/usr/share/dynare/matlab/bvar_density.m is in dynare-common 4.4.1-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | function bvar_density(maxnlags)
% function bvar_density(maxnlags)
% computes the density of a bayesian var
%
% INPUTS
% maxnlags: maximum number of lags in the bvar
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2003-2007 Christopher Sims
% Copyright (C) 2007-2009 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
for nlags = 1:maxnlags
[ny, nx, posterior, prior] = bvar_toolbox(nlags);
posterior_int = matrictint(posterior.S, posterior.df, posterior.XXi);
prior_int = matrictint(prior.S, prior.df, prior.XXi);
lik_nobs = posterior.df - prior.df;
log_dnsty = posterior_int - prior_int - 0.5*ny*lik_nobs*log(2*pi);
skipline()
fprintf('The marginal log density of the BVAR(%g) model is equal to %10.4f\n', ...
nlags, log_dnsty);
skipline()
end
function w = matrictint(S, df, XXi)
% Computes the log of the integral of the kernel of the PDF of a
% normal-inverse-Wishart distribution.
%
% S: parameter of inverse-Wishart distribution
% df: number of degrees of freedom of inverse-Wishart distribution
% XXi: first component of VCV matrix of matrix-normal distribution
%
% Computes the integral over (Phi, Sigma) of:
%
% det(Sigma)^(-k/2)*exp(-0.5*Tr((Phi-PhiHat)'*(XXi)^(-1)*(Phi-PhiHat)*Sigma^(-1)))*
% det(Sigma)^((df+ny+1)/2)*exp(-0.5*Tr(Sigma^(-1)*S))
%
% (where k is the dimension of XXi and ny is the dimension of S and
% Sigma)
% Original file downloaded from:
% http://sims.princeton.edu/yftp/VARtools/matlab/matrictint.m
k=size(XXi,1);
ny=size(S,1);
[cx,p]=chol(XXi);
[cs,q]=chol(S);
if any(diag(cx)<100*eps)
error('singular XXi')
end
if any(diag(cs<100*eps))
error('singular S')
end
% Matrix-normal component
w1 = 0.5*k*ny*log(2*pi)+ny*sum(log(diag(cx)));
% Inverse-Wishart component
w2 = -df*sum(log(diag(cs))) + 0.5*df*ny*log(2) + ny*(ny-1)*0.25*log(pi) + ggammaln(ny, df);
w = w1 + w2;
function lgg = ggammaln(m, df)
if df <= (m-1)
error('too few df in ggammaln')
else
garg = 0.5*(df+(0:-1:1-m));
lgg = sum(gammaln(garg));
end
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