%% Prepare Prior for Exogenous Predictor Variables
% Consider the 2-D VARX(1) model for the US real GDP (RGDP) and investment (GCE) rates that treats the personal consumption (PCEC) rate  as exogenous:
% \left[ {\begin{array}{*{20}{l}}
% {\texttt{RGDP}_t}\\
% {\texttt{GCE}_t}
% \end{array}} \right] = c + \Phi\left[ {\begin{array}{*{20}{l}}
% {\texttt{RGDP}_{t-1}\\
% {\texttt{GCE}_{t-1}
% \end{array}} \right] + \texttt{PCEC}_t\beta+\varepsilon_t.
% For all t, \varepsilon_t is a series of independent 2-D normal innovations with a mean of 0 and covariance \Sigma. Assume that the joint prior distribution is diffuse.
% Create a diffuse prior model for the 2-D VARX(1) model parameters.

numseries = 2;
numlags = 1;
numpredictors = 1;
PriorMdl = diffusebvarm(numseries,numlags,'NumPredictors',numpredictors)

% Copyright 2020 The MathWorks, Inc.
%