_forecast([opt(:)', {{'free_parameters',oo_.ms.maxparams}, ...
+% Plot Forecasts
-    {'shocks_per_parameter', options_.ms.shock_draws}, {'regimes'}}]);
+if options_.ms.regimes
-mexErrCheck('mex_ms_forecast ergodic regimes', err);
+    n_chains = length(options_.ms.ms_chain);
-save([forecastdir filesep 'ergodic_forecast.mat'], 'forecast', 'regime_forecast');
+    n_regimes=1;
+    for i_chain=1:n_chains
+        n_regimes = n_regimes*length(options_.ms.ms_chain(i_chain).regime);
+    end
 
-if exist(options_.ms.mh_file,'file') > 0
+    for regime_i=1:n_regimes
-    [err, forecast] = mex_ms_forecast([opt(:)', {{'free_parameters',oo_.ms.maxparams}, ...
+        forecast_title = ['Forecast, Regimes ' num2str(regime_i)];
-        {'shocks_per_parameter', options_.ms.shocks_per_parameter}, ...
+        forecast_data = load([forecastdir filesep 'forecasts_percentiles_regime_' ...
-        {'simulation_file', options_.ms.mh_file}, {'parameter_uncertainty'}}]);
+            num2str(regime_i-1) '_' options_.ms.output_file_tag ...
-    mexErrCheck('mex_ms_forecast bayesian ', err);
+            '.out'], '-ascii');
-    plot_ms_forecast(M_,options_,forecast,'Forecast w/ Parameter Uncertainty',options_.graph_save_formats,options_.TeX);
+        forecast_data = reshape_ascii_forecast_data(M_.endo_nbr, ...
+            percentiles_size, options_.ms.horizon, forecast_data);
+        save([forecastdir filesep 'forecast_regime_' num2str(regime_i-1)], ...
+            'forecast_data');
+        plot_ms_forecast(M_, options_, forecast_data, forecast_title);
+    end
+else
+    if options_.ms.regime
+        forecast_data = load([forecastdir filesep 'forecasts_percentiles_regime_' ...
+            num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
+            '.out'], '-ascii');
+        forecast_title = ['Forecast, Regime ' num2str(options_.ms.regime)];
+        save_filename = ['forecast_regime_' num2str(options_.ms.regime-1)];
+    else
+        forecast_data = load([forecastdir filesep 'forecasts_percentiles_' ...
+            options_.ms.output_file_tag '.out'], '-ascii');
+        forecast_title = 'Forecast';
+        save_filename = 'forecast';
+    end
 
-    [err, regime_forecast] = mex_ms_forecast([opt(:)', {{'free_parameters',oo_.ms.maxparams}, ...
+    forecast_data = reshape_ascii_forecast_data(M_.endo_nbr, ...
-        {'shocks_per_parameter', options_.ms.shocks_per_parameter}, ...
+        percentiles_size, options_.ms.horizon, forecast_data);
-        {'simulation_file', options_.ms.mh_file}, {'parameter_uncertainty','regimes'}}]);
+    save([forecastdir filesep save_filename], 'forecast_data');
-    mexErrCheck('mex_ms_forecast bayesian regimes ', err);
+    plot_ms_forecast(M_, options_, forecast_data, forecast_title);
-    save([forecastdir filesep 'bayesian_forecast.mat'], 'forecast', 'regime_forecast');
 end
 end

matlab/ms-sbvar/ms_irf.m

@@ -15,7 +15,7 @@ function [options_, oo_]=ms_irf(varlist, M_, options_, oo_)
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -35,50 +35,91 @@ function [options_, oo_]=ms_irf(varlist, M_, options_, oo_)
 disp('MS-SBVAR Impulse Response Function');
 options_ = set_file_tags(options_);
 [options_, oo_] = set_ms_estimation_file(options_.ms.file_tag, options_, oo_);
-options_ = set_ms_simulation_file(options_);
+clean_ms_irf_files(options_.ms.output_file_tag);
-clean_files_for_second_type_of_mex(M_, options_, 'irf')
 irfdir = [options_.ms.output_file_tag filesep 'IRF'];
 create_dir(irfdir);
 
-opt = { ...
+% setup command line options
-    {'file_tag', options_.ms.file_tag}, ...
+opt = ['-ir -seed ' num2str(options_.DynareRandomStreams.seed)];
-    {'seed', options_.DynareRandomStreams.seed}, ...
+opt = [opt ' -do ' irfdir];
-    {'horizon', options_.ms.horizon}, ...
+opt = [opt ' -ft ' options_.ms.file_tag];
-    {'filtered', options_.ms.filtered_probabilities}, ...
+opt = [opt ' -fto ' options_.ms.output_file_tag];
-    {'error_bands', options_.ms.error_bands}, ...
+opt = [opt ' -horizon ' num2str(options_.ms.horizon)];
-    {'percentiles', options_.ms.percentiles}, ...
+opt = [opt ' -thin ' num2str(options_.ms.thinning_factor)];
-    {'thin', options_.ms.thinning_factor}
-    };
 
+if options_.ms.regimes
+    opt = [opt ' -regimes'];
+elseif options_.ms.regime
+    % regime-1 since regime is 0-indexed in C but 1-indexed in Matlab
+    opt = [opt ' -regime ' num2str(options_.ms.regime-1)];
+elseif options_.ms.filtered_probabilities
+    opt = [opt ' -filtered'];
+end
+
+if options_.ms.parameter_uncertainty
+    options_ = set_ms_simulation_file(options_);
+    opt = [opt ' -parameter_uncertainty'];
+    opt = [opt ' -shocks_per_parameter ' num2str(options_.ms.shocks_per_parameter)];
+else
+    opt = [opt ' -shocks_per_parameter ' num2str(options_.ms.shock_draws)];
+end
+
+percentiles_size = 0;
 if options_.ms.median
-    opt = [opt(:)' {{'median'}}];
+    percentiles_size = 1;
-end
+    opt = [opt ' -percentiles ' num2str(percentiles_size) ' 0.5'];
-
+else
-[err, irf] = mex_ms_irf([opt(:)', {{'free_parameters', oo_.ms.maxparams}, {'shocks_per_parameter', options_.ms.shock_draws}}]);
+    percentiles_size = size(options_.ms.percentiles,2);
-mexErrCheck('mex_ms_irf ergodic ', err);
+    opt = [opt ' -percentiles ' num2str(percentiles_size)];
-plot_ms_irf(M_,options_,irf,options_.varobs,'Ergodic Impulse Responses',varlist);
+    for i=1:size(options_.ms.percentiles,2)
-
+        opt = [opt ' ' num2str(options_.ms.percentiles(i))];
-[err, regime_irfs] = mex_ms_irf([opt(:)', {{'free_parameters',oo_.ms.maxparams}, {'shocks_per_parameter', options_.ms.shock_draws}, {'regimes'}}]);
-mexErrCheck('mex_ms_irf ergodic regimes ',err);
-for i=1:size(regime_irfs,1)
-    plot_ms_irf(M_,options_,squeeze(regime_irfs(i,:,:,:)),options_.varobs,['Ergodic ' ...
-                        'Impulse Responses State ' int2str(i)],varlist);
-end
-save([irfdir filesep 'ergodic_irf.mat'], 'irf', 'regime_irfs');
-
-if exist(options_.ms.mh_file,'file') > 0
-    [err, irf] = mex_ms_irf([opt(:)', {{'shocks_per_parameter', options_.ms.shocks_per_parameter}, ...
-        {'parameter_uncertainty'},{'simulation_file',options_.ms.mh_file}}]);
-    mexErrCheck('mex_ms_irf bayesian ',err);
-    plot_ms_irf(M_,options_,irf,options_.varobs,'Impulse Responses with Parameter Uncertainty',varlist);
-    
-    [err, regime_irfs] = mex_ms_irf([opt(:)', {{'shocks_per_parameter', options_.ms.shocks_per_parameter}, ...
-        {'simulation_file',options_.ms.mh_file},{'parameter_uncertainty'},{'regimes'}}]);
-    mexErrCheck('mex_ms_irf bayesian regimes ',err);
-    for i=1:size(regime_irfs,1)
-        plot_ms_irf(M_,options_,squeeze(regime_irfs(i,:,:,:)),options_.varobs,['Impulse ' ...
-                            'Responses with Parameter Uncertainty State ' int2str(i)],varlist);
     end
-    save([irfdir filesep 'bayesian_irf.mat'], 'irf', 'regime_irfs');
+end
+
+% irf
+[err] = ms_sbvar_command_line(opt);
+mexErrCheck('ms_irf',err);
+
+% Plot IRFs
+if options_.ms.regimes
+    n_chains = length(options_.ms.ms_chain);
+    n_regimes=1;
+    for i_chain=1:n_chains
+        n_regimes = n_regimes*length(options_.ms.ms_chain(i_chain).regime);
+    end
+
+    for regime_i=1:n_regimes
+        irf_title = ['Impulse Responses, Regime ' num2str(regime_i)];
+        irf_data = load([irfdir filesep 'ir_percentiles_regime_' ...
+            num2str(regime_i-1) '_' options_.ms.output_file_tag ...
+            '.out'], '-ascii');
+        irf_data = reshape_ascii_irf_data(M_.endo_nbr, percentiles_size, ...
+            options_.ms.horizon, irf_data);
+        save([irfdir filesep 'irf_regime_' num2str(regime_i-1)], 'irf_data');
+        plot_ms_irf(M_, options_, irf_data, irf_title, varlist);
+    end
+else
+    if options_.ms.regime
+        irf_data = load([irfdir filesep 'ir_percentiles_regime_' ...
+            num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
+            '.out'], '-ascii');
+        irf_title = ['Impulse Response, Regime ' num2str(options_.ms.regime)];
+        save_filename = ['irf_regime_' num2str(options_.ms.regime-1)];
+    elseif options_.ms.filtered_probabilities
+        irf_data = load([irfdir filesep 'ir_percentiles_filtered_' ...
+            options_.ms.output_file_tag '.out'], '-ascii');
+        irf_title = 'Impulse Response Filtered';
+        save_filename = 'irf';
+    else
+        irf_data = load([irfdir filesep 'ir_percentiles_ergodic_' ...
+            options_.ms.output_file_tag '.out'], '-ascii');
+        irf_title = 'Impulse Response Ergodic';
+        save_filename = 'irf';
+    end
+
+    irf_data = reshape_ascii_irf_data(M_.endo_nbr, percentiles_size, ...
+        options_.ms.horizon, irf_data);
+    save([irfdir filesep save_filename], 'irf_data');
+    plot_ms_irf(M_, options_, irf_data, irf_title, varlist);
 end
 end

matlab/ms-sbvar/ms_mardd.m

@@ -1,197 +1,197 @@
-function ms_mardd(options_)
+function ms_mardd(options_)
-% Applies to both linear and exclusion restrictions.
+% Applies to both linear and exclusion restrictions.
-% (1) Marginal likelihood function p(Y) for constant structural VAR models, using Chib (1995)'s ``Marginal Likelihood from the Gibbs Output'' in JASA.
+% (1) Marginal likelihood function p(Y) for constant structural VAR models, using Chib (1995)'s ``Marginal Likelihood from the Gibbs Output'' in JASA.
-% (2) Conditional likelihood function f(Y|A0, A+) on the ML estimate for constant exclusion-identified models.
+% (2) Conditional likelihood function f(Y|A0, A+) on the ML estimate for constant exclusion-identified models.
-% See Forecast (II) pp.67-80.
+% See Forecast (II) pp.67-80.
-%
+%
-% Tao Zha, September 1999.  Quick revisions, May 2003.  Final revision, September 2004.
+% Tao Zha, September 1999.  Quick revisions, May 2003.  Final revision, September 2004.
-
+
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011 Dynare Team
-%
+%
-% This file is part of Dynare.
+% This file is part of Dynare.
-%
+%
-% Dynare is free software: you can redistribute it and/or modify
+% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
+% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
+% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
+% (at your option) any later version.
-%
+%
-% Dynare is distributed in the hope that it will be useful,
+% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
+% GNU General Public License for more details.
-%
+%
-% You should have received a copy of the GNU General Public License
+% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-
+
-msstart2     % start the program in which everyhting is initialized through msstart2.m
+msstart2     % start the program in which everyhting is initialized through msstart2.m
-if ~options_.ms.indxestima
+if ~options_.ms.indxestima
-   warning(' ')
+   warning(' ')
-   disp('You must set IxEstima=1 in msstart to run this program')
+   disp('You must set IxEstima=1 in msstart to run this program')
-	disp('Press ctrl-c to abort now')
+	disp('Press ctrl-c to abort now')
-	pause
+	pause
-end
+end
-
+
-A0xhat = zeros(size(A0hat));
+A0xhat = zeros(size(A0hat));
-Apxhat = zeros(size(Aphat));
+Apxhat = zeros(size(Aphat));
-if (0)
+if (0)
-   %Robustness check to see if the same result is obtained with the purterbation of the parameters.
+   %Robustness check to see if the same result is obtained with the purterbation of the parameters.
-   for k=1:nvar
+   for k=1:nvar
-      bk = Uiconst{k}'*A0hat(:,k);
+      bk = Uiconst{k}'*A0hat(:,k);
-      gk = Viconst{k}'*Aphat(:,k);
+      gk = Viconst{k}'*Aphat(:,k);
-      A0xhat(:,k) =  Uiconst{k}*(bk + 5.2*randn(size(bk)));   % Perturbing the posterior estimate.
+      A0xhat(:,k) =  Uiconst{k}*(bk + 5.2*randn(size(bk)));   % Perturbing the posterior estimate.
-      Apxhat(:,k) = Viconst{k}*(gk + 5.2*randn(size(gk)));      % Perturbing the posterior estimate.
+      Apxhat(:,k) = Viconst{k}*(gk + 5.2*randn(size(gk)));      % Perturbing the posterior estimate.
-   end
+   end
-else
+else
-   %At the posterior estimate.
+   %At the posterior estimate.
-   A0xhat = A0hat;   % ML estimate of A0
+   A0xhat = A0hat;   % ML estimate of A0
-   Apxhat = Aphat;      % ML estimate of A+
+   Apxhat = Aphat;      % ML estimate of A+
-end
+end
-%--- Rename variables.
+%--- Rename variables.
-YatYa = yty;
+YatYa = yty;
-XatYa = xty;
+XatYa = xty;
-ytx = xty';
+ytx = xty';
-YatXa = ytx;
+YatXa = ytx;
-XatXa = xtx;
+XatXa = xtx;
-
+
-
+
-
+
-%--------- The log value of p(A0,A+) at some point such as the peak ----------
+%--------- The log value of p(A0,A+) at some point such as the peak ----------
-vlog_a0p = 0;
+vlog_a0p = 0;
-Yexpt=0;  % exponential term for Y in p(Y|A0,A+) at some point such as the peak
+Yexpt=0;  % exponential term for Y in p(Y|A0,A+) at some point such as the peak
-Apexpt=0.0;  % 0.0 because we have chosen posterior estimate of A+ as A+*.  Exponential term for A+ conditional on A0 and Y
+Apexpt=0.0;  % 0.0 because we have chosen posterior estimate of A+ as A+*.  Exponential term for A+ conditional on A0 and Y
-%======= Computing the log prior pdf of a0a+ and the exponential term for Y in p(Y|A0,A+).
+%======= Computing the log prior pdf of a0a+ and the exponential term for Y in p(Y|A0,A+).
-for k=1:nvar
+for k=1:nvar
-   a0k = A0xhat(:,k);  % meaningful parameters in the kth equation.
+   a0k = A0xhat(:,k);  % meaningful parameters in the kth equation.
-   apk = Apxhat(:,k);  % meaningful parameters in the kth equation.
+   apk = Apxhat(:,k);  % meaningful parameters in the kth equation.
-
+
-   %--- Prior settings.
+   %--- Prior settings.
-   S0bar = H0invtld{k};  %See Claim 2 on p.69b.
+   S0bar = H0invtld{k};  %See Claim 2 on p.69b.
-   Spbar = Hpinvtld{k};
+   Spbar = Hpinvtld{k};
-   bk = Uiconst{k}'*a0k;  % free parameters in the kth equation.
+   bk = Uiconst{k}'*a0k;  % free parameters in the kth equation.
-   gk = Viconst{k}'*apk;  % free parameters in the kth equation.
+   gk = Viconst{k}'*apk;  % free parameters in the kth equation.
-   gbark = Ptld{k}*bk;   % bar: prior
+   gbark = Ptld{k}*bk;   % bar: prior
-
+
-   %--- The exponential term for Y in p(Y|A0,A+)
+   %--- The exponential term for Y in p(Y|A0,A+)
-   Yexpt = Yexpt - 0.5*(a0k'*YatYa*a0k - 2*apk'*XatYa*a0k + apk'*XatXa*apk);
+   Yexpt = Yexpt - 0.5*(a0k'*YatYa*a0k - 2*apk'*XatYa*a0k + apk'*XatXa*apk);
-   %--- The log prior pdf.
+   %--- The log prior pdf.
-   vlog_a0p = vlog_a0p - 0.5*(size(Uiconst{k},2)+size(Viconst{k},2))*log(2*pi) + 0.5*log(abs(det(S0bar))) + ...
+   vlog_a0p = vlog_a0p - 0.5*(size(Uiconst{k},2)+size(Viconst{k},2))*log(2*pi) + 0.5*log(abs(det(S0bar))) + ...
-         0.5*log(abs(det(Spbar))) - 0.5*(bk'*S0bar*bk+(gk-gbark)'*Spbar*(gk-gbark));
+         0.5*log(abs(det(Spbar))) - 0.5*(bk'*S0bar*bk+(gk-gbark)'*Spbar*(gk-gbark));
-   %--- For p(A+|Y,a0) only.
+   %--- For p(A+|Y,a0) only.
-   tmpd = gk - Pmat{k}*bk;
+   tmpd = gk - Pmat{k}*bk;
-   Apexpt = Apexpt - 0.5*tmpd'*(Hpinv{k}*tmpd);
+   Apexpt = Apexpt - 0.5*tmpd'*(Hpinv{k}*tmpd);
-end
+end
-vlog_a0p
+vlog_a0p
-
+
-%--------- The log value of p(Y|A0,A+) at some point such as the peak. ----------
+%--------- The log value of p(Y|A0,A+) at some point such as the peak. ----------
-%--------- Note that logMarLHres is the same as vlog_Y_a, just to double check. ----------
+%--------- Note that logMarLHres is the same as vlog_Y_a, just to double check. ----------
-vlog_Y_a = -0.5*nvar*fss*log(2*pi) + fss*log(abs(det(A0xhat))) + Yexpt
+vlog_Y_a = -0.5*nvar*fss*log(2*pi) + fss*log(abs(det(A0xhat))) + Yexpt
-                 % a: given a0 and a+
+                 % a: given a0 and a+
-logMarLHres = 0;   % Initialize log of the marginal likelihood (restricted or constant parameters).
+logMarLHres = 0;   % Initialize log of the marginal likelihood (restricted or constant parameters).
-for ki=1:fss   %ndobs+1:fss     % Forward recursion to get the marginal likelihood.  See F on p.19 and pp. 48-49.
+for ki=1:fss   %ndobs+1:fss     % Forward recursion to get the marginal likelihood.  See F on p.19 and pp. 48-49.
-   %----  Restricted log marginal likelihood function (constant parameters).
+   %----  Restricted log marginal likelihood function (constant parameters).
-   [A0l,A0u] = lu(A0xhat);
+   [A0l,A0u] = lu(A0xhat);
-   ada = sum(log(abs(diag(A0u))));   % log|A0|
+   ada = sum(log(abs(diag(A0u))));   % log|A0|
-   termexp = y(ki,:)*A0xhat - phi(ki,:)*Apxhat;   % 1-by-nvar
+   termexp = y(ki,:)*A0xhat - phi(ki,:)*Apxhat;   % 1-by-nvar
-   logMarLHres = logMarLHres - (0.5*nvar)*log(2*pi) + ada - 0.5*termexp*termexp';  % log value
+   logMarLHres = logMarLHres - (0.5*nvar)*log(2*pi) + ada - 0.5*termexp*termexp';  % log value
-end
+end
-logMarLHres
+logMarLHres
-
+
-
+
-%--------- The log value of p(A+|Y,A0) at some point such as the peak ----------
+%--------- The log value of p(A+|Y,A0) at some point such as the peak ----------
-totparsp = 0.0;
+totparsp = 0.0;
-tmpd = 0.0;
+tmpd = 0.0;
-for k=1:nvar
+for k=1:nvar
-   totparsp = totparsp + size(Viconst{k},2);
+   totparsp = totparsp + size(Viconst{k},2);
-   tmpd = tmpd + 0.5*log(abs(det(Hpinv{k})));
+   tmpd = tmpd + 0.5*log(abs(det(Hpinv{k})));
-end
+end
-vlog_ap_Ya0 = -0.5*totparsp*log(2*pi) + tmpd + Apexpt;
+vlog_ap_Ya0 = -0.5*totparsp*log(2*pi) + tmpd + Apexpt;
-
+
-
+
-
+
-
+
-%===================================
+%===================================
-%  Compute p(a0,k|Y,ao) at some point such as the peak (in this situation, we simply
+%  Compute p(a0,k|Y,ao) at some point such as the peak (in this situation, we simply
-%   generate results from the original Gibbs sampler).  See FORECAST (2) pp.70-71
+%   generate results from the original Gibbs sampler).  See FORECAST (2) pp.70-71
-%===================================
+%===================================
-%--- Global set up for Gibbs.
+%--- Global set up for Gibbs.
-[Tinv,UT] = fn_gibbsrvar_setup(H0inv, Uiconst, Hpinv, Pmat, Viconst, nvar, fss);
+[Tinv,UT] = fn_gibbsrvar_setup(H0inv, Uiconst, Hpinv, Pmat, Viconst, nvar, fss);
-%
+%
-vlog_a0_Yao = zeros(nvar,1);
+vlog_a0_Yao = zeros(nvar,1);
-  % the log value of p(a0k|Y,ao) where ao: other a's at some point such as the peak of ONLY some a0's
+  % the log value of p(a0k|Y,ao) where ao: other a's at some point such as the peak of ONLY some a0's
-vlog=zeros(ndraws2,1);
+vlog=zeros(ndraws2,1);
-for k=1:nvar
+for k=1:nvar
-   bk = Uiconst{k}'*A0xhat(:,k);
+   bk = Uiconst{k}'*A0xhat(:,k);
-   indx_ks=[k:nvar];  % the columns that exclude 1-(k-1)th columns
+   indx_ks=[k:nvar];  % the columns that exclude 1-(k-1)th columns
-   A0gbs0 = A0hat;   % starting at some point such as the peak
+   A0gbs0 = A0hat;   % starting at some point such as the peak
-   nk = n0(k);
+   nk = n0(k);
-
+
-   if k<nvar
+   if k<nvar
-      %--------- The 1st set of draws to be tossed away. ------------------
+      %--------- The 1st set of draws to be tossed away. ------------------
-      for draws = 1:ndraws1
+      for draws = 1:ndraws1
-         if ~mod(draws,nbuffer)
+         if ~mod(draws,nbuffer)
-            disp(' ')
+            disp(' ')
-            disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws))
+            disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws))
-         end
+         end
-         A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
+         A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
-         A0gbs0=A0gbs1;    % repeat the Gibbs sampling
+         A0gbs0=A0gbs1;    % repeat the Gibbs sampling
-      end
+      end
-
+
-
+
-      %--------- The 2nd set of draws to be used. ------------------
+      %--------- The 2nd set of draws to be used. ------------------
-      for draws = 1:ndraws2
+      for draws = 1:ndraws2
-         if ~mod(draws,nbuffer)
+         if ~mod(draws,nbuffer)
-            disp(' ')
+            disp(' ')
-            disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws))
+            disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws))
-         end
+         end
-         [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
+         [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
-         %------ See p.71, Forecast (II).
+         %------ See p.71, Forecast (II).
-         %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
+         %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
-         Vk = Tinv{k}\Wcell{k};  %V_k on p.71 of Forecast (II).
+         Vk = Tinv{k}\Wcell{k};  %V_k on p.71 of Forecast (II).
-         gbeta = Vk\bk;  % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
+         gbeta = Vk\bk;  % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
-         [Vtq,Vtr]=qr(Vk',0);  %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
+         [Vtq,Vtr]=qr(Vk',0);  %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
-         %
+         %
-         vlog(draws) = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
+         vlog(draws) = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
-                  0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
+                  0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
-                  0.5*fss*bk'*(Vtr\(Vtr'\bk));
+                  0.5*fss*bk'*(Vtr\(Vtr'\bk));
-
+
-         A0gbs0=A0gbs1;    % repeat the Gibbs sampling
+         A0gbs0=A0gbs1;    % repeat the Gibbs sampling
-      end
+      end
-      vlogm=max(vlog);
+      vlogm=max(vlog);
-      qlog=vlog-vlogm;
+      qlog=vlog-vlogm;
-      vlogxhat=vlogm-log(ndraws2)+log(sum(exp(qlog)));
+      vlogxhat=vlogm-log(ndraws2)+log(sum(exp(qlog)));
-      vlog_a0_Yao(k) = vlogxhat;
+      vlog_a0_Yao(k) = vlogxhat;
-         % The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's
+         % The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's
-   else
+   else
-      disp(' ')
+      disp(' ')
-      disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws'))
+      disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws'))
-      [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks)
+      [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks)
-      %------ See p.71, Forecast (II).
+      %------ See p.71, Forecast (II).
-      %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
+      %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
-      Vk = Tinv{k}\Wcell{k};  %V_k on p.71 of Forecast (II).
+      Vk = Tinv{k}\Wcell{k};  %V_k on p.71 of Forecast (II).
-      gbeta = Vk\bk;  % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
+      gbeta = Vk\bk;  % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
-      [Vtq,Vtr]=qr(Vk',0);  %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
+      [Vtq,Vtr]=qr(Vk',0);  %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
-      %
+      %
-      vloglast = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
+      vloglast = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
-               0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
+               0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
-               0.5*fss*bk'*(Vtr\(Vtr'\bk));
+               0.5*fss*bk'*(Vtr\(Vtr'\bk));
-      vlog_a0_Yao(k) = vloglast;
+      vlog_a0_Yao(k) = vloglast;
-   end
+   end
-end
+end
-ndraws2
+ndraws2
-
+
-disp('Prior pdf -- log(p(a0hat, a+hat)):');
+disp('Prior pdf -- log(p(a0hat, a+hat)):');
-vlog_a0p
+vlog_a0p
-disp('LH pdf -- log(p(Y|a0hat, a+hat)):');
+disp('LH pdf -- log(p(Y|a0hat, a+hat)):');
-vlog_Y_a
+vlog_Y_a
-disp('Posterior Kernal -- logp(ahat) + logp(Y|ahat):');
+disp('Posterior Kernal -- logp(ahat) + logp(Y|ahat):');
-vlog_Y_a + vlog_a0p
+vlog_Y_a + vlog_a0p
-disp('Posterior pdf -- log(p(a0_i_hat|a0_other_hat, Y)):');
+disp('Posterior pdf -- log(p(a0_i_hat|a0_other_hat, Y)):');
-vlog_a0_Yao
+vlog_a0_Yao
-disp('Posterior pdf -- log(p(aphat|a0hat, Y)):');
+disp('Posterior pdf -- log(p(aphat|a0hat, Y)):');
-vlog_ap_Ya0
+vlog_ap_Ya0
-
+
-%--------- The value of marginal density p(Y) ----------
+%--------- The value of marginal density p(Y) ----------
-disp(' ');
+disp(' ');
-disp(' ');
+disp(' ');
-disp('************ Marginal Likelihood of Y or Marginal Data Density: ************');
+disp('************ Marginal Likelihood of Y or Marginal Data Density: ************');
-vlogY = vlog_a0p+vlog_Y_a-sum(vlog_a0_Yao)-vlog_ap_Ya0
+vlogY = vlog_a0p+vlog_Y_a-sum(vlog_a0_Yao)-vlog_ap_Ya0

matlab/ms-sbvar/ms_variance_decomposition.m

@@ -34,46 +34,97 @@ function [options_, oo_]=ms_variance_decomposition(M_, options_, oo_)
 disp('MS-SBVAR Variance Decomposition');
 options_ = set_file_tags(options_);
 [options_, oo_] = set_ms_estimation_file(options_.ms.file_tag, options_, oo_);
-options_ = set_ms_simulation_file(options_);
+clean_ms_variance_decomposition_files(options_.ms.output_file_tag);
-clean_files_for_second_type_of_mex(M_, options_, 'variance_decomposition')
 vddir = [options_.ms.output_file_tag filesep 'Variance_Decomposition'];
 create_dir(vddir);
 
-% NOTICE THAT VARIANCE DECOMPOSITION DEFAULTS TO USING THE MEAN, NOT MEDIAN OR BANDED
+% setup command line options
+opt = ['-variance_decomposition -seed ' num2str(options_.DynareRandomStreams.seed)];
+opt = [opt ' -do ' vddir];
+opt = [opt ' -ft ' options_.ms.file_tag];
+opt = [opt ' -fto ' options_.ms.output_file_tag];
+opt = [opt ' -horizon ' num2str(options_.ms.horizon)];
+opt = [opt ' -thin ' num2str(options_.ms.thinning_factor)];
 
-opt = {
+if options_.ms.regimes
-    {'file_tag', options_.ms.file_tag}, ...
+    opt = [opt ' -regimes'];
-    {'seed', options_.DynareRandomStreams.seed}, ...
+elseif options_.ms.regime
-    {'horizon', options_.ms.horizon}, ...
+    % regime-1 since regime is 0-indexed in C but 1-indexed in Matlab
-    {'filtered', options_.ms.filtered_probabilities}, ...
+    opt = [opt ' -regime ' num2str(options_.ms.regime-1)];
-    {'error_bands', options_.ms.error_bands}, ...
+elseif options_.ms.filtered_probabilities
-    {'percentiles', options_.ms.percentiles}, ...
+    opt = [opt ' -filtered'];
-    {'thin', options_.ms.thinning_factor}, ...
-    {'mean'} ...
-    };
-
-if options_.ms.median
-    opt = [opt(:)' {{'median'}}];
 end
 
-[err, vd] = mex_ms_variance_decomposition([opt(:)', {{'free_parameters',oo_.ms.maxparams}, ...
+if options_.ms.parameter_uncertainty
-    {'shocks_per_parameter', options_.ms.shock_draws}}]);
+    options_ = set_ms_simulation_file(options_);
-mexErrCheck('mex_ms_variance_decomposition ergodic ', err);
+    opt = [opt ' -parameter_uncertainty'];
-plot_ms_variance_decomposition(M_,options_,vd, 'Ergodic Variance Decomposition',options_.graph_save_formats,options_.TeX);
+    opt = [opt ' -shocks_per_parameter ' num2str(options_.ms.shocks_per_parameter)];
+else
+    opt = [opt ' -shocks_per_parameter ' num2str(options_.ms.shock_draws)];
+end
 
-[err, regime_vd] = mex_ms_variance_decomposition([opt(:)', {{'free_parameters',oo_.ms.maxparams}, ...
+percentiles_size = 1;
-    {'shocks_per_parameter', options_.ms.shock_draws}, {'regimes'}}]);
+outfile = [vddir filesep 'var_decomp_mean_'];
-mexErrCheck('mex_ms_variance_decomposition ergodic regimes', err);
+if options_.ms.error_bands
-save([vddir filesep 'ergodic_vd.mat'], 'vd', 'regime_vd');
+    % error_bands / percentiles used differently by
+    % Dan's variance decomposition code
+    % no_error_bands => mean is computed
+    percentiles_size = size(options_.ms.percentiles,2);
+    opt = [opt ' -percentiles ' num2str(percentiles_size)];
+    for i=1:size(options_.ms.percentiles,2)
+        opt = [opt ' ' num2str(options_.ms.percentiles(i))];
+    end
+    outfile = [vddir filesep 'var_decomp_percentiles_'];
+end
 
-if exist(options_.ms.mh_file,'file') > 0
+% variance_decomposition
-    [err, vd] = mex_ms_variance_decomposition([opt(:)', {{'simulation_file',options_.ms.mh_file}, ...
+[err] = ms_sbvar_command_line(opt);
-        {'shocks_per_parameter', options_.ms.shocks_per_parameter}, {'parameter_uncertainty'}}]);
+mexErrCheck('ms_variance_decomposition',err);
-    mexErrCheck('mex_ms_variance_decomposition bayesian ', err);
 
-    [err, regime_vd] = mex_ms_variance_decomposition([opt(:)', {{'simulation_file',options_.ms.mh_file}, ...
+if options_.ms.regime || options_.ms.regimes
-        {'shocks_per_parameter', options_.ms.shocks_per_parameter}, {'parameter_uncertainty'}, {'regimes'}}]);
+    outfile = [outfile 'regime_'];
-    mexErrCheck('mex_ms_variance_decomposition bayesian regimes ', err);
+    if options_.ms.regime
-    save([vddir filesep 'bayesian_vd.mat'], 'vd', 'regime_vd');
+        outfile = [outfile num2str(options_.ms.regime-1) ...
+            '_' options_.ms.output_file_tag '.out'];
+    end
+elseif options_.ms.filtered_probabilities
+    outfile = [outfile 'filtered_' options_.ms.output_file_tag '.out'];
+else
+    outfile = [outfile 'ergodic_' options_.ms.output_file_tag '.out'];
+end
+
+% Create plots
+if options_.ms.regimes
+    n_chains = length(options_.ms.ms_chain);
+    n_regimes=1;
+    for i_chain=1:n_chains
+        n_regimes = n_regimes*length(options_.ms.ms_chain(i_chain).regime);
+    end
+    for regime_i=1:n_regimes
+        vd_title = ['Variance Decomposition, Regime ' num2str(regime_i)];
+        vd_data = load([outfile num2str(regime_i-1) '_' ...
+            options_.ms.output_file_tag '.out'], '-ascii');
+        vd_data = reshape_ascii_variance_decomposition_data( ...
+            M_.endo_nbr, percentiles_size, options_.ms.horizon, vd_data);
+        save([vddir filesep 'variance_decomposition_regime_' num2str(regime_i-1)], 'vd_data');
+        plot_ms_variance_decomposition(M_, options_, vd_data, vd_title);
+    end
+else
+    if options_.ms.regime
+        vd_title = ['Variance Decomposition, Regime ' num2str(options_.ms.regime)];
+        save_filename = ['variance_decomposition_regime_' num2str(options_.ms.regime-1)];
+    else
+        save_filename = 'variance_decomposition';
+        if options_.ms.filtered_probabilities
+            vd_title = 'Variance Decomposition Filtered';
+        else
+            vd_title = 'Variance Decomposition Ergodic';
+        end
+    end
+    vd_data = load(outfile, '-ascii');
+    vd_data = reshape_ascii_variance_decomposition_data( ...
+        M_.endo_nbr, percentiles_size, options_.ms.horizon, vd_data);
+    save([vddir filesep save_filename], 'vd_data');
+    plot_ms_variance_decomposition(M_, options_, vd_data, vd_title);
 end
 end

matlab/ms-sbvar/ms_write_markov_file.m

@@ -1,162 +1,162 @@
-function ms_write_markov_file(fname, options)
+function ms_write_markov_file(fname, options)
-% function ms_write_markov_file(fname, options)
+% function ms_write_markov_file(fname, options)
-%
+%
-% INPUTS
+% INPUTS
-%    fname:       (string)    name of markov file
+%    fname:       (string)    name of markov file
-%    options:     (struct)    options
+%    options:     (struct)    options
-%
+%
-% OUTPUTS
+% OUTPUTS
-%
+%
-% SPECIAL REQUIREMENTS
+% SPECIAL REQUIREMENTS
-%    none
+%    none
-
+
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011 Dynare Team
-%
+%
-% This file is part of Dynare.
+% This file is part of Dynare.
-%
+%
-% Dynare is free software: you can redistribute it and/or modify
+% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
+% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
+% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
+% (at your option) any later version.
-%
+%
-% Dynare is distributed in the hope that it will be useful,
+% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
+% GNU General Public License for more details.
-%
+%
-% You should have received a copy of the GNU General Public License
+% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-
+
-    n_chains = length(options.ms.ms_chain);
+    n_chains = length(options.ms.ms_chain);
-    nvars = size(options.varobs,1);
+    nvars = size(options.varobs,1);
-    
+    
-    fh = fopen(fname,'w');
+    fh = fopen(fname,'w');
-    %/******************************************************************************/
+    %/******************************************************************************/
-    %/********************* Markov State Variable Information **********************/
+    %/********************* Markov State Variable Information **********************/
-    %/******************************************************************************/
+    %/******************************************************************************/
-    
+    
-    fprintf(fh,'//== Markov State Variables with Simple Restrictions ==//\n\n');
+    fprintf(fh,'//== Markov State Variables with Simple Restrictions ==//\n\n');
-    
+    
-    
+    
-    %//This number is NOT used but read in.
+    %//This number is NOT used but read in.
-    fprintf(fh,'//== Number Observations ==//\n');
+    fprintf(fh,'//== Number Observations ==//\n');
-    fprintf(fh,'0\n\n');
+    fprintf(fh,'0\n\n');
-
+
-    fprintf(fh,'//== Number independent state variables ==//\n');
+    fprintf(fh,'//== Number independent state variables ==//\n');
-    fprintf(fh,'%d\n\n',n_chains);
+    fprintf(fh,'%d\n\n',n_chains);
-
+
-    for i_chain = 1:n_chains
+    for i_chain = 1:n_chains
-
+
-        %//=====================================================//
+        %//=====================================================//
-        %//== state_variable[i] (1 <= i <= n_state_variables) ==//
+        %//== state_variable[i] (1 <= i <= n_state_variables) ==//
-        %//=====================================================//
+        %//=====================================================//
-        fprintf(fh,'//== Number of states for state_variable[%d] ==//\n', ...
+        fprintf(fh,'//== Number of states for state_variable[%d] ==//\n', ...
-                i_chain);
+                i_chain);
-        n_states = length(options.ms.ms_chain(i_chain).regime);
+        n_states = length(options.ms.ms_chain(i_chain).regime);
-        fprintf(fh,'%d\n\n',n_states);
+        fprintf(fh,'%d\n\n',n_states);
-
+
-        %//== 03/15/06: DW TVBVAR code reads the data below and overwrite the prior data read somewhere else if any.
+        %//== 03/15/06: DW TVBVAR code reads the data below and overwrite the prior data read somewhere else if any.
-        %//== Each column contains the parameters for a Dirichlet prior on the corresponding
+        %//== Each column contains the parameters for a Dirichlet prior on the corresponding
-        %//== column of the transition matrix.  Each element must be positive.  For each column,
+        %//== column of the transition matrix.  Each element must be positive.  For each column,
-        %//== the relative size of the prior elements determine the relative size of the elements
+        %//== the relative size of the prior elements determine the relative size of the elements
-        %//== of the transition matrix and overall larger sizes implies a tighter prior.
+        %//== of the transition matrix and overall larger sizes implies a tighter prior.
-        fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ...
+        fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ...
-          i_chain);
+          i_chain);
-        Alpha = ones(n_states,n_states);
+        Alpha = ones(n_states,n_states);
-        for i_state = 1:n_states
+        for i_state = 1:n_states
-            p = 1-1/options.ms.ms_chain(i_chain).regime(i_state).duration;
+            p = 1-1/options.ms.ms_chain(i_chain).regime(i_state).duration;
-            Alpha(i_state,i_state) = p*(n_states-1)/(1-p);
+            Alpha(i_state,i_state) = p*(n_states-1)/(1-p);
-            fprintf(fh,'%22.16f',Alpha(i_state,:));
+            fprintf(fh,'%22.16f',Alpha(i_state,:));
-            fprintf(fh,'\n');
+            fprintf(fh,'\n');
-        end
+        end
- 
+ 
-        fprintf(fh,['\n//== Dirichlet dimensions for state_variable[%d] ' ...
+        fprintf(fh,['\n//== Dirichlet dimensions for state_variable[%d] ' ...
-                    '==//\n'],i_chain);
+                    '==//\n'],i_chain);
-        %        fprintf(fh,'%d ',repmat(n_states,1,n_states));
+        %        fprintf(fh,'%d ',repmat(n_states,1,n_states));
-        fprintf(fh,'%d ',repmat(2,1,n_states));
+        fprintf(fh,'%d ',repmat(2,1,n_states));
-        fprintf(fh,'\n\n');
+        fprintf(fh,'\n\n');
-
+
-        %//== The jth restriction matrix is n_states-by-free[j].  Each row of the restriction
+        %//== The jth restriction matrix is n_states-by-free[j].  Each row of the restriction
-        %//== matrix has exactly one non-zero entry and the sum of each column must be one.
+        %//== matrix has exactly one non-zero entry and the sum of each column must be one.
-        fprintf(fh,['//== Column restrictions for state_variable[%d] ' ...
+        fprintf(fh,['//== Column restrictions for state_variable[%d] ' ...
-                    '==//\n'],i_chain);
+                    '==//\n'],i_chain);
-        for i_state = 1:n_states
+        for i_state = 1:n_states
-            if i_state == 1
+            if i_state == 1
-                M = eye(n_states,2);
+                M = eye(n_states,2);
-            elseif i_state == n_states
+            elseif i_state == n_states
-                M = [zeros(n_states-2,2); eye(2)];
+                M = [zeros(n_states-2,2); eye(2)];
-            else
+            else
-                M = zeros(n_states,2);
+                M = zeros(n_states,2);
-                M(i_state+[-1 1],1) = ones(2,1)/2;
+                M(i_state+[-1 1],1) = ones(2,1)/2;
-                M(i_state,2) = 1;
+                M(i_state,2) = 1;
-                disp(M)
+                disp(M)
-            end
+            end
-            for j_state = 1:n_states
+            for j_state = 1:n_states
-                fprintf(fh,'%f ',M(j_state,:));
+                fprintf(fh,'%f ',M(j_state,:));
-                fprintf(fh,'\n');
+                fprintf(fh,'\n');
-            end
+            end
-            fprintf(fh,'\n');
+            fprintf(fh,'\n');
-        end
+        end
-    end
+    end
-
+
-    %/******************************************************************************/
+    %/******************************************************************************/
-    %/******************************* VAR Parameters *******************************/
+    %/******************************* VAR Parameters *******************************/
-    %/******************************************************************************/
+    %/******************************************************************************/
-    %//NOT read
+    %//NOT read
-    fprintf(fh,'//== Number Variables ==//\n');
+    fprintf(fh,'//== Number Variables ==//\n');
-    fprintf(fh,'%d\n\n',nvars);
+    fprintf(fh,'%d\n\n',nvars);
-
+
-    %//NOT read
+    %//NOT read
-    fprintf(fh,'//== Number Lags ==//\n');
+    fprintf(fh,'//== Number Lags ==//\n');
-    fprintf(fh,'%d\n\n',options.ms.nlags);
+    fprintf(fh,'%d\n\n',options.ms.nlags);
-
+
-    %//NOT read
+    %//NOT read
-    fprintf(fh,'//== Exogenous Variables ==//\n');
+    fprintf(fh,'//== Exogenous Variables ==//\n');
-    fprintf(fh,'1\n\n');
+    fprintf(fh,'1\n\n');
-
+
-
+
-    %//== nvar x n_state_variables matrix.  In the jth row, a non-zero value implies that
+    %//== nvar x n_state_variables matrix.  In the jth row, a non-zero value implies that
-    %this state variable controls the jth column of A0 and Aplus
+    %this state variable controls the jth column of A0 and Aplus
-    fprintf(fh,['//== Controlling states variables for coefficients ==//\' ...
+    fprintf(fh,['//== Controlling states variables for coefficients ==//\' ...
-                'n']);
+                'n']);
-    
+    
-    for i_var = 1:nvars
+    for i_var = 1:nvars
-        for i_chain = 1:n_chains
+        for i_chain = 1:n_chains
-            if ~isfield(options.ms.ms_chain(i_chain),'svar_coefficients') ...
+            if ~isfield(options.ms.ms_chain(i_chain),'svar_coefficients') ...
-                    || isempty(options.ms.ms_chain(i_chain).svar_coefficients)
+                    || isempty(options.ms.ms_chain(i_chain).svar_coefficients)
-                i_equations = 0;
+                i_equations = 0;
-            else
+            else
-                i_equations = ...
+                i_equations = ...
-                    options.ms.ms_chain(i_chain).svar_coefficients.equations; 
+                    options.ms.ms_chain(i_chain).svar_coefficients.equations; 
-            end
+            end
-            if strcmp(i_equations,'ALL') || any(i_equations == i_var)
+            if strcmp(i_equations,'ALL') || any(i_equations == i_var)
-                fprintf(fh,'%d ',1);
+                fprintf(fh,'%d ',1);
-            else
+            else
-                fprintf(fh,'%d ',0);
+                fprintf(fh,'%d ',0);
-            end
+            end
-        end
+        end
-        fprintf(fh,'\n');
+        fprintf(fh,'\n');
-    end
+    end
-
+
-    %//== nvar x n_state_variables matrix.  In the jth row, a non-zero value implies that
+    %//== nvar x n_state_variables matrix.  In the jth row, a non-zero value implies that
-    %this state variable controls the jth diagonal element of Xi
+    %this state variable controls the jth diagonal element of Xi
-    fprintf(fh,'\n//== Controlling states variables for variance ==//\n');
+    fprintf(fh,'\n//== Controlling states variables for variance ==//\n');
-    for i_var = 1:nvars
+    for i_var = 1:nvars
-        for i_chain = 1:n_chains
+        for i_chain = 1:n_chains
-            if ~isfield(options.ms.ms_chain(i_chain),'svar_variances') ...
+            if ~isfield(options.ms.ms_chain(i_chain),'svar_variances') ...
-                    || isempty(options.ms.ms_chain(i_chain).svar_variances)
+                    || isempty(options.ms.ms_chain(i_chain).svar_variances)
-                    i_equations = 0;
+                    i_equations = 0;
-            else
+            else
-                i_equations = ...
+                i_equations = ...
-                    options.ms.ms_chain(i_chain).svar_variances.equations; 
+                    options.ms.ms_chain(i_chain).svar_variances.equations; 
-            end
+            end
-            if strcmp(i_equations,'ALL') || any(i_equations == i_var)
+            if strcmp(i_equations,'ALL') || any(i_equations == i_var)
-                fprintf(fh,'%d ',1);
+                fprintf(fh,'%d ',1);
-            else
+            else
-                fprintf(fh,'%d ',0);
+                fprintf(fh,'%d ',0);
-            end
+            end
-        end
+        end
-        fprintf(fh,'\n');
+        fprintf(fh,'\n');
-    end
+    end
-
+
-    fclose(fh);
+    fclose(fh);

matlab/ms-sbvar/ms_write_mhm_input.m

@@ -1,68 +1,68 @@
-function ms_write_mhm_input(fname, options_ms)
+function ms_write_mhm_input(fname, options_ms)
-% function ms_write_mhm_input(fname, options_ms)
+% function ms_write_mhm_input(fname, options_ms)
-%
+%
-% INPUTS
+% INPUTS
-%    fname:         (string)    filename
+%    fname:         (string)    filename
-%    options_ms:    (struct)    options
+%    options_ms:    (struct)    options
-%
+%
-% OUTPUTS
+% OUTPUTS
-%    none
+%    none
-%
+%
-% SPECIAL REQUIREMENTS
+% SPECIAL REQUIREMENTS
-%    none
+%    none
-
+
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011 Dynare Team
-%
+%
-% This file is part of Dynare.
+% This file is part of Dynare.
-%
+%
-% Dynare is free software: you can redistribute it and/or modify
+% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
+% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
+% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
+% (at your option) any later version.
-%
+%
-% Dynare is distributed in the hope that it will be useful,
+% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
+% GNU General Public License for more details.
-%
+%
-% You should have received a copy of the GNU General Public License
+% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-
+
-    fh = fopen(fname,'w');
+    fh = fopen(fname,'w');
-
+
-
+
-    fprintf(fh,'/**********************************************************\n');
+    fprintf(fh,'/**********************************************************\n');
-    fprintf(fh,' *** This input file is read by swzmsbvar_mhm_1 and swzmsbvar_mhm_1.exe only, NOT by swzmsbvar_printdraws.exe.\n');
+    fprintf(fh,' *** This input file is read by swzmsbvar_mhm_1 and swzmsbvar_mhm_1.exe only, NOT by swzmsbvar_printdraws.exe.\n');
-    fprintf(fh,' ***\n');
+    fprintf(fh,' ***\n');
-    fprintf(fh,' **********************************************************/\n');
+    fprintf(fh,' **********************************************************/\n');
-
+
-    fprintf(fh,'\n\n//------------- 1st set of posterior draws to find optimal scales for Metropolis (30000). ---------------\n');
+    fprintf(fh,'\n\n//------------- 1st set of posterior draws to find optimal scales for Metropolis (30000). ---------------\n');
-    fprintf(fh,'//== number draws for first burn-in ==//  //For determining the Metropolis scales only.\n');
+    fprintf(fh,'//== number draws for first burn-in ==//  //For determining the Metropolis scales only.\n');
-    fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_1);
+    fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_1);
-
+
-    fprintf(fh,'//------------- MCMC burn-in draws once the Metropolis scales (previous stage) are fixed. --------------\n');
+    fprintf(fh,'//------------- MCMC burn-in draws once the Metropolis scales (previous stage) are fixed. --------------\n');
-    fprintf(fh,'//------------- 2nd set of standard burn-in posterior draws to throw away the initial draws (10000). ---------------\n');
+    fprintf(fh,'//------------- 2nd set of standard burn-in posterior draws to throw away the initial draws (10000). ---------------\n');
-    fprintf(fh,'//== number draws for second burn-in ==//\n');
+    fprintf(fh,'//== number draws for second burn-in ==//\n');
-    fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_2);
+    fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_2);
-
+
-    fprintf(fh,'//--------------- 1st set of posterior draws to compute the mean and variance for the weighting function in the MHM (200000) ----------------\n');
+    fprintf(fh,'//--------------- 1st set of posterior draws to compute the mean and variance for the weighting function in the MHM (200000) ----------------\n');
-    fprintf(fh,'//== number draws to estimate mean and variance ==//\n');
+    fprintf(fh,'//== number draws to estimate mean and variance ==//\n');
-    fprintf(fh,'%d\n\n',options_ms.draws_nbr_mean_var_estimate);
+    fprintf(fh,'%d\n\n',options_ms.draws_nbr_mean_var_estimate);
-
+
-    fprintf(fh,'//--------------- Only applied to mhm_2 process: total number of MCMC draws = thinning factor * 2nd set of saved posterior draws ----------------\n');
+    fprintf(fh,'//--------------- Only applied to mhm_2 process: total number of MCMC draws = thinning factor * 2nd set of saved posterior draws ----------------\n');
-    fprintf(fh,'//== thinning factor for modified harmonic mean process ==//\n');
+    fprintf(fh,'//== thinning factor for modified harmonic mean process ==//\n');
-    fprintf(fh,'%d\n\n',options_ms.thinning_factor);
+    fprintf(fh,'%d\n\n',options_ms.thinning_factor);
-
+
-    fprintf(fh,'//--------------- 2nd set of saved posterior draws from MHM_2 (second stage): saved draws AFTER thinning (1000000) ----------------\n');
+    fprintf(fh,'//--------------- 2nd set of saved posterior draws from MHM_2 (second stage): saved draws AFTER thinning (1000000) ----------------\n');
-    fprintf(fh,'//== number draws for modified harmonic mean process ==//\n');
+    fprintf(fh,'//== number draws for modified harmonic mean process ==//\n');
-    fprintf(fh,'%d\n\n',options_ms.draws_nbr_modified_harmonic_mean);
+    fprintf(fh,'%d\n\n',options_ms.draws_nbr_modified_harmonic_mean);
-
+
-    fprintf(fh,'//------- 1st stage: computing all three tightness factors for Dirichlet.  ---------\n');
+    fprintf(fh,'//------- 1st stage: computing all three tightness factors for Dirichlet.  ---------\n');
-    fprintf(fh,'//------- 2nd stage: hard-code the second scale factor (in principle, we can do all three). ---------\n');
+    fprintf(fh,'//------- 2nd stage: hard-code the second scale factor (in principle, we can do all three). ---------\n');
-    fprintf(fh,'//------- It seems that Dan''s code only use the first element of the following scales.  The scale applies to the Dirichlet''s hyperparameter alpha for the diagonal of the transition matrix in the weighting function.  Note that the weighting function for the transition matrix parameters is Dirichlet. ---------\n');
+    fprintf(fh,'//------- It seems that Dan''s code only use the first element of the following scales.  The scale applies to the Dirichlet''s hyperparameter alpha for the diagonal of the transition matrix in the weighting function.  Note that the weighting function for the transition matrix parameters is Dirichlet. ---------\n');
-            
+            
-    fprintf(fh,'//== scale values for Dirichlet distribution ==//\n');
+    fprintf(fh,'//== scale values for Dirichlet distribution ==//\n');
-    fprintf(fh,'3\n\n');
+    fprintf(fh,'3\n\n');
-    fprintf(fh,'%f ',options_ms.dirichlet_scale);
+    fprintf(fh,'%f ',options_ms.dirichlet_scale);
-    fprintf(fh,'\n');
+    fprintf(fh,'\n');
     fclose(fh);

matlab/ms-sbvar/msstart_setup.m

@@ -1,166 +1,165 @@
-%function []= msstart_setup(options_)
+%function []= msstart_setup(options_)
-
+
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011 Dynare Team
-%
+%
-% This file is part of Dynare.
+% This file is part of Dynare.
-%
+%
-% Dynare is free software: you can redistribute it and/or modify
+% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
+% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
+% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
+% (at your option) any later version.
-%
+%
-% Dynare is distributed in the hope that it will be useful,
+% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
+% GNU General Public License for more details.
-%
+%
-% You should have received a copy of the GNU General Public License
+% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-
+
-% ** ONLY UNDER UNIX SYSTEM
+% ** ONLY UNDER UNIX SYSTEM
-%path(path,'/usr2/f1taz14/mymatlab')
+%path(path,'/usr2/f1taz14/mymatlab')
-
+
-
+
-
+
-%===========================================
+%===========================================
-% Exordium I
+% Exordium I
-%===========================================
+%===========================================
-format short g     % format
+format short g     % format
-%
+%
-%options_.ms.freq = 4;   % quarters or months
+%options_.ms.freq = 4;   % quarters or months
-%options_.ms.initial_year=1959;   % beginning of the year
+%options_.ms.initial_year=1959;   % beginning of the year
-%options_.ms.initial_subperiod=1;    % begining of the quarter or month
+%options_.ms.initial_subperiod=1;    % begining of the quarter or month
-%options_.ms.final_year=2005;   % final year
+%options_.ms.final_year=2005;   % final year
-%options_.ms.final_subperiod=4;    % final month or quarter
+%options_.ms.final_subperiod=4;    % final month or quarter
-nData=(options_.ms.final_year-options_.ms.initial_year)*options_.ms.freq + (options_.ms.final_subperiod-options_.ms.initial_subperiod+1);
+nData=(options_.ms.final_year-options_.ms.initial_year)*options_.ms.freq + (options_.ms.final_subperiod-options_.ms.initial_subperiod+1);
-       % total number of the available data -- this is all you have
+       % total number of the available data -- this is all you have
-
+
-%*** Load data and series
+%*** Load data and series
-%load datainf_argen.prn      % the default name for the variable is "options_.ms.data".
+%load datainf_argen.prn      % the default name for the variable is "options_.ms.data".
-%load datacbogdpffr.prn
+%load datacbogdpffr.prn
-%options_.ms.data = datacbogdpffr;
+%options_.ms.data = datacbogdpffr;
-%clear datacbogdpffr;
+%clear datacbogdpffr;
-[nt,ndv]=size(options_.data);
+[nt,ndv]=size(options_.data);
-if nt~=nData
+if nt~=nData
-   disp(' ')
+   disp(' ')
-   warning(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
+   warning(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
-   %disp(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
+   %disp(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
-   disp('Press ctrl-c to abort')
+   disp('Press ctrl-c to abort')
-   return
+   return
-end
+end
-%--------
+%--------
-%1    CBO output gap --  log(x_t)-log(x_t potential)
+%1    CBO output gap --  log(x_t)-log(x_t potential)
-%2    GDP deflator -- (P_t/P_{t-1})^4-1.0
+%2    GDP deflator -- (P_t/P_{t-1})^4-1.0
-%2    FFR/100.
+%2    FFR/100.
-options_.ms.vlist = [1:size(options_.varobs,1)];    % 1: U; 4: PCE inflation.
+options_.ms.vlist = [1:size(options_.varobs,1)];    % 1: U; 4: PCE inflation.
-options_.ms.varlist=cellstr(options_.varobs);
+options_.ms.varlist=cellstr(options_.varobs);
-options_.ms.log_var = sort(varlist_indices(options_.ms.vlistlog,options_.varobs));   % subset of "options_.ms.vlist.  Variables in log level so that differences are in **monthly** growth, unlike R and U which are in annual percent (divided by 100 already).
+options_.ms.log_var = sort(varlist_indices(options_.ms.vlistlog,options_.varobs));   % subset of "options_.ms.vlist.  Variables in log level so that differences are in **monthly** growth, unlike R and U which are in annual percent (divided by 100 already).
-options_.ms.percent_var =setdiff(options_.ms.vlist,options_.ms.log_var);
+options_.ms.percent_var =setdiff(options_.ms.vlist,options_.ms.log_var);
-%options_.ms.restriction_fname='ftd_upperchol3v';   %Only used by msstart2.m.
+%options_.ms.restriction_fname='ftd_upperchol3v';   %Only used by msstart2.m.
-ylab = options_.ms.varlist;
+ylab = options_.ms.varlist;
-xlab = options_.ms.varlist;
+xlab = options_.ms.varlist;
-
+
-%----------------
+%----------------
-nvar = size(options_.varobs,1);   % number of endogenous variables
+nvar = size(options_.varobs,1);   % number of endogenous variables
-nlogeno = length(options_.ms.log_var);  % number of endogenous variables in options_.ms.log_var
+nlogeno = length(options_.ms.log_var);  % number of endogenous variables in options_.ms.log_var
-npereno = length(options_.ms.percent_var);  % number of endogenous variables in options_.ms.percent_var
+npereno = length(options_.ms.percent_var);  % number of endogenous variables in options_.ms.percent_var
-if (nvar~=(nlogeno+npereno))
+if (nvar~=(nlogeno+npereno))
-   disp(' ')
+   disp(' ')
-   warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist')
+   warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist')
-   disp('Press ctrl-c to abort')
+   disp('Press ctrl-c to abort')
-   return
+   return
-elseif (nvar==length(options_.ms.vlist))
+elseif (nvar==length(options_.ms.vlist))
-   nexo=1;    % only constants as an exogenous variable.  The default setting.
+   nexo=1;    % only constants as an exogenous variable.  The default setting.
-elseif (nvar<length(options_.ms.vlist))
+elseif (nvar<length(options_.ms.vlist))
-   nexo=length(options_.ms.vlist)-nvar+1;
+   nexo=length(options_.ms.vlist)-nvar+1;
-else
+else
-   disp(' ')
+   disp(' ')
-   warning('Make sure there are only nvar endogenous variables in options_.ms.vlist')
+   warning('Make sure there are only nvar endogenous variables in options_.ms.vlist')
-   disp('Press ctrl-c to abort')
+   disp('Press ctrl-c to abort')
-   return
+   return
-end
+end
-
+
-
+
-%------- A specific sample is considered for estimation -------
+%------- A specific sample is considered for estimation -------
-yrStart=options_.ms.initial_year;
+yrStart=options_.ms.initial_year;
-qmStart=options_.ms.initial_subperiod;
+qmStart=options_.ms.initial_subperiod;
-yrEnd=options_.ms.final_year;
+yrEnd=options_.ms.final_year;
-qmEnd=options_.ms.final_subperiod;
+qmEnd=options_.ms.final_subperiod;
-%options_.forecast = 4;   % number of years for forecasting
+%options_.forecast = 4;   % number of years for forecasting
-if options_.forecast<1
+if options_.forecast<1
-   error('To be safe, the number of forecast years should be at least 1')
+   error('To be safe, the number of forecast years should be at least 1')
-end
+end
-ystr=num2str(yrEnd);
+forelabel = [num2str(yrEnd) ':' num2str(qmEnd) ' Forecast'];
-forelabel = [ ystr(3:4) ':' num2str(qmEnd) ' Forecast'];
+
-
+nSample=(yrEnd-yrStart)*options_.ms.freq + (qmEnd-qmStart+1);
-nSample=(yrEnd-yrStart)*options_.ms.freq + (qmEnd-qmStart+1);
+if qmEnd==options_.ms.freq
-if qmEnd==options_.ms.freq
+   E1yrqm = [yrEnd+1 1];  % first year and quarter (month) after the sample
-   E1yrqm = [yrEnd+1 1];  % first year and quarter (month) after the sample
+else
-else
+   E1yrqm = [yrEnd qmEnd+1];  % first year and quarter (month) after the sample
-   E1yrqm = [yrEnd qmEnd+1];  % first year and quarter (month) after the sample
+end
-end
+E2yrqm = [yrEnd+options_.forecast qmEnd];   % end at the last month (quarter) of a calendar year after the sample
-E2yrqm = [yrEnd+options_.forecast qmEnd];   % end at the last month (quarter) of a calendar year after the sample
+[fdates,nfqm]=fn_calyrqm(options_.ms.freq,E1yrqm,E2yrqm);   % forecast dates and number of forecast dates
-[fdates,nfqm]=fn_calyrqm(options_.ms.freq,E1yrqm,E2yrqm);   % forecast dates and number of forecast dates
+[sdates,nsqm] = fn_calyrqm(options_.ms.freq,[yrStart qmStart],[yrEnd qmEnd]);
-[sdates,nsqm] = fn_calyrqm(options_.ms.freq,[yrStart qmStart],[yrEnd qmEnd]);
+   % sdates: dates for the whole sample (including options_.ms.nlags)
-   % sdates: dates for the whole sample (including options_.ms.nlags)
+if nSample~=nsqm
-if nSample~=nsqm
+   warning('Make sure that nSample is consistent with the size of sdates')
-   warning('Make sure that nSample is consistent with the size of sdates')
+   disp('Hit any key to continue, or ctrl-c to abort')
-   disp('Hit any key to continue, or ctrl-c to abort')
+   pause
-   pause
+end
-end
+imstp = 4*options_.ms.freq;    % <<>>  impulse responses (4 years)
-imstp = 4*options_.ms.freq;    % <<>>  impulse responses (4 years)
+nayr = 4; %options_.forecast;  % number of years before forecasting for plotting.
-nayr = 4; %options_.forecast;  % number of years before forecasting for plotting.
+
-
+
-
+%------- Prior, etc. -------
-%------- Prior, etc. -------
+%options_.ms.nlags = 4;        % number of options_.ms.nlags
-%options_.ms.nlags = 4;        % number of options_.ms.nlags
+%options_.ms.cross_restrictions = 0;   % 1: cross-A0-and-A+ restrictions; 0: options_.ms.restriction_fname is all we have
-%options_.ms.cross_restrictions = 0;   % 1: cross-A0-and-A+ restrictions; 0: options_.ms.restriction_fname is all we have
+            % Example for indxOres==1: restrictions of the form P(t) = P(t-1).
-            % Example for indxOres==1: restrictions of the form P(t) = P(t-1).
+%options_.ms.contemp_reduced_form = 0;  % 1: contemporaneous recursive reduced form; 0: restricted (non-recursive) form
-%options_.ms.contemp_reduced_form = 0;  % 1: contemporaneous recursive reduced form; 0: restricted (non-recursive) form
+%options_.ms.real_pseudo_forecast = 0;  % 1: options_.ms.real_pseudo_forecast forecasts; 0: real time forecasts
-%options_.ms.real_pseudo_forecast = 0;  % 1: options_.ms.real_pseudo_forecast forecasts; 0: real time forecasts
+%options_.ms.bayesian_prior = 1;  % 1: Bayesian prior; 0: no prior
-%options_.ms.bayesian_prior = 1;  % 1: Bayesian prior; 0: no prior
+indxDummy = options_.ms.bayesian_prior;  % 1: add dummy observations to the data; 0: no dummy added.
-indxDummy = options_.ms.bayesian_prior;  % 1: add dummy observations to the data; 0: no dummy added.
+%options_.ms.dummy_obs = 0;  % No dummy observations for xtx, phi, fss, xdatae, etc.  Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
-%options_.ms.dummy_obs = 0;  % No dummy observations for xtx, phi, fss, xdatae, etc.  Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
+%if indxDummy
-%if indxDummy
+%   options_.ms.dummy_obs=nvar+1;         % number of dummy observations
-%   options_.ms.dummy_obs=nvar+1;         % number of dummy observations
+%else
-%else
+%   options_.ms.dummy_obs=0;    % no dummy observations
-%   options_.ms.dummy_obs=0;    % no dummy observations
+%end
-%end
+%=== The following mu is effective only if options_.ms.bayesian_prior==1.
-%=== The following mu is effective only if options_.ms.bayesian_prior==1.
+
-
+mu = options_.ms.coefficients_prior_hyperparameters;
-mu = options_.ms.coefficients_prior_hyperparameters;
+
-
+%   mu(1): overall tightness and also for A0;
-%   mu(1): overall tightness and also for A0;
+%   mu(2): relative tightness for A+;
-%   mu(2): relative tightness for A+;
+%   mu(3): relative tightness for the constant term;
-%   mu(3): relative tightness for the constant term;
+%   mu(4): tightness on lag decay;  (1)
-%   mu(4): tightness on lag decay;  (1)
+%   mu(5): weight on nvar sums of coeffs dummy observations (unit roots);
-%   mu(5): weight on nvar sums of coeffs dummy observations (unit roots);
+%   mu(6): weight on single dummy initial observation including constant
-%   mu(6): weight on single dummy initial observation including constant
+%           (cointegration, unit roots, and stationarity);
-%           (cointegration, unit roots, and stationarity);
+%
-%
+%
-%
+hpmsmd = [0.0; 0.0];
-hpmsmd = [0.0; 0.0];
+indxmsmdeqn = [0; 0; 0; 0];  %This option disenable using this in fn_rnrprior_covres_dobs.m
-indxmsmdeqn = [0; 0; 0; 0];  %This option disenable using this in fn_rnrprior_covres_dobs.m
+
-
+
-
+tdf = 3;          % degrees of freedom for t-dist for initial draw of the MC loop
-tdf = 3;          % degrees of freedom for t-dist for initial draw of the MC loop
+nbuffer = 1000;        % a block or buffer of draws (buffer) that is saved to the disk (not memory)
-nbuffer = 1000;        % a block or buffer of draws (buffer) that is saved to the disk (not memory)
+ndraws1=1*nbuffer;         % 1st part of Monte Carlo draws
-ndraws1=1*nbuffer;         % 1st part of Monte Carlo draws
+ndraws2=10*ndraws1;        % 2nd part of Monte Carlo draws
-ndraws2=10*ndraws1;        % 2nd part of Monte Carlo draws
+% seednumber = options_.DynareRandomStreams.seed; %7910;    %472534;   % if 0, random state at each clock time
-% seednumber = options_.DynareRandomStreams.seed; %7910;    %472534;   % if 0, random state at each clock time
+%            % good one 420 for [29 45], [29 54]
-%            % good one 420 for [29 45], [29 54]
+% if seednumber
-% if seednumber
+%    randn('state',seednumber);
-%    randn('state',seednumber);
+%    rand('state',seednumber);
-%    rand('state',seednumber);
+% else
-% else
+%    randn('state',fix(100*sum(clock)));
-%    randn('state',fix(100*sum(clock)));
+%    rand('state',fix(100*sum(clock)));
-%    rand('state',fix(100*sum(clock)));
+% end
-% end
+%  nstarts=1         % number of starting points
-%  nstarts=1         % number of starting points
+%  imndraws = nstarts*ndraws2;   % total draws for impulse responses or forecasts
-%  imndraws = nstarts*ndraws2;   % total draws for impulse responses or forecasts
+%<<<<<<<<<<<<<<<<<<<
-%<<<<<<<<<<<<<<<<<<<
+
-
+
-
+
-
+
-
+
-

matlab/ms-sbvar/plot_ms_forecast.m

@@ -1,16 +1,21 @@
-function plot_ms_forecast(M_,options_,forecast,title_,save_graph_formats,TeX)
+function plot_ms_forecast(M_, options_, forecast, figure_name)
-% function plot_ms_forecast(M_,options_,forecast,title_,save_graph_formats,TeX)
+% function plot_ms_forecast(M_, options_, forecast, figure_name)
 % plots the forecast from the output from a ms-sbvar
 %
 % INPUTS
-%   M_
+%    M_:          (struct)    model structure
-%   forecast should be in the form (percentile x horizon x nvar ), if banded otherwise
+%    options_:    (struct)    options
-%     ( horizon x nvar )
+%    forecast:    (matrix)    in the form (percentile x horizon x nvar ), if banded otherwise
+%                             ( horizon x nvar )
+%    figure_name: (string)    title
 %
-%   title: optional super title
+% OUTPUTS
+%    none
 %
+% SPECIAL REQUIREMENTS
+%    none
 
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -26,15 +31,12 @@ function plot_ms_forecast(M_,options_,forecast,title_,save_graph_formats,TeX)
 %
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-    
+
     nc = 2;
     nr = 2;
     nvars = M_.endo_nbr;
     endo_names = M_.endo_names;
-    
     var_list = endo_names(1:M_.orig_endo_nbr,:);
-
-    i_var = [];
     names = {};
     tex_names = {};
     m = 1;
@@ -49,16 +51,9 @@ function plot_ms_forecast(M_,options_,forecast,title_,save_graph_formats,TeX)
             tex_names{m} = tex_name;
             m = m + 1;
         end
-        i_var = [i_var; tmp];
     end
-    nvar = length(i_var);
+
-    
     dims = size(forecast);
-    
-    if nargin < 3
-        title_ = '';
-    end
-    
     if (length(dims) == 2)
         % Point Forecast (horizon x nvars )
         horizon = dims(1);
@@ -66,40 +61,40 @@ function plot_ms_forecast(M_,options_,forecast,title_,save_graph_formats,TeX)
     elseif (length(dims) == 3)
         % Banded Forecast
         horizon = dims(2);
-        num_percentiles = dims(1);  
+        num_percentiles = dims(1);
     else
         error('The impulse response matrix passed to be plotted does not appear to be the correct size');
     end
 
     if num_percentiles == 1
-        plot_point_forecast(forecast, nvars, nr, nc, var_list, title_, ...
+        plot_point_forecast(forecast, nvars, nr, nc, var_list, figure_name, ...
-            save_graph_formats, TeX, names, tex_names, ...
+            options_.graph_save_formats, options_.TeX, names, tex_names, ...
             [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
     else
         plot_banded_forecast(forecast, nvars, nr, nc, var_list, num_percentiles, ...
-            title_, save_graph_formats, TeX, names, tex_names, ...
+            figure_name, options_.graph_save_formats, options_.TeX, names, tex_names, ...
             [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
     end
 
 end
 
-function plot_point_forecast(forecast,nvars,nr,nc,endo_names,title_,save_graph_formats,TeX,names,tex_names,dirname)
+function plot_point_forecast(forecast,nvars,nr,nc,endo_names,figure_name,save_graph_formats,TeX,names,tex_names,dirname)
     if nvars > nr*nc
-        graph_name = 'MS-Forecast (1)';
+        graph_name = ['MS (1) ' figure_name];
-        fig = figure('Name','Forecast (I)'); 
+        figure('Name', graph_name);
     else
-        graph_name = 'MS-Forecast';
+        graph_name = figure_name;
-        fig = figure('Name','Forecast'); 
+        figure('Name', graph_name);
-    end    
+    end
     m = 1;
     n_fig = 1;
     for j=1:nvars
         if m > nr*nc
-            graph_name = ['MS-Forecast (' int2str(n_fig) ')']
+            graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
             dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
                            save_graph_formats,TeX,names,tex_names,graph_name);
             n_fig =n_fig+1;
-            figure('Name',['MS-Forecast (' int2str(n_fig) ')']);
+            figure('Name', graph_name);
             m = 1;
         end
         subplot(nr,nc,m);
@@ -115,19 +110,19 @@ function plot_point_forecast(forecast,nvars,nr,nc,endo_names,title_,save_graph_f
     end
 end
 
-function plot_banded_forecast(forecast,nvars,nr,nc,endo_names,num_percentiles,title_,save_graph_formats,TeX,names,tex_names,dirname)
+function plot_banded_forecast(forecast,nvars,nr,nc,endo_names,num_percentiles,figure_name,save_graph_formats,TeX,names,tex_names,dirname)
     if nvars > nr*nc
-        graph_name = 'MS-Forecast (1)';
+        graph_name = ['MS (1) ' figure_name];
-        fig = figure('Name','Forecast (I)'); 
+        figure('Name', graph_name);
     else
-        graph_name = 'MS-Forecast';
+        graph_name = figure_name;
-        fig = figure('Name','Forecast'); 
+        figure('Name', graph_name);
-    end    
+    end
     m = 1;
     n_fig = 1;
     for j=1:nvars
         if m > nr*nc
-            graph_name = ['MS-Forecast (' int2str(n_fig) ')'];
+            graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
             dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
                            save_graph_formats,TeX,names,tex_names,graph_name);
             n_fig =n_fig+1;

matlab/ms-sbvar/plot_ms_irf.m

@@ -1,21 +1,21 @@
-function plot_ms_irf(M_, options_, irf, names, title_, varlist)
+function plot_ms_irf(M_, options_, irf, figure_name, varlist)
-% function plot_ms_irf(M_, options_, irf, names, title_, varlist)
+% function plot_ms_irf(M_, options_, irf, figure_name, varlist)
 % plots the impulse responses from the output from a ms-sbvar
 %
 % INPUTS
-%   M_
+%    M_:          (struct)    model structure
-%   irf should be in the form (percentile x horizon x (nvar x nvar)), if banded otherwise
+%    options_:    (struct)    options
-%     ( horizon x (nvar x nvar) )
+%    irf:         (matrix)    in the form (percentile x horizon x (nvar x nvar)), if banded otherwise
+%                             ( horizon x (nvar x nvar) )
+%    figure_name: (string)    title
 %
-%   names: character list of the names of the variables
+% OUTPUTS
+%    none
 %
-%   title: optional super title
+% SPECIAL REQUIREMENTS
-%
+%    none
-% The element in position (k,i+j*nvars) of ir is the response of the ith 
-% variable to the jth shock at horizon k.  Horizon 0 is the contemporaneous 
-% response.
 
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -33,19 +33,16 @@ function plot_ms_irf(M_, options_, irf, names, title_, varlist)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
     if nargin < 4
-        title_ = '';
+        figure_name = '';
     end
-    
+
-    nc = 2;
-    nr = 2;
     nvars = M_.endo_nbr;
     endo_names = M_.endo_names;
-    
+
     if isempty(varlist)
         var_list = endo_names(1:M_.orig_endo_nbr,:);
     end
-    
+
-    i_var = [];
     names = {};
     tex_names = {};
     m = 1;
@@ -54,7 +51,6 @@ function plot_ms_irf(M_, options_, irf, names, title_, varlist)
         if isempty(tmp)
             error([var_list(i,:) ' isn''t and endogenous variable'])
         end
-        i_var = [i_var; tmp];
         tex_name = deblank(M_.endo_names_tex(tmp,:));
         if ~isempty(tex_name)
             names{m} = deblank(var_list(i,:));
@@ -71,11 +67,8 @@ function plot_ms_irf(M_, options_, irf, names, title_, varlist)
             m = m + 1;
         end
     end
-    
-    nvar = length(i_var);
 
     dims = size(irf);
-    
     if (length(dims) == 2)
         % Point IRF (horizon x (nvarsxnvars) )
         horizon = dims(1);
@@ -87,11 +80,11 @@ function plot_ms_irf(M_, options_, irf, names, title_, varlist)
     else
         error('The impulse response matrix passed to be plotted does not appear to be the correct size');
     end
-    
+
     if size(endo_names,1) ~= nvars
-        error('The names passed are not the same length as the number of variables')
+        error('The names passed are not the same length as the number of variables');
     end
-    
+
     if num_percentiles == 1
         % loop through the shocks
         for s=1:nvars
@@ -100,7 +93,7 @@ function plot_ms_irf(M_, options_, irf, names, title_, varlist)
                 shock(:,i) = irf(:,((i-1) + ((s-1)*nvars)+1));
             end
             plot_point_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
-                title_, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
+                figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
         end
     else
         for s=1:nvars
@@ -111,26 +104,25 @@ function plot_ms_irf(M_, options_, irf, names, title_, varlist)
                 end
             end
             plot_banded_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
-                title_, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
+                figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
         end
     end
-    
 end
 
-function [fig] = plot_point_irf_for_shock(irf,nvars,endo_names,shock_name,title_,dirname,options_,names,tex_names)
+function [fig] = plot_point_irf_for_shock(irf,nvars,endo_names,shock_name,figure_name,dirname,options_,names,tex_names)
-    fig = figure('Name',title_);
+    fig = figure('Name',figure_name);
     for k=1:nvars
         subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
         plot(irf(:,k))
         disp([endo_names(k,:) ' shock from ' shock_name]);
         title([endo_names(k,:) ' shock from ' shock_name]);
     end
-    dyn_save_graph(dirname,[title_ ' ' shock_name],options_.graph_save_formats, ...
+    dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
-                   options_.TeX,names,tex_names,[title_ ' ' shock_name]);
+                   options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
 end
 
-function [fig] = plot_banded_irf_for_shock(irf,nvars, endo_names, shock_name,title_,dirname,options_,names,tex_names)
+function [fig] = plot_banded_irf_for_shock(irf,nvars, endo_names, shock_name,figure_name,dirname,options_,names,tex_names)
-    fig = figure('Name',title_);
+    fig = figure('Name',figure_name);
     npercentiles = size(irf,3);
     for k=1:nvars
         subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
@@ -142,8 +134,6 @@ function [fig] = plot_banded_irf_for_shock(irf,nvars, endo_names, shock_name,tit
         disp([endo_names(k,:) ' shock from ' shock_name]);
         title([endo_names(k,:) ' shock from ' shock_name]);
     end
-    dyn_save_graph(dirname,[title_ ' ' shock_name],options_.graph_save_formats, ...
+    dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
-                   options_.TeX,names,tex_names,[title_ ' ' shock_name]);
+                   options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
 end
-
-

matlab/ms-sbvar/plot_ms_variance_decomposition.m

@@ -1,12 +1,14 @@
-function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_formats, TeX, varargin)
+function plot_ms_variance_decomposition(M_, options_, vd, figure_name, varargin)
-% function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_formats, TeX, varargin)
+% function plot_ms_variance_decomposition(M_, options_, vd, figure_name, varargin)
 % plot the variance decomposition of shocks
 %
-% Inputs
+% INPUTS
-%       M_
+%    M_:          (struct)    model structure
-%		shocks: matrix of the individual shocks Tx(KxK)with J=number of shocks
+%    options_:    (struct)    options
+%    vd:          (matrix)    variance decomposition
+%    figure_name: (string)    graph name
 %
-% Optional Inputs
+% OPTIONAL INPUTS
 %		'data': the actual data, TxK with K=number of data series
 %		'steady': the steady state value, TxK
 %		'shock_names': to specify the names of the shocks
@@ -14,10 +16,13 @@ function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_for
 %		'dates': pass a date vector to use, otherwise will just index on 1:T
 %		'colors': Jx3 list of the rgb colors to use for each shock
 %
-% Example:
+% OUTPUTS
-% plot_historic_decomposition(shocks,'VD','shock_names',shock_names,'series_names',series_names)
+%    none
+%
+% SPECIAL REQUIREMENTS
+%    none
 
-% Copyright (C) 2011 Dynare Team
+% Copyright (C) 2011-2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -34,6 +39,11 @@ function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_for
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
+if length(size(vd)) == 3
+    plot_ms_variance_decomposition_error_bands(M_, options_, vd, figure_name);
+    return;
+end
+
     nvars = M_.endo_nbr;
     endo_names = M_.endo_names;
 
@@ -48,11 +58,13 @@ function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_for
             m = m + 1;
         end
     end
-    
+
     dims = size(vd);
     if length(dims) == 3
-  	[T,K,J] = dims;
+        T = dims(1);
-  	shocks = vd;
+        K = dims(2);
+        J = dims(3);
+        shocks = vd;
     else
         T = dims(1);
         K = nvars;
@@ -65,30 +77,25 @@ function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_for
         end
         shocks = temp_vd;
     end
-    
+
     for i=1:nvars
         shock_names{i} = endo_names(i,:);
         series_names{i} = endo_names(i,:);
     end
 
-    if nargin < 2
+    x = [1:T];
-        title_ = '';
+    plot_dates = 0;
-    end
-
-    x = [1:T]; plot_dates = 0;
-    
     data = 0;
     steady = 0;
-    
     colors = [ .1 .1 .75
                .8 0 0
                1 .7 .25
-               1 1 0	
+               1 1 0
-               .5 1 .5 
+               .5 1 .5
                .7 .7 .1
                .5 .6 .2
                .1 .5 .1];
-    
+
     % overide the defaults with optional inputs
     for i=1:length(varargin)
         if strcmpi(varargin{i},'data')
@@ -105,12 +112,11 @@ function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_for
             colors = varargin{i+1};
         end
     end
-    
+
     % add an extra period to the time series
     x(T+1) = x(T) + (x(T) - x(T-1));
 
-    
+    figure('Name',figure_name)
-    figure('Name',title_)
     for k=1:K
         % Go through each series
         subplot(K,1,k);
@@ -144,13 +150,13 @@ function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_for
             plot(x(2:end)',steady(:,k), '--k','LineWidth',2.25);
         end
         if k==K
-	    if exist('OCTAVE_VERSION')
+            if exist('OCTAVE_VERSION')
                 legend(shock_names,'Location','SouthOutside');
-	    else 
+            else
-                legend(shock_names,'Location','BestOutside','Orientation','horizontal'); 
+                legend(shock_names,'Location','BestOutside','Orientation','horizontal');
             end
         end
-        
+
         hold off
         if plot_dates
             datetick 'x';
@@ -159,9 +165,9 @@ function plot_ms_variance_decomposition(M_, options_, vd, title_, graph_save_for
         ylim([0 , 1])
         grid on
         title(series_names{k});
-        %suptitle(title_);
     end
     dyn_save_graph([options_.ms.output_file_tag filesep 'Output' ...
         filesep 'Variance_Decomposition'], 'MS-Variance-Decomposition', ...
-        graph_save_formats, TeX,names,tex_names,'Variance decomposition');
+        options_.graph_save_formats, options_.TeX, names, tex_names, ...
+        'Variance decomposition');
 end

matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m

@@ -0,0 +1,105 @@
+function plot_ms_variance_decomposition_error_bands(M_, options_, vddata, figure_name)
+% function plot_ms_variance_decomposition_error_bands(M_, options_, vddata, figure_name)
+% plots the variance decomposition with percentiles
+%
+% INPUTS
+%    M_:          (struct)    model structure
+%    options_:    (struct)    options
+%    vddata:      (matrix)    variance_decomposition (percentile, options_.ms.horizon, nvar
+%                             x nvar)
+%    figure_name: (string)    title
+%
+% OUTPUTS
+%    none
+%
+% SPECIAL REQUIREMENTS
+%    none
+
+% Copyright (C) 2011-2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+nvars = M_.endo_nbr;
+endo_names = M_.endo_names;
+var_list = endo_names(1:M_.orig_endo_nbr,:);
+
+names = {};
+tex_names = {};
+m = 1;
+for i = 1:size(var_list)
+    tmp = strmatch(var_list(i,:), endo_names, 'exact');
+    if isempty(tmp)
+        error([var_list(i,:) ' isn''t an endogenous variable'])
+    end
+    tex_name = deblank(M_.endo_names_tex(tmp,:));
+    if ~isempty(tex_name)
+        names{m} = deblank(var_list(i,:));
+        tex_names{m} = tex_name;
+        m = m + 1;
+    end
+end
+
+for i=1:M_.exo_nbr
+    tex_name = deblank(M_.exo_names_tex(i,:));
+    if ~isempty(tex_name)
+        names{m} = deblank(M_.exo_names(i,:));
+        tex_names{m} = tex_name;
+        m = m + 1;
+    end
+end
+
+dims = size(vddata);
+if length(dims) ~= 3
+    error('The variance decomposition matrix passed to be plotted does not appear to be the correct size');
+end
+num_percentiles = dims(1);
+
+if size(endo_names, 1) ~= nvars
+    error('The names passed are not the same length as the number of variables')
+end
+
+for s=1:nvars
+    shock = zeros(options_.ms.horizon, nvars, num_percentiles);
+    for n=1:num_percentiles
+        for i=1:nvars
+            shock(:,i,n) = vddata(n, :, ((i-1) + ((s-1)*nvars)+1));
+        end
+    end
+    plot_banded_vddata_for_shock(shock, nvars, endo_names, ...
+        deblank(endo_names(s,:)), figure_name, ...
+        [options_.ms.output_file_tag filesep 'Output' filesep 'Variance_Decomposition'], ...
+        options_, names, tex_names);
+end
+end
+
+function [fig] = plot_banded_vddata_for_shock(vddata, nvars, endo_names, ...
+    shock_name, figure_name, dirname, options_, names, tex_names)
+fig = figure('Name', figure_name);
+npercentiles = size(vddata,3);
+for k=1:nvars
+    subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
+    for nn=1:npercentiles
+        plot(vddata(:,k,nn))
+        hold on
+    end
+    hold off
+    disp([endo_names(k,:) ' contribution to ' shock_name]);
+    title([endo_names(k,:) ' contribution to ' shock_name]);
+end
+dyn_save_graph(dirname, [figure_name ' ' shock_name], ...
+    options_.graph_save_formats, options_.TeX, names, tex_names, ...
+    [figure_name ' ' shock_name]);
+end

matlab/ms-sbvar/reshape_ascii_forecast_data.m

@@ -0,0 +1,44 @@
+function forecast_data=reshape_ascii_forecast_data(endo_nbr, psize, horizon, ascii_data)
+% function forecast_data=reshape_ascii_forecast_data(endo_nbr, psize, horizon, ascii_data)
+%
+% INPUTS
+%    endo_nbr:    number of endogenous
+%    psize:       number of percentiles
+%    horizon:     forecast horizon
+%    ascii_data:  data from .out file created by Dan's C code
+%
+% OUTPUTS
+%    forecast_data:    new 3-d array holding data with error bands
+%
+% SPECIAL REQUIREMENTS
+%    none
+
+% Copyright (C) 2011-2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+if psize <= 1
+    forecast_data = ascii_data;
+    return
+end
+
+forecast_data = zeros(psize, horizon, endo_nbr);
+for i=1:endo_nbr
+    for j=1:psize
+        forecast_data(j,:,i) = ascii_data(1+horizon*(j-1):horizon*j,i)';
+    end
+end
+end

matlab/ms-sbvar/reshape_ascii_irf_data.m

@@ -0,0 +1,44 @@
+function irf_data=reshape_ascii_irf_data(endo_nbr, psize, horizon, ascii_data)
+% function irf_data=reshape_ascii_irf_data(endo_nbr, psize, horizon, ascii_data)
+%
+% INPUTS
+%    endo_nbr:    number of endogenous
+%    psize:       number of percentiles
+%    horizon:     forecast horizon
+%    ascii_data:  data from .out file created by Dan's C code
+%
+% OUTPUTS
+%    irf_data:    new 3-d array holding data with error bands
+%
+% SPECIAL REQUIREMENTS
+%    none
+
+% Copyright (C) 2011-2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+if psize <= 1
+    irf_data = ascii_data;
+    return
+end
+
+irf_data = zeros(psize, horizon, endo_nbr*endo_nbr);
+for i=1:endo_nbr*endo_nbr
+    for j=1:psize
+        irf_data(j,:,i) = ascii_data(1+horizon*(j-1):horizon*j,i)';
+    end
+end
+end

matlab/ms-sbvar/reshape_ascii_variance_decomposition_data.m

@@ -0,0 +1,44 @@
+function vd_data=reshape_ascii_variance_decomposition_data(endo_nbr, psize, horizon, ascii_data)
+% function vd_data=reshape_ascii_vd_data(endo_nbr, psize, horizon, ascii_data)
+%
+% INPUTS
+%    endo_nbr:    number of endogenous
+%    psize:       number of percentiles
+%    horizon:     forecast horizon
+%    ascii_data:  data from .out file created by Dan's C code
+%
+% OUTPUTS
+%    vd_data:    new 3-d array holding data with error bands
+%
+% SPECIAL REQUIREMENTS
+%    none
+
+% Copyright (C) 2011-2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+if psize <= 1
+    vd_data = ascii_data;
+    return
+end
+
+vd_data = zeros(psize, horizon, endo_nbr*endo_nbr);
+for i=1:endo_nbr*endo_nbr
+    for j=1:psize
+        vd_data(j,:,i) = ascii_data(1+horizon*(j-1):horizon*j,i)';
+    end
+end
+end

matlab/non_linear_dsge_likelihood.m

@@ -101,7 +101,7 @@ function [fval,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,Dynar
 %! @end deftypefn
 %@eod:
 
-% Copyright (C) 2010-2011 Dynare Team
+% Copyright (C) 2010, 2011, 2012 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -127,7 +127,6 @@ persistent init_flag
 persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
 persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
 
-
 % Initialization of the persistent variable.
 if ~nargin || isempty(penalty)
     penalty = 1e8;
@@ -142,10 +141,10 @@ end
 fval            = [];
 ys              = [];
 trend_coeff     = [];
-cost_flag       = 1;
+exit_flag       = 1;
 
 % Set the number of observed variables
-nvobs = DynareDataset.info.vobs;
+nvobs = DynareDataset.info.nvobs;
 
 %------------------------------------------------------------------------------
 % 1. Get the structural parameters & define penalties
@@ -155,7 +154,7 @@ nvobs = DynareDataset.info.vobs;
 if (DynareOptions.mode_compute~=1) & any(xparam1<BayesInfo.lb)
     k = find(xparam1 < BayesInfo.lb);
     fval = penalty+sum((BayesInfo.lb(k)-xparam1(k)).^2);
-    cost_flag = 0;
+    exit_flag = 0;
     info = 41;
     return
 end
@@ -164,7 +163,7 @@ end
 if (DynareOptions.mode_compute~=1) & any(xparam1>BayesInfo.ub)
     k = find(xparam1>BayesInfo.ub);
     fval = penalty+sum((xparam1(k)-BayesInfo.ub(k)).^2);
-    cost_flag = 0;
+    exit_flag = 0;
     info = 42;
     return
 end
@@ -172,50 +171,50 @@ end
 % Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
 Q = Model.Sigma_e;
 H = Model.H;
-for i=1:EstimatedParameters_.nvx
+for i=1:EstimatedParameters.nvx
-    k =EstimatedParameters_.var_exo(i,1);
+    k =EstimatedParameters.var_exo(i,1);
     Q(k,k) = xparam1(i)*xparam1(i);
 end
-offset = EstimatedParameters_.nvx;
+offset = EstimatedParameters.nvx;
-if EstimatedParameters_.nvn
+if EstimatedParameters.nvn
-    for i=1:EstimatedParameters_.nvn
+    for i=1:EstimatedParameters.nvn
-        k = EstimatedParameters_.var_endo(i,1);
+        k = EstimatedParameters.var_endo(i,1);
         H(k,k) = xparam1(i+offset)*xparam1(i+offset);
     end
-    offset = offset+EstimatedParameters_.nvn;
+    offset = offset+EstimatedParameters.nvn;
 else
     H = zeros(nvobs);
 end
 
 % Get the off-diagonal elements of the covariance matrix for the structural innovations. Test if Q is positive definite.
-if EstimatedParameters_.ncx
+if EstimatedParameters.ncx
-    for i=1:EstimatedParameters_.ncx
+    for i=1:EstimatedParameters.ncx
-        k1 =EstimatedParameters_.corrx(i,1);
+        k1 =EstimatedParameters.corrx(i,1);
-        k2 =EstimatedParameters_.corrx(i,2);
+        k2 =EstimatedParameters.corrx(i,2);
         Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
         Q(k2,k1) = Q(k1,k2);
     end
     % Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
     [CholQ,testQ] = chol(Q);
-    if testQ 
+    if testQ
-        % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty. 
+        % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
         a = diag(eig(Q));
         k = find(a < 0);
         if k > 0
             fval = penalty+sum(-a(k));
-            cost_flag = 0;
+            exit_flag = 0;
             info = 43;
             return
         end
     end
-    offset = offset+EstimatedParameters_.ncx;
+    offset = offset+EstimatedParameters.ncx;
 end
 
 % Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite.
-if EstimatedParameters_.ncn 
+if EstimatedParameters.ncn
-    for i=1:EstimatedParameters_.ncn
+    for i=1:EstimatedParameters.ncn
-        k1 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,1));
+        k1 = DynareOptions.lgyidx2varobs(EstimatedParameters.corrn(i,1));
-        k2 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,2));
+        k2 = DynareOptions.lgyidx2varobs(EstimatedParameters.corrn(i,2));
         H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
         H(k2,k1) = H(k1,k2);
     end
@@ -227,17 +226,17 @@ if EstimatedParameters_.ncn
         k = find(a < 0);
         if k > 0
             fval = penalty+sum(-a(k));
-            cost_flag = 0;
+            exit_flag = 0;
             info = 44;
             return
         end
     end
-    offset = offset+EstimatedParameters_.ncn;
+    offset = offset+EstimatedParameters.ncn;
 end
 
 % Update estimated structural parameters in Mode.params.
-if EstimatedParameters_.np > 0
+if EstimatedParameters.np > 0
-    Model.params(EstimatedParameters_.param_vals(:,1)) = xparam1(offset+1:end);
+    Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
 end
 
 % Update Model.Sigma_e and Model.H.
@@ -253,11 +252,11 @@ Model.H = H;
 
 if info(1) == 1 || info(1) == 2 || info(1) == 5
     fval = penalty+1;
-    cost_flag = 0;
+    exit_flag = 0;
     return
 elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
     fval = penalty+info(2);
-    cost_flag = 0;
+    exit_flag = 0;
     return
 end
 
@@ -266,8 +265,8 @@ BayesInfo.mf = BayesInfo.mf1;
 
 % Define the deterministic linear trend of the measurement equation.
 if DynareOptions.noconstant
-    constant = zeros(nvobs,1); 
+    constant = zeros(nvobs,1);
-else    
+else
     if DynareOptions.loglinear
         constant = log(SteadyState(BayesInfo.mfys));
     else
@@ -275,7 +274,6 @@ else
     end
 end
 
-
 % Define the deterministic linear trend of the measurement equation.
 if BayesInfo.with_trend
     trend_coeff = zeros(DynareDataset.info.nvobs,1);
@@ -294,7 +292,7 @@ end
 start = DynareOptions.presample+1;
 np    = size(T,1);
 mf    = BayesInfo.mf;
-Y     = transpose(dataset_.rawdata);
+Y     = transpose(DynareDataset.rawdata);
 
 %------------------------------------------------------------------------------
 % 3. Initial condition of the Kalman filter
@@ -332,10 +330,10 @@ ReducedForm.mf1 = mf1;
 
 % Set initial condition.
 switch DynareOptions.particle.initialization
-  case 1% Initial state vector variance is the ergodic variance associated to the first order Taylor-approximation of the model. 
+  case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
     StateVectorMean = ReducedForm.constant(mf0);
     StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',1e-12,1e-12);
-  case 2% Initial state vector variance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
+  case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
     StateVectorMean = ReducedForm.constant(mf0);
     old_DynareOptionsperiods = DynareOptions.periods;
     DynareOptions.periods = 5000;
@@ -344,7 +342,7 @@ switch DynareOptions.particle.initialization
     StateVectorVariance = cov(y_');
     DynareOptions.periods = old_DynareOptionsperiods;
     clear('old_DynareOptionsperiods','y_');
-  case 3
+  case 3% Initial state vector covariance is a diagonal matrix.
     StateVectorMean = ReducedForm.constant(mf0);
     StateVectorVariance = DynareOptions.particle.initial_state_prior_std*eye(number_of_state_variables);
   otherwise
@@ -357,13 +355,13 @@ ReducedForm.StateVectorVariance = StateVectorVariance;
 % 4. Likelihood evaluation
 %------------------------------------------------------------------------------
 DynareOptions.warning_for_steadystate = 0;
-LIK = feval(DynareOptions.particle.algorithm,ReducedForm,Y,[]);
+LIK = feval(DynareOptions.particle.algorithm,ReducedForm,Y,[],DynareOptions);
 if imag(LIK)
     likelihood = penalty;
-    cost_flag  = 0;
+    exit_flag  = 0;
 elseif isnan(LIK)
     likelihood = penalty;
-    cost_flag  = 0;
+    exit_flag  = 0;
 else
     likelihood = LIK;
 end

matlab/numgrad2.m

@@ -32,6 +32,7 @@ g=zeros(n,1);
 badg=0;
 goog=1;
 scale=1;
+g0 = 0;
 for i=1:n
     if size(x,1)>size(x,2)
         tvecv=tvec(i,:);
@@ -57,4 +58,4 @@ for i=1:n
         g(i) = 0;
         badg = 1;
     end
-end
+end

matlab/parallel/AnalyseComputationalEnvironment.m

@@ -559,6 +559,7 @@ for Node=1:length(DataInput) % To obtain a recoursive function remove the 'for'
     si0=[];
     de0=[];
     
+    disp('Checking Hardware please wait ...');
     if (DataInput(Node).Local == 1)
         if Environment,
             [si0 de0]=system('grep processor /proc/cpuinfo');
@@ -579,7 +580,7 @@ for Node=1:length(DataInput) % To obtain a recoursive function remove the 'for'
     
     
     RealCPUnbr='';
-    keyboard;
+%    keyboard;
     RealCPUnbr=GiveCPUnumber(de0,OStargetUnix);
     
     % Questo controllo penso che si possa MIGLIORARE!!!!!

matlab/parallel/closeSlave.m

@@ -1,4 +1,4 @@
-function closeSlave(Parallel,TmpFolder),
+function closeSlave(Parallel,TmpFolder,partial),
 % PARALLEL CONTEXT
 % In parallel context, this utility closes all remote matlab instances
 % called by masterParallel when strategy (1) is active i.e. always open (which leaves
@@ -32,6 +32,32 @@ function closeSlave(Parallel,TmpFolder),
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
+if nargin<3,
+    partial=0;
+end
+
+s=warning('off');
+
+if partial==1
+    save('slaveParallel_break','partial')
+    for indPC=1:length(Parallel),
+        if (Parallel(indPC).Local==0),
+            dynareParallelSendFiles('slaveParallel_break.mat',TmpFolder,Parallel(indPC));
+        end
+    end
+%     delete('slaveParallel_break')
+    return
+end
+if partial==-1
+    delete('slaveParallel_break.mat')
+    for indPC=1:length(Parallel),
+        if (Parallel(indPC).Local==0),
+            dynareParallelDelete( 'slaveParallel_break.mat',TmpFolder,Parallel(indPC));
+        end
+    end
+%     delete('slaveParallel_break')
+    return
+end
 
 for indPC=1:length(Parallel),
     if (Parallel(indPC).Local==0),
@@ -58,3 +84,5 @@ while(1)
     end
 end
 
+s=warning('on');
+ 

matlab/parallel/dynareParallelDelete.m

@@ -40,7 +40,7 @@ else
 end
 
 for indPC=1:length(Parallel),
-    if isunix
+    if ~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem),
         [NonServeS NonServeD]=system(['ssh ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' rm -f ',Parallel(indPC).RemoteDirectory,'/',pname,fname]);
     else
         delete(['\\',Parallel(indPC).ComputerName,'\',Parallel(indPC).RemoteDrive,'$\',Parallel(indPC).RemoteDirectory,'\',pname,fname]);

matlab/parallel/dynareParallelDeleteNewFiles.m

@@ -57,9 +57,9 @@ for indPC=1:length(Parallel),
                 fileaddress={sT(1:SlashNumberAndPosition(end)),sT(SlashNumberAndPosition(end)+1:end)};
                 dynareParallelDelete(fileaddress{2},[PRCDir,fS,fileaddress{1}],Parallel(indPC));
 
-                display('New file deleted in remote -->');
+                disp('New file deleted in remote -->');
-                display(fileaddress{2});
+                disp(fileaddress{2});
-                display('<--');
+                disp('<--');
 
             end
         else

matlab/parallel/dynareParallelGetNewFiles.m

@@ -58,9 +58,9 @@ for indPC=1:length(Parallel),
                 fileaddress={sT(1:SlashNumberAndPosition(end)),sT(SlashNumberAndPosition(end)+1:end)};
                 dynareParallelGetFiles(fileaddress,PRCDir,Parallel(indPC));
 
-                display('New file copied in local -->');
+                disp('New file copied in local -->');
-                display(fileaddress{2});
+                disp(fileaddress{2});
-                display('<--');
+                disp('<--');
 
             end
         else

matlab/parallel/dynareParallelRmDir.m

@@ -35,7 +35,25 @@ if nargin ==0,
     return
 end
 
+% security check of remote folder delete
+ok(1)=isempty(strfind(Parallel_info.RemoteTmpFolder,'..'));
+tmp1=strfind(Parallel_info.RemoteTmpFolder,'2');
+ok(2)=tmp1(1)==1;
+ok(3)=~isempty(strfind(Parallel_info.RemoteTmpFolder,'-'));
+ok(4)=~isempty(strfind(Parallel_info.RemoteTmpFolder,'h'));
+ok(5)=~isempty(strfind(Parallel_info.RemoteTmpFolder,'m'));
+ok(6)=~isempty(strfind(Parallel_info.RemoteTmpFolder,'s'));
+ok(7)=~isempty(PRCDir);
+
+if sum(ok)<7,
+    error('The name of the remote tmp folder does not comply the security standards!'),
+end
+
 for indPC=1:length(Parallel),
+    ok(1)=isempty(strfind(Parallel(indPC).RemoteDirectory,'..'));
+    if sum(ok)<7,
+        error('The remote folder path structure does not comply the security standards!'),
+    end
     while (1)
         if ~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)
             [stat NonServe] = system(['ssh ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' rm -fr ',Parallel(indPC).RemoteDirectory,'/',PRCDir,]);

matlab/parallel/fMessageStatus.m

@@ -43,7 +43,8 @@ catch
 end
 
 fslave = dir( ['slaveParallel_input',int2str(njob),'.mat']);
-if isempty(fslave),
+fbreak = dir( ['slaveParallel_break.mat']);
+if isempty(fslave) || ~isempty(fbreak),
     error('Master asked to break the job');
 end
 

matlab/parallel/fParallel.m

@@ -81,20 +81,33 @@ try,
         % Save the output result.
         save([ fname,'_output_',int2str(whoiam),'.mat'],'fOutputVar' )
     end
+    if isfield(fOutputVar,'CloseAllSlaves'),
+        CloseAllSlaves = 1;
+        fOutputVar = rmfield(fOutputVar,'CloseAllSlaves');
+        save([ fname,'_output_',int2str(whoiam),'.mat'],'fOutputVar' )
+        save(['comp_status_',funcName,int2str(whoiam),'.mat'],'CloseAllSlaves');
+    end
     
     disp(['fParallel ',int2str(whoiam),' completed.'])
 catch,
-    disp(['fParallel ',int2str(whoiam),' crashed.'])
+    theerror = lasterror;
-    fOutputVar.error = lasterror;
+    if strfind(theerror.message,'Master asked to break the job')
-    save([ fname,'_output_',int2str(whoiam),'.mat'],'fOutputVar' )
+        fOutputVar.message = theerror;
-    waitbarString = fOutputVar.error.message;
+        save([ fname,'_output_',int2str(whoiam),'.mat'],'fOutputVar' )
-    %       waitbarTitle=['Metropolis-Hastings ',options_.parallel(ThisMatlab).ComputerName];
+        waitbarString = theerror.message;
-    if Parallel(ThisMatlab).Local,
-        waitbarTitle='Local ';
     else
-        waitbarTitle=[Parallel(ThisMatlab).ComputerName];
+        disp(['fParallel ',int2str(whoiam),' crashed.'])
+        fOutputVar.error = theerror;
+        save([ fname,'_output_',int2str(whoiam),'.mat'],'fOutputVar' )
+        waitbarString = theerror.message;
+        %       waitbarTitle=['Metropolis-Hastings ',options_.parallel(ThisMatlab).ComputerName];
+        if Parallel(ThisMatlab).Local,
+            waitbarTitle='Local ';
+        else
+            waitbarTitle=[Parallel(ThisMatlab).ComputerName];
+        end
+        fMessageStatus(NaN,whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
     end
-    fMessageStatus(NaN,whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
     
 end
 diary off;