Cosmetic changes to the documentation of BVAR "à la Sims"

doc/bvar-a-la-sims.tex

-\documentclass[10pt,a4paper]{article}
+\documentclass[11pt,a4paper]{article}
 
 \usepackage{amsmath}
 \usepackage{amssymb}
-\usepackage{url}
+\usepackage{hyperref}
+\hypersetup{breaklinks=true,pagecolor=white,colorlinks=true,linkcolor=blue,citecolor=blue,urlcolor=blue}
+\usepackage{fullpage}
+\usepackage{textcomp}
 
 \newcommand{\df}{\text{df}}
 
 \begin{document}
 
-\title{BVAR models ``\`a la Sims'' in Dynare}
-\author{S\'ebastien Villemot\thanks{CEPREMAP. E-mail: \texttt{sebastien.villemot@ens.fr}}}
-\date{September 2007}
+\title{BVAR models ``\`a la Sims'' in Dynare\thanks{Copyright \copyright~2007--2011 S\'ebastien
+    Villemot. Permission is granted to copy, distribute and/or modify
+    this document under the terms of the GNU Free Documentation
+    License, Version 1.3 or any later version published by the Free
+    Software Foundation; with no Invariant Sections, no Front-Cover
+    Texts, and no Back-Cover Texts. A copy of the license can be found
+    at: \url{http://www.gnu.org/licenses/fdl.txt}
+    \newline
+    \indent Many thanks to Christopher Sims for providing his BVAR
+    MATLAB\textregistered~routines, to St\'ephane Adjemian and Michel Juillard
+    for their helpful support, and to Marek Jaroci\'nski for reporting a bug.
+  }}
+
+\author{S\'ebastien Villemot\thanks{Paris School of Economics and
+    CEPREMAP. E-mail:
+    \href{mailto:sebastien.villemot@ens.fr}{\texttt{sebastien.villemot@ens.fr}}.}}
+\date{First version: September 2007 \hspace{1cm} This version: September 2011}
 
 \maketitle
 
-Dynare incorporates routines for BVAR models estimation, that can be used alone or in parallel with a DSGE estimation.
-This document describes their implementation and usage.
+\begin{abstract}
+  Dynare incorporates routines for Bayesian VAR models estimation, using a
+  flavor of the so-called ``Minnesota priors,''. These routines can be used
+  alone or in parallel with a DSGE estimation. This document describes their
+  implementation and usage.
+\end{abstract}
 
 If you are impatient to try the software and wish to skip mathematical details, jump to section \ref{dynare-commands}.
 
@@ -94,7 +115,7 @@ The second component of the prior is constructed from the likelihood of $T^*$ du
 
 $$p_2(\Phi, \Sigma) \propto |\Sigma|^{-T^*/2} \exp\left\{-\frac{1}{2}Tr(\Sigma^{-1}(Y^*-X^*\Phi)'(Y^*-X^*\Phi))\right\}$$
 
-The dummy observations are constructed according to Sims' version of the Minnesota prior\footnote{See Doan, Litterman and Sims (1984).}.
+The dummy observations are constructed according to Sims' version of the Minnesota prior.\footnote{See Doan, Litterman and Sims (1984).}
 
 Before constructing the dummy observations, one needs to choose values for the following parameters:
 \begin{itemize}
@@ -394,7 +415,7 @@ f(\Phi,\Sigma | \df,S,\hat{\Phi},\Omega) & = & |\Sigma|^{-(\df + ny + 1)/2} \exp
 We also note:
 $$F(\df,S,\hat{\Phi},\Omega) = \int f(\Phi,\Sigma | \df,S,\hat{\Phi},\Omega)d\Phi d\Sigma$$
 
-The function $F$ has an analytical form, which is given by the normalization constants of matrix-normal and inverse-Wishart densities\footnote{Function \texttt{matricint} of file \texttt{bvar\_density.m} implements the calculation of the log of $F$.}:
+The function $F$ has an analytical form, which is given by the normalization constants of matrix-normal and inverse-Wishart densities:\footnote{Function \texttt{matricint} of file \texttt{bvar\_density.m} implements the calculation of the log of $F$.}
 
 $$F(\df,S,\hat{\Phi},\Omega) = (2\pi)^{\frac{ny\cdot k}{2}} |\Omega|^{\frac{ny}{2}} \cdot 2^{\frac{ny\cdot \df}{2}} \pi^{\frac{ny(ny-1)}{4}} |S|^{-\frac{\df}{2}} \prod_{i=1}^{ny} \Gamma\left(\frac{\df + 1 - i}{2}\right) $$
 
@@ -464,7 +485,7 @@ Note that option \texttt{prefilter} implies option \texttt{noconstant}.
 
 Please also note that if option \texttt{loglinear} had been specified in a previous \texttt{estimation} statement, without option \texttt{logdata}, then the BVAR model will be estimated on the log of the provided dataset, for maintaining coherence with the DSGE estimation procedure.
 
-\emph{Restrictions related to the initialization of lags:} in DSGE estimation routines, the likelihood (and therefore the marginal density) are evaluated starting from the observation numbered \texttt{first\_obs + presample} in the datafile\footnote{\texttt{first\_obs} points to the first observation to be used in the datafile (defaults to 1), and \texttt{presample} indicates how many observations after \texttt{first\_obs} will be used to initialize the Kalman filter (defaults to 0).}. The BVAR estimation routines use the same convention (i.e. the first observation of $Y^+$ will be \texttt{first\_obs + presample}). Since we need $p$ observations to initialize the lags, and since we may also use a training sample, the user must ensure that the following condition holds (estimation will fail otherwise):
+\emph{Restrictions related to the initialization of lags:} in DSGE estimation routines, the likelihood (and therefore the marginal density) are evaluated starting from the observation numbered \texttt{first\_obs + presample} in the datafile.\footnote{\texttt{first\_obs} points to the first observation to be used in the datafile (defaults to 1), and \texttt{presample} indicates how many observations after \texttt{first\_obs} will be used to initialize the Kalman filter (defaults to 0).} The BVAR estimation routines use the same convention (i.e. the first observation of $Y^+$ will be \texttt{first\_obs + presample}). Since we need $p$ observations to initialize the lags, and since we may also use a training sample, the user must ensure that the following condition holds (estimation will fail otherwise):
 $$\texttt{first\_obs} + \texttt{presample} > \texttt{bvar\_prior\_train} + \text{number\_of\_lags}$$
 
 
@@ -592,16 +613,5 @@ Schorfheide, Frank (2004), ``\textit{Notes on Model Evaluation}'', Department of
 
 Sims, Christopher (2003), ``\textit{Matlab Procedures to Compute Marginal Data Densities for VARs with Minnesota and Training Sample Priors}'', Department of Economics, Princeton University
 
-\section*{Acknowledgements}
-
-Many thanks to Christopher Sims for his BVAR Matlab routines, and to St\'ephane Adjemian and Michel Juillard for their helpful support.
-
-\section*{License}
-
-Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
-
-A copy of the license can be found at:
-\url{http://www.gnu.org/licenses/fdl.txt}
-
 \end{document}
 

license.txt

@@ -139,6 +139,16 @@ License: GFDL-1.3+
  .
  A copy of the license can be found at <http://www.gnu.org/licenses/fdl.txt>
 
+Files: doc/bvar_a_la_sims.tex
+Copyright: 2007-2011, Sébastien Villemot
+License: GFDL-1.3+
+ Permission is granted to copy, distribute and/or modify this document
+ under the terms of the GNU Free Documentation License, Version 1.3 or
+ any later version published by the Free Software Foundation; with no
+ Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
+ .
+ A copy of the license can be found at <http://www.gnu.org/licenses/fdl.txt>
+
 Files: dynare++/*.cweb, dynare++/*.hweb, dynare++/*.cpp, dynare++/*.h,
  dynare++/*.tex, dynare++/*.mod, dynare++/*.m, dynare++/*.web, dynare++/*.lex,
  dynare++/*.y