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with 837 additions and 6836 deletions
doc/guide.bbl deleted 100644 → 0
View file @ ae0f83c3
\ifx\undefined\bysame
\newcommand{\bysame}{\leavevmode\hbox to\leftmargin{\hrulefill\,\,}}
\fi
\begin{thebibliography}{xx}
\harvarditem[Collard and Juillard]{Collard and Juillard}{2001}{COLL/JUIL/01a}
{ Collard, F. and M.~Juillard}, Accuracy of stochastic perturbation methods:
The case of asset pricing models, {\it Journal of Economic Dynamics and
Control}, 2001, {\it 25}, 979--999.
\harvarditem[Schmitt-Grohe and Uribe]{Schmitt-Grohe and Uribe}{2002}{SGU/02}
{ Schmitt-Grohe, S. and M.~Uribe}, {\it Solving Dynamic General Equilibrium
Models Using a Second-Order Approximation to the Policy Function}, technical
working paper, Rutgers Univsersity 2002.
\end{thebibliography}
doc/guide.tex
View file @ a29fb27b
\documentclass[11pt,a4paper]{article}
\usepackage{bibmad,graphicx,latexsym,amssymb,times}
\usepackage[cp850]{inputenc}
\usepackage{graphicx,latexsym,amssymb,times}
\usepackage[utf8]{inputenc}
\begin{document}
\title{Stochastic simulations with {\sc Dynare}. \\ A practical guide.}
\author{Fabrice Collard (GREMAQ, University of Toulouse)\\Adapted for Dynare 4.1\\ by Michel Juillard and S\'ebastien Villemot (CEPREMAP)}
\author{Fabrice Collard (GREMAQ, University of Toulouse)\\Adapted for Dynare 4.x\\ by Michel Juillard and S\'ebastien Villemot (CEPREMAP)}
\date{First draft: February 2001\hspace{10mm}This draft: December 2009.}
\maketitle
This document describes a model involving both endogenous and exogenous state variable. We first describe the theoretical model, before showing how the perturbation method is implemented in {\sc Dynare}.
......@@ -324,8 +324,8 @@ end;
stoch_simul(periods=2000, drop=200);
\end{verbatim}
\bibliographystyle{Usmad}
\bibliography{/papers/biblio/michel}
%\bibliographystyle{Usmad}
%\bibliography{/papers/biblio/michel}
\end{document}
......
doc/internals/Makefile.am deleted 100644 → 0
View file @ ae0f83c3
EXTRA_DIST = dynare-internals.org
if ENABLE_ORG_EXPORT
html-local:
emacs --batch --visit=dynare-internals.org --funcall org-export-as-html-batch
endif
clean-local:
rm -rf *.html ltxpng
doc/internals/build_internal_documentation.m
View file @ a29fb27b
......@@ -2,7 +2,6 @@ function build_internal_documentation()
% The name of the function should be explicit...
datafiles = [];
datafiles = [ datafiles ; {'../../matlab/utilities/dataset'}, {'initialize_dataset'}];
datafiles = [ datafiles ; {'../../matlab/utilities/dataset'}, {'descriptive_statistics'}];
datafiles = [ datafiles ; {'../../matlab/utilities/dataset'}, {'compute_stdv'}];
datafiles = [ datafiles ; {'../../matlab/utilities/dataset'}, {'compute_cova'}];
......
doc/internals/internals.texi
View file @ a29fb27b
......@@ -60,7 +60,7 @@ 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 @uref{http://www.gnu.org/licenses/fdl.txt}.
A copy of the license can be found at @uref{https://www.gnu.org/licenses/fdl.txt}.
@end quotation
@end copying
......@@ -203,18 +203,18 @@ institutions who cannot afford, or do not want to pay for, MATLAB and
are willing to bear the concomitant performance loss.
The development of Dynare is mainly done at
@uref{http://www.cepremap.ens.fr, Cepremap} by a core team of
@uref{http://www.cepremap.ens.fr, CEPREMAP} by a core team of
researchers who devote part of their time to software
development. Currently the development team of Dynare is composed of
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 Economics). Financial support is provided by Cepremap,
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 Economics). Financial support is provided by CEPREMAP,
Banque de France and DSGE-net (an international research network for
DSGE modeling). Increasingly, the developer base is expanding, as
tools developed by researchers outside of Cepremap are integrated into
tools developed by researchers outside of CEPREMAP are integrated into
Dynare.
Interaction between developers and users of Dynare is central to the
......
doc/macroprocessor/Makefile.am deleted 100644 → 0
View file @ ae0f83c3
if HAVE_PDFLATEX
if HAVE_BEAMER
pdf-local: macroprocessor.pdf
endif
endif
SRC = macroprocessor.tex old-design.pdf new-design.pdf
EXTRA_DIST = $(SRC)
macroprocessor.pdf: $(SRC)
$(PDFLATEX) macroprocessor
$(PDFLATEX) macroprocessor
clean-local:
rm -f macroprocessor.pdf *.toc *.aux *.log *.nav *.snm *.vrb *.out *~
doc/macroprocessor/macroprocessor.tex deleted 100644 → 0
View file @ ae0f83c3
\documentclass{beamer}
\usepackage[utf8]{inputenc}
\usepackage{amsmath}
\usetheme{Boadilla}
\title{The Dynare Macro-processor}
\subtitle{Dynare Summer School 2012}
\author{Sébastien Villemot}
\institute{CEPREMAP}
\date{June 22, 2012}
\AtBeginSection[]
{
\begin{frame}
\frametitle{Outline}
\tableofcontents[currentsection]
\end{frame}
}
\begin{document}
\begin{frame}
\titlepage
\end{frame}
\begin{frame}
\frametitle{Outline}
\tableofcontents
\end{frame}
\section{Overview}
\begin{frame}
\frametitle{Motivation}
\begin{itemize}
\item The \textbf{Dynare language} (used in MOD files) is well suited for many economic models
\item However, as such, it lacks some useful features, such as:
\begin{itemize}
\item a loop mechanism for automatically repeating similar blocks of equations (such as in multi-country models)
\item an operator for indexed sums or products inside equations
\item a mechanism for splitting large MOD files in smaller modular files
\item the possibility of conditionally including some equations or some runtime commands
\end{itemize}
\item The \textbf{Dynare Macro-language} was specifically designed to address these issues
\item Being flexible and fairly general, it can also be helpful in other situations
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Design of the macro-language}
\begin{itemize}
\item The Dynare Macro-language provides a new set of \textbf{macro-commands} which can be inserted inside MOD files
\item Language features include:
\begin{itemize}
\item file inclusion
\item loops (\textit{for} structure)
\item conditional inclusion (\textit{if/else} structures)
\item expression substitution
\end{itemize}
\item Implemented in Dynare starting from version 4.0
\item The macro-processor transforms a MOD file with macro-commands into a MOD file without macro-commands (doing text expansions/inclusions) and then feeds it to the Dynare parser
\item The key point to understand is that the macro-processor only does \textbf{text substitution} (like the C preprocessor or the PHP language)
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Older design of Dynare (version 3)}
\includegraphics[width=0.95\linewidth]{old-design.pdf}
\end{frame}
\begin{frame}
\frametitle{Current design of Dynare (version 4)}
\includegraphics[width=0.95\linewidth]{new-design.pdf}
\end{frame}
\section{Syntax}
\begin{frame}[fragile=singleslide]
\frametitle{Macro Directives}
\begin{itemize}
\item Directives begin with an at-sign followed by a pound sign (\verb+@#+)
\item A directive produces no output, but gives instructions to the macro-processor
\item Main directives are:
\begin{itemize}
\item file inclusion: \verb+@#include+
\item definition a variable of the macro-processor: \verb+@#define+
\item conditional statements (\verb+@#if/@#ifdef/@#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.
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Inclusion directive}
\begin{itemize}
\item This directive simply includes the content of another file at the place where it is inserted.
\begin{block}{Syntax}
\verb+@#include "+\textit{filename}\verb+"+
\end{block}
\begin{block}{Example}
\begin{verbatim}
@#include "modelcomponent.mod"
\end{verbatim}
\end{block}
\item Exactly equivalent to a copy/paste of the content of the included file
\item Note that it is possible to nest includes (\textit{i.e.} to include a file from an included file)
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Variables}
\begin{itemize}
\item The macro processor maintains its own list of variables (distinct of model variables and of MATLAB variables)
\item Macro-variables can be of four types:
\begin{itemize}
\item integer
\item character string (declared between \textit{double} quotes)
\item array of integers
\item array of strings
\end{itemize}
\item No boolean type:
\begin{itemize}
\item false is represented by integer zero
\item true is any non-null integer
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Macro-expressions (1/2)}
It is possible to construct macro-expressions, using standard operators.
\begin{block}{Operators on integers}
\begin{itemize}
\item arithmetic operators: \texttt{+ - * /}
\item comparison operators: \texttt{< > <= >= == !=}
\item logical operators: \verb+&& || !+
\item integer ranges: \texttt{1:4} is equivalent to integer array \texttt{[1,2,3,4]}
\end{itemize}
\end{block}
\begin{block}{Operators on character strings}
\begin{itemize}
\item comparison operators: \texttt{== !=}
\item concatenation: \texttt{+}
\item extraction of substrings: if \texttt{s} is a string, then one can write \texttt{s[3]} or \texttt{s[4:6]}
\end{itemize}
\end{block}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Macro-expressions (2/2)}
\begin{block}{Operators on arrays}
\begin{itemize}
\item dereferencing: if \texttt{v} is an array, then \texttt{v[2]} is its $2^{\textrm{nd}}$ element
\item concatenation: \texttt{+}
\item difference \texttt{-}: returns the first operand from which the elements of the second operand have been removed
\item extraction of sub-arrays: \textit{e.g.} \texttt{v[4:6]}
\item testing membership of an array: \texttt{in} operator \\ (example:
\texttt{"b" in ["a", "b", "c"]} returns \texttt{1})
\end{itemize}
\end{block}
Macro-expressions can be used at two places:
\begin{itemize}
\item inside macro directives, directly
\item in the body of the MOD file, between an at-sign and curly braces (like \verb+@{expr}+): the macro processor will substitute the expression with its value
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Define directive}
The value of a macro-variable can be defined with the \verb+@#define+ directive.
\begin{block}{Syntax}
\verb+@#define +\textit{variable\_name}\verb+ = +\textit{expression}
\end{block}
\begin{block}{Examples}
\begin{verbatim}
@#define x = 5 // Integer
@#define y = "US" // String
@#define v = [ 1, 2, 4 ] // Integer array
@#define w = [ "US", "EA" ] // String array
@#define z = 3 + v[2] // Equals 5
@#define t = ("US" in w) // Equals 1 (true)
\end{verbatim}
\end{block}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Expression substitution}
\framesubtitle{Dummy example}
\begin{block}{Before macro-processing}
\begin{verbatim}
@#define x = [ "B", "C" ]
@#define i = 2
model;
A = @{x[i]};
end;
\end{verbatim}
\end{block}
\begin{block}{After macro-processing}
\begin{verbatim}
model;
A = C;
end;
\end{verbatim}
\end{block}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Loop directive}
\begin{block}{Syntax}
\verb+@#for +\textit{variable\_name}\verb+ in +\textit{array\_expr} \\
\verb+ +\textit{loop\_body} \\
\verb+@#endfor+
\end{block}
\begin{block}{Example: before macro-processing}
\small
\begin{verbatim}
model;
@#for country in [ "home", "foreign" ]
GDP_@{country} = A * K_@{country}^a * L_@{country}^(1-a);
@#endfor
end;
\end{verbatim}
\normalsize
\end{block}
\begin{block}{Example: after macro-processing}
\small
\begin{verbatim}
model;
GDP_home = A * K_home^a * L_home^(1-a);
GDP_foreign = A * K_foreign^a * L_foreign^(1-a);
end;
\end{verbatim}
\normalsize
\end{block}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Conditional inclusion directives (1/2)}
\begin{columns}[T]
\column{0.47\linewidth}
\begin{block}{Syntax 1}
\verb+@#if +\textit{integer\_expr} \\
\verb+ +\textit{body included if expr != 0} \\
\verb+@#endif+
\end{block}
\column{0.47\linewidth}
\begin{block}{Syntax 2}
\verb+@#if +\textit{integer\_expr} \\
\verb+ +\textit{body included if expr != 0} \\
\verb+@#else+ \\
\verb+ +\textit{body included if expr == 0} \\
\verb+@#endif+
\end{block}
\end{columns}
\begin{block}{Example: alternative monetary policy rules}
\scriptsize
\begin{verbatim}
@#define linear_mon_pol = 0 // or 1
...
model;
@#if 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{verbatim}
\scriptsize
\end{block}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Conditional inclusion directives (2/2)}
\begin{columns}[T]
\column{0.47\linewidth}
\begin{block}{Syntax 1}
\verb+@#ifdef +\textit{variable\_name} \\
\verb+ +\textit{body included if variable defined} \\
\verb+@#endif+
\end{block}
\column{0.47\linewidth}
\begin{block}{Syntax 2}
\verb+@#ifdef +\textit{variable\_name} \\
\verb+ +\textit{body included if variable defined} \\
\verb+@#else+ \\
\verb+ +\textit{body included if variable not defined} \\
\verb+@#endif+
\end{block}
\end{columns}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Echo and error directives}
\begin{itemize}
\item The echo directive will simply display a message on standard output
\item The error directive will display the message and make Dynare stop (only makes sense inside a conditional inclusion directive)
\end{itemize}
\begin{block}{Syntax}
\verb+@#echo +\textit{string\_expr} \\
\verb+@#error +\textit{string\_expr}
\end{block}
\begin{block}{Examples}
\begin{verbatim}
@#echo "Information message."
@#error "Error message!"
\end{verbatim}
\end{block}
\end{frame}
\begin{frame}
\frametitle{Saving the macro-expanded MOD file}
\begin{itemize}
\item For \textbf{debugging or learning} purposes, it is possible to save the output of the macro-processor
\item This output is a valid MOD file, obtained after processing the macro-commands of the original MOD file
% \item Useful to understand how the macro-processor works
\item Just add the \texttt{savemacro} option on the Dynare command line (after the name of your MOD file)
\item If MOD file is \texttt{filename.mod}, then the macro-expanded version will be saved in \texttt{filename-macroexp.mod}
\item You can specify the filename for the macro-expanded version with the syntax \texttt{savemacro=mymacroexp.mod}
\end{itemize}
\end{frame}
% \begin{frame}
% \frametitle{Note on error messages}
% \end{frame}
\section{Typical usages}
\begin{frame}[fragile=singleslide]
\frametitle{Modularization}
\begin{itemize}
\item The \verb+@#include+ directive can be used to split MOD files into several modular components
\item Example setup:
\begin{description}
\item[\texttt{modeldesc.mod}:] contains variable declarations, model equations and shocks declarations
\item[\texttt{simul.mod}:] includes \texttt{modeldesc.mod}, calibrates parameters and runs stochastic simulations
\item[\texttt{estim.mod}:] includes \texttt{modeldesc.mod}, declares priors on parameters and runs bayesian estimation
\end{description}
\item Dynare can be called on \texttt{simul.mod} and \texttt{estim.mod}
\item But it makes no sense to run it on \texttt{modeldesc.mod}
\item Advantage: no need to manually copy/paste the whole model (at the beginning) or changes to the model (during development)
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Indexed sums or products}
\framesubtitle{Example: moving average}
\begin{columns}[T]
\column{0.47\linewidth}
\begin{block}{Before macro-processing}
\begin{verbatim}
@#define window = 2
var x MA_x;
...
model;
...
MA_x = 1/@{2*window+1}*(
@#for i in -window:window
+x(@{i})
@#endfor
);
...
end;
\end{verbatim}
\end{block}
\column{0.47\linewidth}
\begin{block}{After macro-processing}
\begin{verbatim}
var x MA_x;
...
model;
...
MA_x = 1/5*(
+x(-2)
+x(-1)
+x(0)
+x(1)
+x(2)
);
...
end;
\end{verbatim}
\end{block}
\end{columns}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Multi-country models}
\framesubtitle{MOD file skeleton example}
\scriptsize
\begin{verbatim}
@#define countries = [ "US", "EA", "AS", "JP", "RC" ]
@#define nth_co = "US"
@#for co in countries
var Y_@{co} K_@{co} L_@{co} i_@{co} E_@{co} ...;
parameters a_@{co} ...;
varexo ...;
@#endfor
model;
@#for co in countries
Y_@{co} = K_@{co}^a_@{co} * L_@{co}^(1-a_@{co});
...
@# if co != nth_co
(1+i_@{co}) = (1+i_@{nth_co}) * E_@{co}(+1) / E_@{co}; // UIP relation
@# else
E_@{co} = 1;
@# endif
@#endfor
end;
\end{verbatim}
\normalsize
\end{frame}
\begin{frame}
\frametitle{Endogeneizing parameters (1/4)}
\begin{itemize}
\item When doing the steady-state calibration of the model, it may be useful to consider a parameter as an endogenous (and vice-versa)
\item Example:
\begin{gather*}
y = \left(\alpha^{\frac{1}{\xi}} \ell^{1-\frac{1}{\xi}} + (1-\alpha)^{\frac{1}{\xi}}k^{1-\frac{1}{\xi}}\right)^{\frac{\xi}{\xi - 1}} \\
lab\_rat = \frac{w \ell}{p y}
\end{gather*}
\item In the model, $\alpha$ is a (share) parameter, and $lab\_rat$ is an endogenous variable
\item We observe that:
\begin{itemize}
\item calibrating $\alpha$ is not straigthforward!
\item on the contrary, we have real world data for $lab\_rat$
\item it is clear that these two variables are economically linked
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Endogeneizing parameters (2/4)}
\begin{itemize}
\item Therefore, when computing the steady state:
\begin{itemize}
\item we make $\alpha$ an endogenous variable and $lab\_rat$ a parameter
\item we impose an economically relevant value for $lab\_rat$
\item the solution algorithm deduces the implied value for $\alpha$
\end{itemize}
\item We call this method ``variable flipping''
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Endogeneizing parameters (3/4)}
\framesubtitle{Example implementation}
\begin{itemize}
\item File \texttt{modeqs.mod}:
\begin{itemize}
\item contains variable declarations and model equations
\item For declaration of \texttt{alpha} and \texttt{lab\_rat}:
\footnotesize
\begin{verbatim}
@#if steady
var alpha;
parameter lab_rat;
@#else
parameter alpha;
var lab_rat;
@#endif
\end{verbatim}
\normalsize
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{Endogeneizing parameters (4/4)}
\framesubtitle{Example implementation}
\begin{itemize}
\item File \texttt{steady.mod}:
\begin{itemize}
\item begins with \verb+@#define steady = 1+
\item then with \verb+@#include "modeqs.mod"+
\item initializes parameters (including \texttt{lab\_rat}, excluding \texttt{alpha})
\item computes steady state (using guess values for endogenous, including \texttt{alpha})
\item saves values of parameters and endogenous at steady-state in a file, using the \texttt{save\_params\_and\_steady\_state} command
\end{itemize}
\item File \texttt{simul.mod}:
\begin{itemize}
\item begins with \verb+@#define steady = 0+
\item then with \verb+@#include "modeqs.mod"+
\item loads values of parameters and endogenous at steady-state from file, using the \texttt{load\_params\_and\_steady\_state} command
\item computes simulations
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{MATLAB loops vs macro-processor loops (1/3)}
Suppose you have a model with a parameter $\rho$, and you want to make
simulations for three values: $\rho = 0.8, 0.9, 1$. There are
several ways of doing this:
\begin{block}{With a MATLAB loop}
\begin{verbatim}
rhos = [ 0.8, 0.9, 1];
for i = 1:length(rhos)
rho = rhos(i);
stoch_simul(order=1);
end
\end{verbatim}
\end{block}
\begin{itemize}
\item The loop is not unrolled
\item MATLAB manages the iterations
\item Interesting when there are a lot of iterations
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{MATLAB loops vs macro-processor loops (2/3)}
\begin{block}{With a macro-processor loop (case 1)}
\begin{verbatim}
rhos = [ 0.8, 0.9, 1];
@#for i in 1:3
rho = rhos(@{i});
stoch_simul(order=1);
@#endfor
\end{verbatim}
\end{block}
\begin{itemize}
\item Very similar to previous example
\item Loop is unrolled
\item Dynare macro-processor manages the loop index but not the data array (\texttt{rhos})
\end{itemize}
\end{frame}
\begin{frame}[fragile=singleslide]
\frametitle{MATLAB loops vs macro-processor loops (3/3)}
\begin{block}{With a macro-processor loop (case 2)}
\begin{verbatim}
@#for rho_val in [ "0.8", "0.9", "1"]
rho = @{rho_val};
stoch_simul(order=1);
@#endfor
\end{verbatim}
\end{block}
\begin{itemize}
\item Advantage: shorter syntax, since list of values directly given in the loop construct
\item Note that values are given as character strings (the macro-processor does not
know floating point values)
\item Inconvenient: can not reuse an array stored in a MATLAB variable
\end{itemize}
\end{frame}
% \begin{frame}[fragile=singleslide]
% \frametitle{Possible future developments}
% \begin{itemize}
% \item Find a nicer syntax for indexed sums/products
% \item Implement other control structures: \texttt{elsif}, \texttt{switch/case}, \texttt{while/until} loops
% \item Implement macro-functions (or templates), with a syntax like:
% \small
% \verb+@#define QUADRATIC_COST(x, x_ss, phi) = phi/2*(x/x_ss-1)^2+
% \normalsize
% \end{itemize}
% \end{frame}
\section{Dynare for Octave}
\begin{frame}
\frametitle{Dynare for Octave (1/2)}
\begin{itemize}
\item GNU Octave (or simply Octave) is a high-level language, primarily intended for numerical computations
\item Basically, it is a free clone of MATLAB: same syntax, almost same set of functions
\item Runs on Windows, GNU/Linux and Mac OS X
\item Advantages:
\begin{itemize}
\item free software, no license fee to pay
\item source code available
\item dynamic and responsive community of users and developers
\end{itemize}
\item Inconvenients:
\begin{itemize}
\item slower than MATLAB
\item less user friendly (however note that there is a graphical fronted
called ``qtoctave'' that can be installed on top of Octave; a native
frontend is under development)
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Dynare for Octave (2/2)}
\begin{itemize}
\item Since version 4.0, Dynare works on top of Octave
\item This makes Dynare 100\% free software
\item Almost all features of Dynare work with Octave (very few exceptions)
\item For more information: \\
\url{http://www.dynare.org/DynareWiki/DynareOctave}
\end{itemize}
\end{frame}
\end{document}
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.. default-domain:: dynare
############
Bibliography
############
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* Geweke, John (1999): “Using simulation methods for Bayesian econometric models: Inference, development and communication,” *Econometric Reviews*, 18(1), 1–73.
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* Guerrieri, Luca and Matteo Iacoviello (2015): “OccBin: A toolkit for solving dynamic models with occasionally binding constraints easily,” *Journal of Monetary Economics*, 70, 22–38.
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* Harvey, Andrew C. and Garry D.A. Phillips (1979): “Maximum likelihood estimation of regression models with autoregressive-moving average disturbances,” *Biometrika*, 66(1), 49–58.
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* Koop, Gary (2003), *Bayesian Econometrics*, John Wiley & Sons.
* Koopman, S. J. and J. Durbin (2000): “Fast Filtering and Smoothing for Multivariate State Space Models,” *Journal of Time Series Analysis*, 21(3), 281–296.
* Koopman, S. J. and J. Durbin (2003): “Filtering and Smoothing of State Vector for Diffuse State Space Models,” *Journal of Time Series Analysis*, 24(1), 85–98.
* Kuntsevich, Alexei V. and Franz Kappel (1997): “SolvOpt - The solver for local nonlinear optimization problems (version 1.1, Matlab, C, FORTRAN)”, University of Graz, Graz, Austria.
* Laffargue, Jean-Pierre (1990): “Résolution d’un modèle macroéconomique avec anticipations rationnelles”, *Annales d’Économie et Statistique*, 17, 97–119.
* Liu, Jane and Mike West (2001): “Combined parameter and state estimation in simulation-based filtering”, in *Sequential Monte Carlo Methods in Practice*, Eds. Doucet, Freitas and Gordon, Springer Verlag, Chapter 10, 197-223.
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* Pfeifer, Johannes (2013): “A Guide to Specifying Observation Equations for the Estimation of DSGE Models”.
* Pfeifer, Johannes (2014): “An Introduction to Graphs in Dynare”.
* Qu, Zhongjun and Tkachenko, Denis (2012): “Identification and frequency domain quasi-maximum likelihood estimation of linearized dynamic stochastic general equilibrium models“, *Quantitative Economics*, 3, 95–132.
* Rabanal, Pau and Juan Rubio-Ramírez (2003): “Comparing New Keynesian Models of the Business Cycle: A Bayesian Approach,” Federal Reserve of Atlanta, *Working Paper Series*, 2003-30.
* Raftery, Adrian E. and Steven Lewis (1992): “How many iterations in the Gibbs sampler?,” in *Bayesian Statistics, Vol. 4*, ed. J.O. Berger, J.M. Bernardo, A.P. * Dawid, and A.F.M. Smith, Clarendon Press: Oxford, pp. 763-773.
* Ratto, Marco (2008): “Analysing DSGE models with global sensitivity analysis”, *Computational Economics*, 31, 115–139.
* Ratto, Marco and Iskrev, Nikolay (2011): “Identification Analysis of DSGE Models with DYNARE.“, *MONFISPOL* 225149.
* Ruge-Murcia, Francisco J. (2012): “Estimating nonlinear DSGE models by the simulated method of moments: With an application to business cycles“, *Journal of Economic Dynamics and Control*, 36, 914-938.
* Schmitt-Grohé, Stephanie and Martin Uríbe (2004): “Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function,” *Journal of Economic Dynamics and Control*, 28(4), 755–775.
* Schnabel, Robert B. and Elizabeth Eskow (1990): “A new modified Cholesky algorithm,” *SIAM Journal of Scientific and Statistical Computing*, 11, 1136–1158.
* Schorfheide, Frank (2000): “Loss Function-based evaluation of DSGE models,” *Journal of Applied Econometrics*, 15(6), 645–670.
* Sims, Christopher A., Daniel F. Waggoner and Tao Zha (2008): “Methods for inference in large multiple-equation Markov-switching models,” *Journal of Econometrics*, 146, 255–274.
* Skoeld, Martin and Gareth O. Roberts (2003): “Density Estimation for the Metropolis-Hastings Algorithm,” *Scandinavian Journal of Statistics*, 30, 699–718.
* Smets, Frank and Rafael Wouters (2003): “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area,” *Journal of the European Economic Association*, 1(5), 1123–1175.
* Stock, James H. and Mark W. Watson (1999). “Forecasting Inflation,”, *Journal of Monetary Economics*, 44(2), 293–335.
* Uhlig, Harald (2001): “A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily,” in *Computational Methods for the Study of Dynamic Economies*, Eds. Ramon Marimon and Andrew Scott, Oxford University Press, 30–61.
* U.S. Census Bureau (2020): “X-13 ARIMA-SEATS Reference Manual, Version 1.1”, Center for Statistical Research and Methodology, U.S. Census Bureau, https://www.census.gov/data/software/x13as.html
* Villemot, Sébastien (2011): “Solving rational expectations models at first order: what Dynare does,” *Dynare Working Papers*, 2, CEPREMAP.
doc/manual/source/conf.py 0 → 100644
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# -*- coding: utf-8 -*-
# Copyright © 2018-2025 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 <https://www.gnu.org/licenses/>.
import os
import sys
sys.path.insert(0, os.path.abspath('../utils'))
extensions = ['sphinx.ext.autodoc',
'sphinx.ext.mathjax']
source_suffix = '.rst'
templates_path = ['_templates']
html_static_path = ['_static']
master_doc = 'index'
project = u'Dynare'
copyright = u'1996–2025 Dynare Team'
author = u'Dynare Team'
add_function_parentheses = False
language = 'en'
exclude_patterns = []
highlight_language = 'dynare'
todo_include_todos = False
html_theme = 'alabaster'
html_sidebars = {
"**": [
"about.html",
"searchbox.html",
"navigation.html",
]
}
html_theme_options = {
'logo': 'dlogo.svg',
'logo_name': False,
'fixed_sidebar': True,
'page_width': '100%',
}
htmlhelp_basename = 'Dynaremanual'
latex_elements = {
'sphinxsetup': 'VerbatimBorderColor={rgb}{1,1,1},VerbatimColor={RGB}{240,240,240}, \
warningBorderColor={RGB}{255,50,50},OuterLinkColor={RGB}{34,139,34}, \
InnerLinkColor={RGB}{51,51,255},TitleColor={RGB}{51,51,255}',
'papersize': 'a4paper',
# Add support for the perpendicular symbol input as UTF-8
'preamble': r'''
\DeclareUnicodeCharacter{27C2}{\ensuremath{\perp}}
'''
}
latex_documents = [
(master_doc, 'dynare-manual.tex', u'Dynare Reference Manual',
u'Dynare Team', 'manual'),
]
man_pages = [
(master_doc, 'dynare', u'Dynare Reference Manual',
[author], 1)
]
def setup(app):
from dynare_dom import DynareDomain
from dynare_lex import DynareLexer
app.add_lexer("dynare", DynareLexer)
app.add_domain(DynareDomain)
doc/manual/source/dynare-misc-commands.rst 0 → 100644
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.. default-domain:: dynare
.. |br| raw:: html
<br>
####################
Dynare misc commands
####################
.. matcomm:: send_endogenous_variables_to_workspace ;
Puts the simulation results for the endogenous variables stored in ``oo_.endo_simul``
into vectors with the same name as the respective variables into the base workspace.
.. matcomm:: send_exogenous_variables_to_workspace ;
Puts the simulation results for the exogenous variables stored in ``oo_.exo_simul``
into vectors with the same name as the respective variables into the base workspace.
.. matcomm:: send_irfs_to_workspace ;
Puts the IRFs stored in ``oo_.irfs`` into vectors with the same name into the base workspace.
.. command:: prior_function(OPTIONS);
Executes a user-defined function on parameter draws from the prior
distribution. Dynare returns the results of the computations for
all draws in an `ndraws` by :math:`n` cell array named
``oo_.prior_function_results``.
*Options*
.. option:: function = FUNCTION_NAME
The function must have the following header ``output_cell =
FILENAME(xparam1,M_,options_,oo_,estim_params_,bayestopt_,dataset_,dataset_info)``,
providing read-only access to all Dynare structures. The only
output argument allowed is a :math:`1 \times n` cell array,
which allows for storing any type of output/computations. This
option is required.
.. option:: sampling_draws = INTEGER
Number of draws used for sampling. Default: 500.
|br|
.. command:: posterior_function(OPTIONS);
Same as the :comm:`prior_function` command but for the
posterior distribution. Results returned in
``oo_.posterior_function_results``.
*Options*
.. option:: function = FUNCTION_NAME
See :opt:`prior_function_function <function = FUNCTION_NAME>`.
.. option:: sampling_draws = INTEGER
See :opt:`prior_function_sampling_draws <sampling_draws = INTEGER>`.
|br|
.. command:: generate_trace_plots(CHAIN_NUMBER);
Generates trace plots of the MCMC draws for all estimated
parameters and the posterior density for the specified Markov Chain(s)
``CHAIN_NUMBER``. If ``CHAIN_NUMBER`` is a vector of integers, the trace plots
will plot contains separate lines for each chain.
|br|
.. matcomm:: internals FLAG ROUTINENAME[.m]|MODFILENAME
Depending on the value of ``FLAG``, the ``internals`` command
can be used to run unitary tests specific to a MATLAB/Octave
routine (if available), to display documentation about a
MATLAB/Octave routine, or to extract some informations about the
state of Dynare.
*Flags*
``--test``
Performs the unitary test associated to ROUTINENAME (if this
routine exists and if the matlab/octave ``.m`` file has
unitary test sections).
*Example*
::
>> internals --test ROUTINENAME
if ``routine.m`` is not in the current directory, the full
path has to be given::
>> internals --test ../matlab/fr/ROUTINENAME
``--display-mh-history``
Displays information about the previously saved MCMC draws
generated by a ``.mod`` file named MODFILENAME. This file must
be in the current directory.
*Example*
::
>> internals --display-mh-history MODFILENAME
``--load-mh-history``
Loads into the MATLAB/Octave’s workspace informations
about the previously saved MCMC draws generated by a ``.mod``
file named MODFILENAME.
*Example*
::
>> internals --load-mh-history MODFILENAME
This will create a structure called ``mcmc_informations``
(in the workspace) with the following fields:
``Nblck``
The number of MCMC chains.
``InitialParameters``
A ``Nblck*n``, where ``n`` is the number of estimated
parameters, array of doubles. Initial state of
the MCMC.
``LastParameters``
A ``Nblck*n``, where ``n`` is the number of estimated
parameters, array of doubles. Current state of
the MCMC.
``InitialLogPost``
A ``Nblck*1`` array of doubles. Initial value of the
posterior kernel.
``LastLogPost``
A ``Nblck*1`` array of doubles. Current value of the
posterior kernel.
``InitialSeeds``
A ``1*Nblck`` structure array. Initial state of the random
number generator.
``LastSeeds``
A ``1*Nblck`` structure array. Current state of the random
number generator.
``AcceptanceRatio``
A ``1*Nblck`` array of doubles. Current acceptance ratios.
|br|
.. matcomm:: prior [OPTIONS[ ...]];
Prints information about the prior distribution given the provided
options. If no options are provided, the command returns the list of
available options.
*Options*
.. option:: table
Prints a table describing the marginal prior distributions
(mean, mode, std., lower and upper bounds, HPD interval).
.. option:: moments
Computes and displays first and second order moments of the
endogenous variables at the prior mode (considering the
linearized version of the model).
.. option:: moments(distribution)
Computes and displays the prior mean and prior standard
deviation of the first and second moments of the endogenous
variables (considering the linearized version of the model) by
randomly sampling from the prior. The results will also be
stored in the ``prior`` subfolder in a
``_endogenous_variables_prior_draws.mat`` file.
.. option:: optimize
Optimizes the prior density (starting from a random initial
guess). The parameters such that the steady state does not
exist or does not satisfy the Blanchard and Kahn conditions
are penalized, as they would be when maximizing the posterior
density. If a significant proportion of the prior mass is
defined over such regions, the optimization algorithm may fail
to converge to the true solution (the prior mode).
.. option:: simulate
Computes the effective prior mass using a Monte-Carlo. Ideally
the effective prior mass should be equal to 1, otherwise
problems may arise when maximising the posterior density and
model comparison based on marginal densities may be
unfair. When comparing models, say :math:`A` and :math:`B`,
the marginal densities, :math:`m_A` and :math:`m_B`, should be
corrected for the estimated effective prior mass
:math:`p_A\neq p_B \leq 1` so that the prior mass of the
compared models are identical.
.. option:: plot
Plots the marginal prior density.
|br|
.. matcomm:: search VARIABLENAME[ OPTION]
Searches all occurrences of a variable in a model, and prints the
equations where the variable appear in the command line window. If OPTION is
set to `withparamvalues`, the values of the parameters (if available) are
displayed instead of the name of the parameters. Requires the `json` command
line option to be set.
*Example*
Assuming that we already ran a `.mod` file and that the workspace has not
been cleaned after, we can search for all the equations containing variable `X`
::
>> search X
Y = alpha*X/(1-X)+e;
diff(X) = beta*(X(-1)-mX) + gamma1*Z + gamma2*R + u;
To replace the parameters with estimated or calibrated parameters:
::
>> search X withparamvalues
Y = 1.254634*X/(1-X)+e;
diff(X) = -0.031459*(X(-1)-mX) + 0.1*Z - 0.2*R + u;
|br|
.. matcomm:: dplot [OPTION VALUE[ ...]]
Plot expressions extracting data from different dseries objects.
*Options*
.. option:: --expression EXPRESSION
``EXPRESSION`` is a mathematical expression involving variables
available in the dseries objects, dseries methods, numbers or
parameters. All the referenced objects are supposed to be
available in the calling workspace.
.. option:: --dseries NAME
``NAME`` is the name of a dseries object from which the
variables involved in ``EXPRESSION`` will be extracted.
.. option:: --range DATE1:DATE2
This option is not mandatory and allows to plot the expressions
only over a sub-range. ``DATE1`` and ``DATE2`` must be dates as
defined in :ref:`dates in a mod file`.
.. option:: --style MATLAB_SCRIPT_NAME
Name of a Matlab script (without extension) containing Matlab
commands to customize the produced figure.
.. option:: --title MATLAB_STRING
Adds a title to the figure.
.. option:: --with-legend
Prints a legend below the produced plot.
*Remarks*
- More than one --expression argument is allowed, and they must come first.
- For each dseries object we plot all the expressions. We use two
nested loops, the outer loop is over the dseries objects and the
inner loop over the expressions. This determines the ordering of
the plotted lines.
- All dseries objects must be defined in the calling workspace, if a
dseries object is missing the routine throws a warning (we only
build the plots for the available dseries objects), if all dseries
objects are missing the routine throws an error.
- If the range is not provided, the expressions cannot involve leads or lags.
*Example*
::
>> toto = dseries(randn(100,3), dates('2000Q1'), {'x','y','z'});
>> noddy = dseries(randn(100,3), dates('2000Q1'), {'x','y','z'});
>> b = 3;
>> dplot --expression 2/b*cumsum(x/y(-1)-1) --dseries toto --dseries noddy --range 2001Q1:2024Q1 --title 'This is my plot'
will produce plots for ``2/b*cumsum(x/y(-1)-1)``, where ``x`` and
``y`` are variables in dseries objects ``toto`` and ``noddy``, in
the same figure.
.. command:: set_dynare_threads(NAME_OF_MEX_FILE,INTEGER);
A NAME_OF_MEX_FILE and INTEGER pair that can be used to set the number of
parallel threads employed during the execution of .mex files. To get the number
of logical cores `n` available, you can run ``n=numprocs``.
Available NAME_OF_MEX_FILE options are:
``'sparse_hessian_times_B_kronecker_C'``
.mex file used during computation of second-order solutions and in
`identification`. Default number of threads: number of logical cores.
``'local_state_space_iteration_2'``
.mex file used during nonlinear filtering at `order=2` without
`k_order_solver` or with `pruning`. Default number of threads:
number of logical cores.
``'local_state_space_iteration_3'``
.mex file used during nonlinear filtering at `order=3` without
`k_order_solver` or with `pruning`. Default number of threads:
number of logical cores.
``'local_state_space_iteration_k'``
.mex file used during nonlinear filtering at `order>3` (without `pruning`)
or at `order=3` with `k_order_solver` and without `pruning`. Default number of threads: 1.
``'perfect_foresight_problem'``
.mex file used during perfect foresight simulations. Default number of threads:
number of logical cores.
``'k_order_perturbation'``
.mex file used for perturbation solutions with `k_order_solver`. Default number of threads:
half the number of logical cores, but at least 1.
doc/manual/source/examples.rst 0 → 100644
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.. default-domain:: dynare
########
Examples
########
Dynare comes with a database of example ``.mod`` files, which are
designed to show a broad range of Dynare features, and are taken from
academic papers for most of them. You should have these files in the
``examples`` subdirectory of your distribution.
Here is a short list of the examples included. For a more complete
description, please refer to the comments inside the files themselves.
``ramst.mod``
An elementary real business cycle (RBC) model, simulated in a
deterministic setup.
``example1.mod``
``example2.mod``
Two examples of a small RBC model in a stochastic setup, presented
in *Collard (2001)* (see the file ``guide.pdf`` which comes with
Dynare).
``example3.mod``
A small RBC model in a stochastic setup, presented in *Collard
(2001)*. The steady state is solved analytically using the
``steady_state_model`` block (see :bck:`steady_state_model`).
``fs2000.mod``
A cash in advance model, estimated by *Schorfheide (2000)*. The
file shows how to use Dynare for estimation.
``fs2000_nonstationary.mod``
The same model than ``fs2000.mod``, but written in non-stationary
form. Detrending of the equations is done by Dynare.
``bkk.mod``
Multi-country RBC model with time to build, presented in *Backus,
Kehoe and Kydland (1992)*. The file shows how to use Dynare’s
macro processor.
``agtrend.mod``
Small open economy RBC model with shocks to the growth trend,
presented in *Aguiar and Gopinath (2004)*.
``Gali_2015.mod``
Basic New Keynesian model of *Galí (2015)*, Chapter 3 showing how to
i) use "system prior"-type prior restrictions as in *Andrle and Plašil (2018)*
and ii) run prior/posterior-functions.
``NK_baseline.mod``
Baseline New Keynesian Model estimated in *Fernández-Villaverde
(2010)*. It demonstrates how to use an explicit steady state file
to update parameters and call a numerical solver.
``Occbin_example.mod``
RBC model with two occasionally binding constraints. Demonstrates
how to set up Occbin.
``Ramsey_Example.mod``
File demonstrating how to conduct optimal policy experiments in a
simple New Keynesian model either under commitment (Ramsey) or using
optimal simple rules (OSR)
``Ramsey_steady_file.mod``
File demonstrating how to conduct optimal policy experiments in a
simple New Keynesian model under commitment (Ramsey) with a user-defined
conditional steady state file
``rbc_irf_matching.mod``
Baseline RBC model with government spending shocks estimated via impulse response function (IRF) matching.
Both Frequentist (Maximum Likelihood) and Bayesian (Slice Sampling) approaches are presented.
Additionally, it is shown how to estimate an AR(2)-process
by working with the roots of the autoregressive process instead of the coefficients