gsa: update documentation
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doc/gsa/gsa.tex
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doc/gsa/gsa.tex
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@ -22,7 +22,7 @@
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\begin{document}
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% ----------------------------------------------------------------
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\title{Sensitivity Analysis Toolbox for DYNARE\thanks{Copyright \copyright~2012 Dynare
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\title{Sensitivity Analysis Toolbox for Dynare\thanks{Copyright \copyright~2012-2024 Dynare
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Team. Permission is granted to copy, distribute and/or modify
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this document under the terms of the GNU Free Documentation
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License, Version 1.3 or any later version published by the Free
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@ -32,9 +32,9 @@
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\author{Marco Ratto\\
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European Commission, Joint Research Centre \\
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TP361, IPSC, \\21027 Ispra
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TP581\\21027 Ispra
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(VA) Italy\\
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\texttt{marco.ratto@jrc.ec.europa.eu}
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\texttt{Marco.Ratto@ec.europa.eu}
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\thanks{The author gratefully thanks Christophe Planas, Kenneth Judd, Michel Juillard,
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Alessandro Rossi, Frank Schorfheide and the participants to the
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Courses on Global Sensitivity Analysis for Macroeconomic
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@ -52,21 +52,21 @@ helpful suggestions.}}
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%-----------------------------------------------------------------------
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\begin{abstract}
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\noindent The Sensitivity Analysis Toolbox for DYNARE is a set of
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\noindent The Sensitivity Analysis Toolbox for Dynare is a set of
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MATLAB routines for the analysis of DSGE models with global
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sensitivity analysis. The routines are thought to be used within
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the DYNARE v4 environment.
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the Dynare 6 environment.
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\begin{description}
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\item \textbf{Keywords}: Stability Mapping , Reduced form solution, DSGE models,
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Monte Carlo filtering, Global Sensitivity Analysis.
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Monte Carlo filtering, Global Sensitivity Analysis.
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\end{description}
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\end{abstract}
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\newpage
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% ----------------------------------------------------------------
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\section{Introduction} \label{s:intro}
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The Sensitivity Analysis Toolbox for DYNARE is a collection of
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The Sensitivity Analysis Toolbox for Dynare is a collection of
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MATLAB routines implemented to answer the following questions: (i)
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Which is the domain of structural coefficients assuring the
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stability and determinacy of a DSGE model? (ii) Which parameters
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@ -81,20 +81,18 @@ described in \cite{Ratto_CompEcon_2008}.
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\section{Use of the Toolbox}
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The DYNARE parser now recognizes sensitivity analysis commands.
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The Dynare parser now recognizes sensitivity analysis commands.
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The syntax is based on a single command:
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\vspace{0.5cm}
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\verb"dynare_sensitivity(option1=<opt1_val>,option2=<opt2_val>,...)"
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\verb"sensitivity(option1=<opt1_val>,option2=<opt2_val>,...)"
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\vspace{0.5cm} \noindent with a list of options described in the
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next section.
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With respect to the previous version of the toolbox, in order to
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work properly, the sensitivity analysis Toolbox \emph{no longer}
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needs that the DYNARE estimation environment is set-up.
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Therefore, \verb"dynare_sensitivity" is the only command to run to
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In order to work properly, the sensitivity analysis Toolbox does not need
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a Dynare estimation environment to be set up. Rather, \verb"sensitivity"
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is the only command to run to
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make a sensitivity analysis on a DSGE model\footnote{Of course,
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when the user needs to perform the mapping of the fit with a
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posterior sample, a Bayesian estimation has to be performed
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@ -208,16 +206,17 @@ a multivariate normal MC sample, with covariance matrix based on
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the inverse Hessian at the optimum: this analysis is useful when
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ML estimation is done (i.e. no Bayesian estimation);
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\item when \verb"ppost=1" the Toolbox analyses
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the RMSE's for the posterior sample obtained by DYNARE's
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the RMSE's for the posterior sample obtained by Dynare's
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Metropolis procedure.
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\end{enumerate}
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The use of cases 2. and 3. requires an estimation step beforehand!
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The use of cases 2. and 3. require an estimation step beforehand!
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To facilitate the sensitivity analysis after estimation, the
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\verb"dynare_sensitivity" command also allows to indicate some
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options of \verb"dynare_estimation". These are:
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\verb"sensitivity" command also allows to indicate some
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options of \verb"estimation". These are:
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\begin{itemize}
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\item \verb"datafile"
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\item \verb"diffuse_filter"
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\item \verb"mode_file"
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\item \verb"first_obs"
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\item \verb"lik_init"
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@ -278,10 +277,10 @@ identifiable.
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\end{tabular}
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\vspace{1cm}
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\noindent For example, the following commands in the DYNARE model file
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\noindent For example, the following commands in the Dynare model file
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\vspace{1cm}
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\noindent\verb"dynare_sensitivity(identification=1, morris=2);"
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\noindent\verb"sensitivity(identification=1, morris=2);"
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\vspace{1cm}
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\noindent trigger the identification analysis using \cite{Iskrev2010,Iskrev2011}, jointly with the mapping of the acceptable region.
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@ -293,75 +292,75 @@ Sensitivity analysis results are saved on the hard-disk of the
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computer. The Toolbox uses a dedicated folder called \verb"GSA",
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located in \\
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\\
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\verb"<DYNARE_file>\GSA", \\
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\verb"<Dynare_file>\GSA", \\
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\\
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where \verb"<DYNARE_file>.mod" is the name of the DYNARE model
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where \verb"<Dynare_file>.mod" is the name of the Dynare model
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file.
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\subsection{Binary data files}
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A set of binary data files is saved in the \verb"GSA" folder:
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\begin{description}
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\item[]\verb"<DYNARE_file>_prior.mat": this file stores
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\item[]\verb"<Dynare_file>_prior.mat": this file stores
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information about the analyses performed sampling from the prior
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ranges, i.e. \verb"pprior=1" and \verb"ppost=0";
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\item[]\verb"<DYNARE_file>_mc.mat": this file stores
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\item[]\verb"<Dynare_file>_mc.mat": this file stores
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information about the analyses performed sampling from
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multivariate normal, i.e. \verb"pprior=0" and \verb"ppost=0";
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\item[]\verb"<DYNARE_file>_post.mat": this file stores information
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\item[]\verb"<Dynare_file>_post.mat": this file stores information
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about analyses performed using the Metropolis posterior sample,
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i.e. \verb"ppost=1".
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\end{description}
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\begin{description}
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\item[]\verb"<DYNARE_file>_prior_*.mat": these files store
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\item[]\verb"<Dynare_file>_prior_*.mat": these files store
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the filtered and smoothed variables for the prior MC sample,
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generated when doing RMSE analysis (\verb"pprior=1" and
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\verb"ppost=0");
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\item[]\verb"<DYNARE_file>_mc_*.mat": these files store
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\item[]\verb"<Dynare_file>_mc_*.mat": these files store
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the filtered and smoothed variables for the multivariate normal MC
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sample, generated when doing RMSE analysis (\verb"pprior=0" and
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\verb"ppost=0").
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\end{description}
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\subsection{Stability analysis}
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Figure files \verb"<DYNARE_file>_prior_*.fig" store results for
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Figure files \verb"<Dynare_file>_prior_*.fig" store results for
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the stability mapping from prior MC samples:
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\begin{description}
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\item[]\verb"<DYNARE_file>_prior_stab_SA_*.fig": plots of the Smirnov
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test analyses confronting the cdf of the sample fulfilling
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Blanchard-Kahn conditions with the cdf of the rest of the sample;
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\item[]\verb"<DYNARE_file>_prior_stab_indet_SA_*.fig": plots of the Smirnov
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test analyses confronting the cdf of the sample producing
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indeterminacy with the cdf of the original prior sample;
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\item[]\verb"<DYNARE_file>_prior_stab_unst_SA_*.fig": plots of the Smirnov
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test analyses confronting the cdf of the sample producing unstable
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(explosive roots) behaviour with the cdf of the original prior
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\item[]\verb"<Dynare_file>_prior_stab_SA_*.fig": plots of the Smirnov
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test analyses confronting the CDF of the sample fulfilling
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Blanchard-Kahn conditions with the CDF of the rest of the sample;
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\item[]\verb"<Dynare_file>_prior_stab_indet_SA_*.fig": plots of the Smirnov
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test analyses confronting the CDF of the sample producing
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indeterminacy with the CDF of the original prior sample;
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\item[]\verb"<Dynare_file>_prior_stab_unst_SA_*.fig": plots of the Smirnov
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test analyses confronting the CDF of the sample producing unstable
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(explosive roots) behaviour with the CDF of the original prior
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sample;
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\item[]\verb"<DYNARE_file>_prior_stable_corr_*.fig": plots of
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\item[]\verb"<Dynare_file>_prior_stable_corr_*.fig": plots of
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bivariate projections of the sample fulfilling Blanchard-Kahn
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conditions;
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\item[]\verb"<DYNARE_file>_prior_indeterm_corr_*.fig": plots of
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\item[]\verb"<Dynare_file>_prior_indeterm_corr_*.fig": plots of
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bivariate projections of the sample producing indeterminacy;
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\item[]\verb"<DYNARE_file>_prior_unstable_corr_*.fig": plots of
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\item[]\verb"<Dynare_file>_prior_unstable_corr_*.fig": plots of
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bivariate projections of the sample producing instability;
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\item[]\verb"<DYNARE_file>_prior_unacceptable_corr_*.fig": plots of
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\item[]\verb"<Dynare_file>_prior_unacceptable_corr_*.fig": plots of
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bivariate projections of the sample producing unacceptable
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solutions, i.e. either instability or indeterminacy or the
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solution could not be found (e.g. the steady state solution could
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not be found by the solver).
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\end{description}
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Similar conventions apply for \verb"<DYNARE_file>_mc_*.fig" files,
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Similar conventions apply for \verb"<Dynare_file>_mc_*.fig" files,
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obtained when samples from multivariate normal are used.
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\subsection{RMSE analysis}
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Figure files \verb"<DYNARE_file>_rmse_*.fig" store results for the
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Figure files \verb"<Dynare_file>_rmse_*.fig" store results for the
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RMSE analysis.
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\begin{description}
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\item[]\verb"<DYNARE_file>_rmse_prior*.fig": save results for
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\item[]\verb"<Dynare_file>_rmse_prior*.fig": save results for
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the analysis using prior MC samples;
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\item[]\verb"<DYNARE_file>_rmse_mc*.fig": save results for
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\item[]\verb"<Dynare_file>_rmse_mc*.fig": save results for
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the analysis using multivariate normal MC samples;
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\item[]\verb"<DYNARE_file>_rmse_post*.fig": save results for
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\item[]\verb"<Dynare_file>_rmse_post*.fig": save results for
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the analysis using Metropolis posterior samples.
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\end{description}
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@ -369,33 +368,33 @@ The following types of figures are saved (we show prior sample to
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fix ideas, but the same conventions are used for multivariate
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normal and posterior):
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\begin{description}
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\item[]\verb"<DYNARE_file>_rmse_prior_*.fig": for each parameter, plots the cdf's
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\item[]\verb"<Dynare_file>_rmse_prior_*.fig": for each parameter, plots the CDF's
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corresponding to the best 10\% RMES's of each observed series;
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\item[]\verb"<DYNARE_file>_rmse_prior_dens_*.fig": for each parameter, plots the pdf's
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\item[]\verb"<Dynare_file>_rmse_prior_dens_*.fig": for each parameter, plots the pdf's
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corresponding to the best 10\% RMES's of each observed series;
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\item[]\verb"<DYNARE_file>_rmse_prior_<name of observedseries>_corr_*.fig": for each observed series plots the
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\item[]\verb"<Dynare_file>_rmse_prior_<name of observedseries>_corr_*.fig": for each observed series plots the
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bi-dimensional projections of samples with the best 10\% RMSE's,
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when the correlation is significant;
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\item[]\verb"<DYNARE_file>_rmse_prior_lnlik*.fig": for each observed
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series, plots \emph{in red} the cdf of the log-likelihood
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corresponding to the best 10\% RMSE's, \emph{in green} the cdf of
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the rest of the sample and \emph{in blue }the cdf of the full
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\item[]\verb"<Dynare_file>_rmse_prior_lnlik*.fig": for each observed
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series, plots \emph{in red} the CDF of the log-likelihood
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corresponding to the best 10\% RMSE's, \emph{in green} the CDF of
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the rest of the sample and \emph{in blue }the CDF of the full
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sample; this allows to see the presence of some idiosyncratic
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behaviour;
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\item[]\verb"<DYNARE_file>_rmse_prior_lnpost*.fig": for each observed
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series, plots \emph{in red} the cdf of the log-posterior
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corresponding to the best 10\% RMSE's, \emph{in green} the cdf of
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the rest of the sample and \emph{in blue }the cdf of the full
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\item[]\verb"<Dynare_file>_rmse_prior_lnpost*.fig": for each observed
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series, plots \emph{in red} the CDF of the log-posterior
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corresponding to the best 10\% RMSE's, \emph{in green} the CDF of
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the rest of the sample and \emph{in blue }the CDF of the full
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sample; this allows to see idiosyncratic behaviour;
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\item[]\verb"<DYNARE_file>_rmse_prior_lnprior*.fig": for each observed
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series, plots \emph{in red} the cdf of the log-prior corresponding
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to the best 10\% RMSE's, \emph{in green} the cdf of the rest of
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the sample and \emph{in blue }the cdf of the full sample; this
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\item[]\verb"<Dynare_file>_rmse_prior_lnprior*.fig": for each observed
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series, plots \emph{in red} the CDF of the log-prior corresponding
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to the best 10\% RMSE's, \emph{in green} the CDF of the rest of
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the sample and \emph{in blue }the CDF of the full sample; this
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allows to see idiosyncratic behaviour;
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\item[]\verb"<DYNARE_file>_rmse_prior_lik_SA_*.fig": when
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\item[]\verb"<Dynare_file>_rmse_prior_lik_SA_*.fig": when
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\verb"lik_only=1", this shows the Smirnov tests for the filtering
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of the best 10\% log-likelihood values;
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\item[]\verb"<DYNARE_file>_rmse_prior_post_SA_*.fig": when
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\item[]\verb"<Dynare_file>_rmse_prior_post_SA_*.fig": when
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\verb"lik_only=1", this shows the Smirnov test for the filtering
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of the best 10\% log-posterior values.
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\end{description}
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@ -405,19 +404,19 @@ In the case of the mapping of the reduced form solution, synthetic
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figures are saved in the \verb"\GSA" folder:
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\begin{description}
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\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_lags_*.fig":
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\item[]\verb"<Dynare_file>_redform_<endo name>_vs_lags_*.fig":
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shows bar charts of the sensitivity indices for the \emph{ten most
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important} parameters driving the reduced form coefficients of the
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selected endogenous variables (\verb"namendo") versus lagged
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endogenous variables (\verb"namlagendo"); suffix \verb"log"
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indicates the results for log-transformed entries;
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\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_shocks_*.fig":
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\item[]\verb"<Dynare_file>_redform_<endo name>_vs_shocks_*.fig":
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shows bar charts of the sensitivity indices for the \emph{ten most
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important} parameters driving the reduced form coefficients of the
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selected endogenous variables (\verb"namendo") versus exogenous
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variables (\verb"namexo"); suffix \verb"log" indicates the results
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for log-transformed entries;
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\item[]\verb"<DYNARE_file>_redform_GSA(_log).fig": shows bar chart of
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\item[]\verb"<Dynare_file>_redform_GSA(_log).fig": shows bar chart of
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all sensitivity indices for each parameter: this allows to notice
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parameters that have a minor effect for \emph{any} of the reduced
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form coefficients,
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@ -449,24 +448,24 @@ without the need of any user's intervention.
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\subsection{Screening analysis}
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The results of the screening analysis with Morris sampling design
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are stored in the subfolder \verb"\GSA\SCREEN". The data file
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\verb"<DYNARE_file>_prior" stores all the information of the
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\verb"<Dynare_file>_prior" stores all the information of the
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analysis (Morris sample, reduced form coefficients, etc.).
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Screening analysis merely concerns reduced form coefficients.
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Similar synthetic bar charts as for the reduced form analysis with
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MC samples are saved:
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\begin{description}
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\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_lags_*.fig":
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\item[]\verb"<Dynare_file>_redform_<endo name>_vs_lags_*.fig":
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shows bar charts of the elementary effect tests for the \emph{ten
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most important} parameters driving the reduced form coefficients
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of the selected endogenous variables (\verb"namendo") versus
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lagged endogenous variables (\verb"namlagendo");
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\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_shocks_*.fig":
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\item[]\verb"<Dynare_file>_redform_<endo name>_vs_shocks_*.fig":
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shows bar charts of the elementary effect tests for the \emph{ten
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most important} parameters driving the reduced form coefficients
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of the selected endogenous variables (\verb"namendo") versus
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exogenous variables (\verb"namexo");
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\item[]\verb"<DYNARE_file>_redform_screen.fig": shows bar chart of
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\item[]\verb"<Dynare_file>_redform_screen.fig": shows bar chart of
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all elementary effect tests for each parameter: this allows to
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identify parameters that have a minor effect for \emph{any} of the
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reduced form coefficients.
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