
    \foldertitle{BVAR}{Bayesian VAR prior dummies: BVAR package}{BVAR/Contents}

	The BVAR package can be used to create the basic types of prior dummy
observations when estimating Bayesian VAR models. The dummy observations
are passed in the \href{VAR/estimate}{\texttt{VAR/estimate}} function
through the \texttt{'BVAR='} option.

\paragraph{Constructing dummy
observations}

\begin{itemize}
\itemsep1pt\parskip0pt\parsep0pt
\item
  \href{BVAR/covmat}{\texttt{covmat}} - Covariance matrix prior dummy
  observations for BVARs.
\item
  \href{BVAR/litterman}{\texttt{litterman}} - Litterman's prior dummy
  observations for BVARs.
\item
  \href{BVAR/sumofcoeff}{\texttt{sumofcoeff}} - Doan et al
  sum-of-coefficient prior dummy observations for BVARs.
\item
  \href{BVAR/uncmean}{\texttt{uncmean}} - Unconditional-mean dummy (or
  Sims' initial dummy) observations for BVARs.
\item
  \href{BVAR/user}{\texttt{user}} - User-supplied prior dummy
  observations for BVARs.
\end{itemize}

\paragraph{Weights on prior dummy
observations}

The prior dummies produced by \href{BVAR/litterman}{\texttt{litterman}},
\href{BVAR/uncmean}{\texttt{uncmean}},
\href{BVAR/sumofcoeff}{\texttt{sumofcoeff}} can be weighted up or down
using the input argument \texttt{Mu}. To give the weight a clear
interpretation, use the option \texttt{'stdize=' true} when estimating
the VAR. In that case, setting \texttt{Mu} to \texttt{sqrt(N)} means the
prior dummies are worth a total of extra \texttt{N} artifical
observations; the weight can be related to the actual number of
observations used in estimation.

\paragraph{Getting help on BVAR
functions}

\begin{verbatim}
help BVAR
help BVAR/function_name
\end{verbatim}



