%% Create Bayesian AR(2) Model with Normal Conjugate Coefficient Prior
% Consider a 1-D Bayesian AR(2) model for the daily NASDAQ returns from January 2, 1990 through December 31, 2001.
% y_t=c+\phi_1 y_{t-1} + \phi_2y_{t-1} + \varepsilon_t.
% The coefficient prior distribution is {[\begin{array}{*{20}{c}}
% \phi_1 &\phi_2 & c
% \end{array}]^\prime }|{\sigma ^2} \sim {{\rm{N}}_3}\left( {\mu ,V} \right), where \mu is a 3-by-1 vector of coefficient means and V is a 3-by-3  covariance matrix. Assume Var(\varepsilon_t) is 2.
% Create a normal conjugate prior model for the AR(2) model parameters.

numseries = 1;
numlags = 2;
PriorMdl = normalbvarm(numseries,numlags,'Sigma',2)

% Copyright 2020 The MathWorks, Inc.
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