

    \filetitle{litterman}{Litterman's prior dummy observations for BVARs}{BVAR/litterman}

	\paragraph{Syntax}
 
 \begin{verbatim}
 [X,Y0,K,Y1,G1] = BVAR.litterman(RHO,SIGMA,LAMBDA,[NY,P])
 [X,Y0,K,Y1,G1] = BVAR.litterman(RHO,SIGMA,LAMBDA,[NY,P,NG])
 \end{verbatim}
 \paragraph{Input arguments}
 
 \begin{itemize}
 \item
   \texttt{RHO} {[} numeric {]} - White-noise priors (\texttt{RHO = 0})
   or random-walk priors (\texttt{RHO = 1}).
 \item
   \texttt{SIGMA} {[} numeric {]} - Weight on the dummy observations.
 \item
   \texttt{LAMBDA} {[} numeric {]} - Exponential increase in weight
   depending on the lag; \texttt{LAMBDA = 0} means all lags are weighted
   equally.
 \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 extra output arguments only 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}


