

    \filetitle{uncmean}{Unconditional-mean dummy (or Sims' initial dummy) observations for BVARs}{BVAR/uncmean}

	\paragraph{Syntax}
 
 \begin{verbatim}
 [X,Y0,K,Y1,G1] = BVAR.uncmean(YBAR,MU,[NY,P])
 [X,Y0,K,Y1,G1] = BVAR.uncmean(ybar,mu,[ny,p,ng])
 \end{verbatim}
 \paragraph{Input arguments}
 
 \begin{itemize}
 \item
   \texttt{YBAR} {[} numeric {]} - Vector of unconditional means imposed
   as priors.
 \item
   \texttt{MU} {[} numeric {]} - Weight on the dummy observations.
 \item
   \texttt{{[}NY,P,NG{]}} {[} numeric {]} - Number of variables, order of
   the VAR, and number of co-integrating vectors in the VAR for which the
   prior dummies will be created.
 \end{itemize}
 \paragraph{Output arguments}
 
 \begin{itemize}
 \item
   \texttt{X} {[} numeric {]} - Array with prior dummy observations that
   can be used in the \texttt{'BVAR='} option of the \url{VAR/estimate}
   function.
 \item
   \texttt{Y0}, \texttt{K}, \texttt{Y1}, \texttt{G1} {[} numeric {]} -
   These output arguments provide more details on the structure of the
   dummy observations, and have no use; \texttt{X={[}Y0;K;Y1;G1{]}}.
 \end{itemize}
 \paragraph{Description}
 
 \paragraph{Example}


