GSA: cleanup of doc and inclusion in build system
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@ -174,6 +174,7 @@ AC_CONFIG_FILES([Makefile
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doc/userguide/Makefile
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doc/parallel/Makefile
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doc/internals/Makefile
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doc/gsa/Makefile
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tests/Makefile
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matlab/dynare_version.m
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windows/dynare-version.nsi
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@ -1,4 +1,4 @@
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SUBDIRS = preprocessor macroprocessor userguide parallel internals
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SUBDIRS = preprocessor macroprocessor userguide parallel internals gsa
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PDF_TARGETS =
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@ -0,0 +1,18 @@
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if HAVE_PDFLATEX
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if HAVE_BIBTEX
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pdf-local: gsa.pdf
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endif
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endif
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SRC = gsa.tex marco.bib
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EXTRA_DIST = $(SRC)
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gsa.pdf: $(SRC)
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$(PDFLATEX) gsa
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$(BIBTEX) gsa
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$(PDFLATEX) gsa
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$(PDFLATEX) gsa
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clean-local:
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rm -f *.pdf *.log *.aux *.toc *.lof *.blg *.bbl *.out *~
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@ -1,24 +0,0 @@
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\relax
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\citation{Ratto_CompEcon_2008}
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\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{2}}
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\newlabel{s:intro}{{1}{2}}
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\@writefile{toc}{\contentsline {section}{\numberline {2}Use of the Toolbox}{2}}
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\@writefile{toc}{\contentsline {section}{\numberline {3}List of options}{3}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Sampling options}{3}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Stability mapping}{3}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Reduced form mapping}{4}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Mapping the fit}{5}}
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\citation{Iskrev2009}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.5}Screening analysis}{6}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.6}Identification analysis (under development)}{6}}
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\citation{Iskrev2009}
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\@writefile{toc}{\contentsline {section}{\numberline {4}Directory structure}{8}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Binary data files}{8}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Stability analysis}{8}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}RMSE analysis}{9}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Reduced form mapping}{10}}
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\bibstyle{plainnat}
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\bibdata{marco}
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\bibcite{Iskrev2009}{{1}{2009}{{Iskrev}}{{}}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.5}Screening analysis}{11}}
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\bibcite{Ratto_CompEcon_2008}{{2}{2008}{{Ratto}}{{}}}
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@ -1,18 +0,0 @@
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\begin{thebibliography}{2}
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\providecommand{\natexlab}[1]{#1}
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\providecommand{\url}[1]{\texttt{#1}}
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\expandafter\ifx\csname urlstyle\endcsname\relax
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\providecommand{\doi}[1]{doi: #1}\else
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\providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi
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\bibitem[Iskrev(2009)]{Iskrev2009}
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Nikolay Iskrev.
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\newblock Local identification in \textsc{DSGE} models.
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\newblock mimeo, 2009.
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\bibitem[Ratto(2008)]{Ratto_CompEcon_2008}
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M.~Ratto.
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\newblock Analysing dsge models with global sensitivity analysis.
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\newblock \emph{Computational Economics}, 31:\penalty0 115--139, 2008.
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\end{thebibliography}
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This is BibTeX, Version 0.99cThe top-level auxiliary file: dyn_tbx.aux
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The style file: plainnat.bst
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Database file #1: marco.bib
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I was expecting a `,' or a `}'---line 921 of file marco.bib
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:
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: pages = {},
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(Error may have been on previous line)
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I'm skipping whatever remains of this entry
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I was expecting a `,' or a `}'---line 1177 of file marco.bib
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:
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: organisation = {Los Alamos National Laboratories}
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(Error may have been on previous line)
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I'm skipping whatever remains of this entry
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I was expecting a `,' or a `}'---line 1364 of file marco.bib
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:
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: organisation = {}
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I'm skipping whatever remains of this entry
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You're missing a field name---line 2210 of file marco.bib
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:
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: %@ARTICLE{kn:NIH,
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(Error may have been on previous line)
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I'm skipping whatever remains of this entry
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You're missing a field name---line 2211 of file marco.bib
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:
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: % author = {NIH},
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(Error may have been on previous line)
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I'm skipping whatever remains of this entry
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Warning--empty institution in Iskrev2009
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[][][]\OT1/cmtt/m/n/12 dynare_sensitivity(option1=<opt1_val>,option2=<opt2_val>
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,...)
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\usepackage{psfrag}
|
||||
\usepackage{setspace}
|
||||
\usepackage{rotating}
|
||||
%\singlespacing (interlinea singola)
|
||||
%\onehalfspacing (interlinea 1,5)
|
||||
%\doublespacing (interlinea doppia)
|
||||
|
||||
|
||||
%\bibpunct{(}{)}{;}{a}{,}{,}
|
||||
\bibpunct[, ]{(}{)}{;}{a}{,}{,}
|
||||
%\pagestyle{headings}
|
||||
% ----------------------------------------------------------------
|
||||
\begin{document}
|
||||
|
||||
% ----------------------------------------------------------------
|
||||
\title{Sensitivity Analysis Toolbox for DYNARE}%
|
||||
|
||||
\author{Marco Ratto\\
|
||||
European Commission, Joint Research Centre \\
|
||||
TP361, IPSC, via E. Fermi 1\\21020 Ispra
|
||||
(VA) Italy\\
|
||||
\texttt{marco.ratto@jrc.it}
|
||||
\thanks{The author gratefully thanks Christophe Planas, Kenneth Judd, Michel Juillard,
|
||||
Alessandro Rossi, Frank Schorfheide and the participants to the
|
||||
Courses on Global Sensitivity Analysis for Macroeconomic
|
||||
Models (Ispra, 2006-2007-2008) for interesting discussions and
|
||||
helpful suggestions.}}
|
||||
|
||||
%%% To have the current date inserted, use \date{\today}:
|
||||
%%% To insert a footnote, add thanks in the date/title/author fields:
|
||||
\date{\today}
|
||||
%\date{\today \thanks{Authors gratefully acknowledge the
|
||||
%contribution by ... for ...}}
|
||||
\maketitle %\tableofcontents
|
||||
|
||||
%\doublespacing
|
||||
|
||||
%-----------------------------------------------------------------------
|
||||
\begin{abstract}
|
||||
\noindent The Sensitivity Analysis Toolbox for DYNARE is a set of
|
||||
MATLAB routines for the analysis of DSGE models with global
|
||||
sensitivity analysis. The routines are thought to be used within
|
||||
the DYNARE v4 environment.
|
||||
|
||||
|
||||
\begin{description}
|
||||
\item \textbf{Keywords}: Stability Mapping , Reduced form solution, DSGE models,
|
||||
Monte Carlo filtering, Global Sensitivity Analysis.
|
||||
\end{description}
|
||||
\end{abstract}
|
||||
\newpage
|
||||
% ----------------------------------------------------------------
|
||||
\section{Introduction} \label{s:intro}
|
||||
The Sensitivity Analysis Toolbox for DYNARE is a collection of
|
||||
MATLAB routines implemented to answer the following questions: (i)
|
||||
Which is the domain of structural coefficients assuring the
|
||||
stability and determinacy of a DSGE model? (ii) Which parameters
|
||||
mostly drive the fit of, e.g., GDP and which the fit of inflation?
|
||||
Is there any conflict between the optimal fit of one observed
|
||||
series versus another one? (iii) How to represent in a direct,
|
||||
albeit approximated, form the relationship between structural
|
||||
parameters and the reduced form of a rational expectations model?
|
||||
|
||||
The discussion of the methodologies and their application is
|
||||
described in \cite{Ratto_CompEcon_2008}.
|
||||
|
||||
|
||||
\section{Use of the Toolbox}
|
||||
The DYNARE parser now recognizes sensitivity analysis commands.
|
||||
The syntax is based on a single command:
|
||||
\vspace{0.5cm}
|
||||
|
||||
\verb"dynare_sensitivity(option1=<opt1_val>,option2=<opt2_val>,...)"
|
||||
|
||||
\vspace{0.5cm} \noindent with a list of options described in the
|
||||
next section.
|
||||
|
||||
With respect to the previous version of the toolbox, in order to
|
||||
work properly, the sensitivity analysis Toolbox \emph{no longer}
|
||||
needs that the DYNARE estimation environment is set-up.
|
||||
|
||||
Therefore, \verb"dynare_sensitivity" is the only command to run to
|
||||
make a sensitivity analysis on a DSGE model\footnote{Of course,
|
||||
when the user needs to perform the mapping of the fit with a
|
||||
posterior sample, a Bayesian estimation has to be performed
|
||||
beforehand}.
|
||||
|
||||
|
||||
\section{List of options}
|
||||
|
||||
\subsection{Sampling options}
|
||||
\begin{tabular}{r|l|l}
|
||||
% after \\ : \hline or \cline{col1-col2} \cline{col3-col4} ...
|
||||
option name & default & description \\ \hline
|
||||
\verb"Nsam"& 2048& Size of MC sample \\
|
||||
\verb"ilptau"& 1& 1 = use $LP_\tau$ quasi-Monte Carlo \\
|
||||
& & 0 = use LHS Monte Carlo \\
|
||||
\verb"pprior"& 1& 1 = sample from prior distributions\\
|
||||
& & 0 = sample from multivariate
|
||||
normal \\
|
||||
& & \hspace{0.5 cm} $N(\hat{\theta},\Sigma)$, $\hat{\theta}$ is posterior mode \\
|
||||
& & \hspace{0.5 cm} $\Sigma = H^{-1}$, $H$ is Hessian at the mode\\
|
||||
\verb"prior_range"& 1& 1 = sample \textit{uniformly} from prior ranges\\
|
||||
& & 0 = sample from prior distributions: \\
|
||||
& & this requires MATLAB Statistics Toolbox\\
|
||||
\verb"morris"& 0& 0 = no Morris sampling for screening \\
|
||||
& & 1 = Morris sampling for screening \\
|
||||
\verb"morris_nliv"& 6& number of levels in Morris design\\
|
||||
\verb"morris_ntra"& 20& number of trajectories in Morris design\\
|
||||
\verb"ppost"& 0& 0 = don't use Metropolis posterior sample\\
|
||||
& & 1 = use Metropolis posterior sample: this \\
|
||||
& & \hspace{0.5 cm} overrides any other sampling option! \\ \hline
|
||||
\end{tabular}
|
||||
\subsection{Stability mapping}
|
||||
\begin{tabular}{r|l|l}
|
||||
option name & default & description \\ \hline
|
||||
\verb"stab"& 1& 1 = perform stability mapping \\
|
||||
& & 0 = no stability mapping is performed\\
|
||||
\verb"load_stab"& 0& 0 = generate a new sample\\
|
||||
& & 1 = load a previously created sample \\
|
||||
\verb"alpha2_stab"& 0.4& critical value for correlations $\rho$ in filtered samples:\\
|
||||
& & plot couples of parameters with \\
|
||||
& & $|\rho|>$\verb"alpha2_stab"\\
|
||||
\verb"ksstat"& 0.1& critical value for Smirnov statistics $d$: \\
|
||||
& & plot parameters with $d>$\verb"ksstat"\\ \hline
|
||||
\end{tabular}
|
||||
|
||||
\newpage
|
||||
\subsection{Reduced form mapping}% and identification}
|
||||
The mapping of the reduced form solution forces the use of samples
|
||||
from prior ranges or prior distributions, i.e.:
|
||||
\\
|
||||
\verb"options_.opt_gsa.pprior=1;"\\
|
||||
\verb"options_.opt_gsa.ppost=0;"\\
|
||||
|
||||
It uses 250 samples to optimize smoothing parameters and 1000
|
||||
samples to compute the fit. The rest of the sample is used for
|
||||
out-of-sample validation. \vspace{0.5cm}
|
||||
|
||||
|
||||
\begin{tabular}{r|l|l}
|
||||
option name & default & description \\ \hline
|
||||
\verb"redform"& 0& 0 = don't prepare MC sample of \\
|
||||
& & reduced form matrices \\
|
||||
& & 1 = prepare MC sample of \\
|
||||
& & reduced form matrices \\
|
||||
\verb"load_redform"& 0& 0 = estimate the mapping of \\
|
||||
& & reduced form model\\
|
||||
& & 1 = load previously estimated mapping\\
|
||||
\verb"logtrans_redform"& 0& 0 = use raw entries\\
|
||||
& & 1 = use log-transformed entries \\
|
||||
\verb"threshold_redform"& []& [] = don't filter MC entries \\
|
||||
& & of reduced form coefficients\\
|
||||
& & [\verb"max" \verb"max"] = analyse filtered \\
|
||||
& & entries within the range [\verb"max" \verb"max"] \\
|
||||
\verb"ksstat_redform"& 0.1& critical value for Smirnov statistics $d$ \\
|
||||
& & when reduced form entries are filtered\\
|
||||
\verb"alpha2_redform"& 0.3& critical value for correlation $\rho$ \\
|
||||
& & when reduced form entries are filtered\\
|
||||
\verb"namendo"& () & list of endogenous variables \\
|
||||
& : & jolly character to indicate ALL endogenous \\
|
||||
\verb"namlagendo"& () & list of lagged endogenous variables:\\
|
||||
& & analyse entries [\verb"namendo"$\times$\verb"namlagendo"]\\
|
||||
& : & jolly character to indicate ALL endogenous \\
|
||||
\verb"namexo"& ()& list of exogenous variables:\\
|
||||
& & analyse entries
|
||||
[\verb"namendo"$\times$\verb"namexo"]\\
|
||||
& : & jolly character to indicate ALL exogenous \\\hline
|
||||
\end{tabular}
|
||||
\vspace{0.5cm} \\
|
||||
|
||||
One can also load a previously estimated mapping
|
||||
with a new MC sample, to look at the forecast for the new MC sample.\\
|
||||
|
||||
\subsection{Mapping the fit}
|
||||
The RMSE analysis can be performed with different types of
|
||||
sampling options:
|
||||
\begin{enumerate}
|
||||
\item when \verb"pprior=1" and \verb"ppost=0", the Toolbox analyses the RMSE's for
|
||||
the MC sample obtained by sampling parameters from their prior
|
||||
distributions (or prior ranges): this analysis provides some hints
|
||||
about what parameter drives the fit of which observed series,
|
||||
prior to the full estimation;
|
||||
\item when \verb"pprior=0" and \verb"ppost=0", the Toolbox analyses the RMSE's for
|
||||
a multivariate normal MC sample, with covariance matrix based on
|
||||
the inverse Hessian at the optimum: this analysis is useful when
|
||||
ML estimation is done (i.e. no Bayesian estimation);
|
||||
\item when \verb"ppost=1" the Toolbox analyses
|
||||
the RMSE's for the posterior sample obtained by DYNARE's
|
||||
Metropolis procedure.
|
||||
\end{enumerate}
|
||||
|
||||
The use of cases 2. and 3. requires an estimation step beforehand!
|
||||
To facilitate the sensitivity analysis after estimation, the
|
||||
\verb"dynare_sensitivity" command also allows to indicate some
|
||||
options of \verb"dynare_estimation". These are:
|
||||
\begin{itemize}
|
||||
\item \verb"datafile"
|
||||
\item \verb"mode_file"
|
||||
\item \verb"first_obs"
|
||||
\item \verb"nobs"
|
||||
\item \verb"prefilter"
|
||||
\item \verb"presample"
|
||||
\item \verb"loglinear"
|
||||
\end{itemize}
|
||||
|
||||
|
||||
\vspace{1cm}
|
||||
|
||||
\begin{tabular}{r|l|l}
|
||||
option name & default & description \\ \hline
|
||||
\verb"rmse"& 0& 0 = no RMSE analysis\\
|
||||
& & 1 = do RMSE analysis \\
|
||||
\verb"load_rmse"& 0& 0 = make a new RMSE analysis\\
|
||||
& & 1 = load previous RMSE analysis \\
|
||||
\verb"lik_only"& 0& 0 = compute RMSE's for all observed series\\
|
||||
& & 1 = compute only likelihood and posterior \\
|
||||
\verb"var_rmse"& varobs& list of observed series to be considered\\
|
||||
\verb"pfilt_rmse"& 0.1& filtering threshold for RMSE's: default it to\\
|
||||
& & filter the best 10\% for each observed series\\
|
||||
\verb"istart_rmse"& 1& start computing RMSE's from \verb"istart_rmse":\\
|
||||
& & use 2 to avoid big initial error \\
|
||||
\verb"alpha_rmse"& 0.002& critical value for Smirnov statistics $d$:\\
|
||||
& & plot parameters with $d>$\verb"alpha_rmse"\\
|
||||
\verb"alpha2_rmse"& 1& critical value for correlation $\rho$\\
|
||||
& & plot couples of parameters with
|
||||
$|\rho|>$\verb"alpha2_rmse"\\
|
||||
\verb"glue"& 0& prepare for GLUE graphical interface\\\hline
|
||||
\end{tabular}
|
||||
|
||||
\subsection{Screening analysis}
|
||||
The screening analysis does not require any additional options
|
||||
with respect to those listed in the `Sampling options':
|
||||
\verb"morris", \verb"morris_nliv", \verb"morris_ntra". The Toolbox
|
||||
performs all the analyses required and displays results.
|
||||
|
||||
|
||||
\subsection{Identification analysis (under development)}
|
||||
Setting the option \verb"identification=1", an identification
|
||||
analysis based on theoretical moments is performed. Sensitivity plots are provided that
|
||||
allow to infer which parameters are most likely to be less
|
||||
identifiable.
|
||||
|
||||
Prerequisite for properly running all the identification routines, is the keyword
|
||||
\verb"identification;" in the DYNARE model file. This keyword triggers the computation of analytic derivatives of the model with respect to estimated parameters and shocks. This is required for options \verb"morris=2" below, which implement \cite{Iskrev2009} identification analysis.
|
||||
\vspace{1cm}
|
||||
|
||||
|
||||
\begin{tabular}{r|l|l}
|
||||
option name & default & description \\ \hline
|
||||
\verb"identification"& 0 & 0 = no identification analysis \\
|
||||
& & 1 = performs identification analysis:\\
|
||||
& & this forces \verb"redform"=0 and default\verb"morris"=1\\
|
||||
\verb"morris"& 1 & 1 = Screening analysis (Type II error)\\
|
||||
& & 0 = ANOVA Mapping (Type I error)\\
|
||||
& & 2 = Analytic derivatives (similar to Type II error)\\
|
||||
\verb"morris_nliv"& 6 & number of levels in Morris design\\
|
||||
\verb"morris_ntra"& 20& number of trajectories in Morris design\\
|
||||
\verb"trans_ident"& 0 & data transformation for ANOVA Mapping\\
|
||||
& & 0 = no transformation\\
|
||||
& & 1 = log-transformation\\
|
||||
& & 2 = rank-transformation\\\hline
|
||||
\end{tabular}
|
||||
|
||||
\vspace{1cm}
|
||||
\noindent For example, the following commands in the DYNARE model file
|
||||
|
||||
\vspace{1cm}
|
||||
\noindent\verb"identification;"\\
|
||||
\verb"dynare_sensitivity(identification=1, morris=2);"
|
||||
|
||||
\vspace{1cm}
|
||||
\noindent trigger the identification analysis using analytic derivatives \citep{Iskrev2009}, jointly with the mapping of the acceptable region.
|
||||
|
||||
The identification analysis with derivatives can also be triggered by the commands
|
||||
|
||||
\vspace{1cm}
|
||||
\noindent\verb"identification;"\\
|
||||
\verb"dynare_identification;"
|
||||
|
||||
\vspace{1cm}
|
||||
This does not do the mapping of acceptable regions for the model and uses the standard random sampler of DYNARE. It completely offsets any use of the sensitivity analysis toolbox.
|
||||
\newpage
|
||||
\section{Directory structure}
|
||||
Sensitivity analysis results are saved on the hard-disk of the
|
||||
computer. The Toolbox uses a dedicated folder called \verb"GSA",
|
||||
located in \\
|
||||
\\
|
||||
\verb"<DYNARE_file>\GSA", \\
|
||||
\\
|
||||
where \verb"<DYNARE_file>.mod" is the name of the DYNARE model
|
||||
file.
|
||||
|
||||
\subsection{Binary data files}
|
||||
A set of binary data files is saved in the \verb"GSA" folder:
|
||||
\begin{description}
|
||||
\item[]\verb"<DYNARE_file>_prior.mat": this file stores
|
||||
information about the analyses performed sampling from the prior
|
||||
ranges, i.e. \verb"pprior=1" and \verb"ppost=0";
|
||||
\item[]\verb"<DYNARE_file>_mc.mat": this file stores
|
||||
information about the analyses performed sampling from
|
||||
multivariate normal, i.e. \verb"pprior=0" and \verb"ppost=0";
|
||||
\item[]\verb"<DYNARE_file>_post.mat": this file stores information
|
||||
about analyses performed using the Metropolis posterior sample,
|
||||
i.e. \verb"ppost=1".
|
||||
\end{description}
|
||||
|
||||
\begin{description}
|
||||
\item[]\verb"<DYNARE_file>_prior_*.mat": these files store
|
||||
the filtered and smoothed variables for the prior MC sample,
|
||||
generated when doing RMSE analysis (\verb"pprior=1" and
|
||||
\verb"ppost=0");
|
||||
\item[]\verb"<DYNARE_file>_mc_*.mat": these files store
|
||||
the filtered and smoothed variables for the multivariate normal MC
|
||||
sample, generated when doing RMSE analysis (\verb"pprior=0" and
|
||||
\verb"ppost=0").
|
||||
\end{description}
|
||||
|
||||
\subsection{Stability analysis}
|
||||
Figure files \verb"<DYNARE_file>_prior_*.fig" store results for
|
||||
the stability mapping from prior MC samples:
|
||||
\begin{description}
|
||||
\item[]\verb"<DYNARE_file>_prior_stab_SA_*.fig": plots of the Smirnov
|
||||
test analyses confronting the cdf of the sample fulfilling
|
||||
Blanchard-Kahn conditions with the cdf of the rest of the sample;
|
||||
\item[]\verb"<DYNARE_file>_prior_stab_indet_SA_*.fig": plots of the Smirnov
|
||||
test analyses confronting the cdf of the sample producing
|
||||
indeterminacy with the cdf of the original prior sample;
|
||||
\item[]\verb"<DYNARE_file>_prior_stab_unst_SA_*.fig": plots of the Smirnov
|
||||
test analyses confronting the cdf of the sample producing unstable
|
||||
(explosive roots) behaviour with the cdf of the original prior
|
||||
sample;
|
||||
\item[]\verb"<DYNARE_file>_prior_stable_corr_*.fig": plots of
|
||||
bivariate projections of the sample fulfilling Blanchard-Kahn
|
||||
conditions;
|
||||
\item[]\verb"<DYNARE_file>_prior_indeterm_corr_*.fig": plots of
|
||||
bivariate projections of the sample producing indeterminacy;
|
||||
\item[]\verb"<DYNARE_file>_prior_unstable_corr_*.fig": plots of
|
||||
bivariate projections of the sample producing instability;
|
||||
\item[]\verb"<DYNARE_file>_prior_unacceptable_corr_*.fig": plots of
|
||||
bivariate projections of the sample producing unacceptable
|
||||
solutions, i.e. either instability or indeterminacy or the
|
||||
solution could not be found (e.g. the steady state solution could
|
||||
not be found by the solver).
|
||||
\end{description}
|
||||
Similar conventions apply for \verb"<DYNARE_file>_mc_*.fig" files,
|
||||
obtained when samples from multivariate normal are used.
|
||||
|
||||
\subsection{RMSE analysis}
|
||||
Figure files \verb"<DYNARE_file>_rmse_*.fig" store results for the
|
||||
RMSE analysis.
|
||||
\begin{description}
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior*.fig": save results for
|
||||
the analysis using prior MC samples;
|
||||
\item[]\verb"<DYNARE_file>_rmse_mc*.fig": save results for
|
||||
the analysis using multivariate normal MC samples;
|
||||
\item[]\verb"<DYNARE_file>_rmse_post*.fig": save results for
|
||||
the analysis using Metropolis posterior samples.
|
||||
\end{description}
|
||||
|
||||
The following types of figures are saved (we show prior sample to
|
||||
fix ideas, but the same conventions are used for multivariate
|
||||
normal and posterior):
|
||||
\begin{description}
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior_*.fig": for each parameter, plots the cdf's
|
||||
corresponding to the best 10\% RMES's of each observed series;
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior_dens_*.fig": for each parameter, plots the pdf's
|
||||
corresponding to the best 10\% RMES's of each observed series;
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior_<name of observedseries>_corr_*.fig": for each observed series plots the
|
||||
bi-dimensional projections of samples with the best 10\% RMSE's,
|
||||
when the correlation is significant;
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior_lnlik*.fig": for each observed
|
||||
series, plots \emph{in red} the cdf of the log-likelihood
|
||||
corresponding to the best 10\% RMSE's, \emph{in green} the cdf of
|
||||
the rest of the sample and \emph{in blue }the cdf of the full
|
||||
sample; this allows to see the presence of some idiosyncratic
|
||||
behaviour;
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior_lnpost*.fig": for each observed
|
||||
series, plots \emph{in red} the cdf of the log-posterior
|
||||
corresponding to the best 10\% RMSE's, \emph{in green} the cdf of
|
||||
the rest of the sample and \emph{in blue }the cdf of the full
|
||||
sample; this allows to see idiosyncratic behaviour;
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior_lnprior*.fig": for each observed
|
||||
series, plots \emph{in red} the cdf of the log-prior corresponding
|
||||
to the best 10\% RMSE's, \emph{in green} the cdf of the rest of
|
||||
the sample and \emph{in blue }the cdf of the full sample; this
|
||||
allows to see idiosyncratic behaviour;
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior_lik_SA_*.fig": when
|
||||
\verb"lik_only=1", this shows the Smirnov tests for the filtering
|
||||
of the best 10\% log-likelihood values;
|
||||
\item[]\verb"<DYNARE_file>_rmse_prior_post_SA_*.fig": when
|
||||
\verb"lik_only=1", this shows the Smirnov test for the filtering
|
||||
of the best 10\% log-posterior values.
|
||||
\end{description}
|
||||
|
||||
\subsection{Reduced form mapping}
|
||||
In the case of the mapping of the reduced form solution, synthetic
|
||||
figures are saved in the \verb"\GSA" folder:
|
||||
|
||||
\begin{description}
|
||||
\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_lags_*.fig":
|
||||
shows bar charts of the sensitivity indices for the \emph{ten most
|
||||
important} parameters driving the reduced form coefficients of the
|
||||
selected endogenous variables (\verb"namendo") versus lagged
|
||||
endogenous variables (\verb"namlagendo"); suffix \verb"log"
|
||||
indicates the results for log-transformed entries;
|
||||
\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_shocks_*.fig":
|
||||
shows bar charts of the sensitivity indices for the \emph{ten most
|
||||
important} parameters driving the reduced form coefficients of the
|
||||
selected endogenous variables (\verb"namendo") versus exogenous
|
||||
variables (\verb"namexo"); suffix \verb"log" indicates the results
|
||||
for log-transformed entries;
|
||||
\item[]\verb"<DYNARE_file>_redform_GSA(_log).fig": shows bar chart of
|
||||
all sensitivity indices for each parameter: this allows to notice
|
||||
parameters that have a minor effect for \emph{any} of the reduced
|
||||
form coefficients,
|
||||
\end{description}
|
||||
|
||||
Detailed results of the analyses are shown in the subfolder
|
||||
\verb"\GSA\redform_stab", where the detailed results of the
|
||||
estimation of the single functional relationships between
|
||||
parameters $\theta$ and reduced form coefficient are stored in
|
||||
separate directories named as:
|
||||
\begin{description}
|
||||
\item[]\verb"<namendo>_vs_<namlagendo>": for the entries of the
|
||||
transition matrix;
|
||||
\item[]\verb"<namendo>_vs_<namexo>": for entries of the matrix of
|
||||
the shocks.
|
||||
\end{description}
|
||||
Moreover, analyses for log-transformed entries are denoted with
|
||||
the following suffixes ($y$ denotes the generic reduced form
|
||||
coefficient):
|
||||
\begin{description}
|
||||
\item[]\verb"log": $y^*=\log(y)$;
|
||||
\item[]\verb"minuslog": $y^*=\log(-y)$;
|
||||
\item[]\verb"logsquared": $y^*=\log(y^2)$ for symmetric fat tails;
|
||||
\item[]\verb"logskew": $y^*=\log(|y+\lambda|)$ for asymmetric fat tails.
|
||||
\end{description}
|
||||
The optimal type of transformation is automatically selected
|
||||
without the need of any user's intervention.
|
||||
|
||||
\subsection{Screening analysis}
|
||||
The results of the screening analysis with Morris sampling design
|
||||
are stored in the subfolder \verb"\GSA\SCREEN". The data file
|
||||
\verb"<DYNARE_file>_prior" stores all the information of the
|
||||
analysis (Morris sample, reduced form coefficients, etc.).
|
||||
|
||||
Screening analysis merely concerns reduced form coefficients.
|
||||
Similar synthetic bar charts as for the reduced form analysis with
|
||||
MC samples are saved:
|
||||
\begin{description}
|
||||
\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_lags_*.fig":
|
||||
shows bar charts of the elementary effect tests for the \emph{ten
|
||||
most important} parameters driving the reduced form coefficients
|
||||
of the selected endogenous variables (\verb"namendo") versus
|
||||
lagged endogenous variables (\verb"namlagendo");
|
||||
\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_shocks_*.fig":
|
||||
shows bar charts of the elementary effect tests for the \emph{ten
|
||||
most important} parameters driving the reduced form coefficients
|
||||
of the selected endogenous variables (\verb"namendo") versus
|
||||
exogenous variables (\verb"namexo");
|
||||
\item[]\verb"<DYNARE_file>_redform_screen.fig": shows bar chart of
|
||||
all elementary effect tests for each parameter: this allows to
|
||||
identify parameters that have a minor effect for \emph{any} of the
|
||||
reduced form coefficients.
|
||||
\end{description}
|
||||
|
||||
|
||||
% ----------------------------------------------------------------
|
||||
\bibliographystyle{plainnat}
|
||||
%\bibliographystyle{amsplain}
|
||||
%\bibliographystyle{alpha}
|
||||
\bibliography{marco}
|
||||
|
||||
\newpage
|
||||
|
||||
|
||||
\end{document}
|
||||
% ----------------------------------------------------------------
|
2260
doc/gsa/marco.bib
2260
doc/gsa/marco.bib
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|
@ -120,7 +120,7 @@ SectionEnd
|
|||
|
||||
Section "Documentation and examples (Dynare and Dynare++)"
|
||||
SetOutPath $INSTDIR\doc
|
||||
File ..\doc\manual.pdf ..\doc\guide.pdf ..\doc\userguide\UserGuide.pdf ..\doc\bvar-a-la-sims.pdf ..\doc\dr.pdf ..\doc\macroprocessor\macroprocessor.pdf ..\doc\preprocessor\preprocessor.pdf ..\doc\parallel\parallel.pdf
|
||||
File ..\doc\manual.pdf ..\doc\guide.pdf ..\doc\userguide\UserGuide.pdf ..\doc\bvar-a-la-sims.pdf ..\doc\dr.pdf ..\doc\macroprocessor\macroprocessor.pdf ..\doc\preprocessor\preprocessor.pdf ..\doc\parallel\parallel.pdf ..\doc\gsa\gsa.pdf
|
||||
|
||||
SetOutPath $INSTDIR\doc\manual-html
|
||||
File ..\doc\manual-html\*.html
|
||||
|
|
Loading…
Reference in New Issue