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Fixed bug related to the mh based approximation of the posterior covariance matrix (used 

for the jumping distribution).
time-shift
Stéphane Adjemian (Scylla) 2010-09-01 22:15:47 +02:00
parent ded34f990b
commit 29f9bb953e
4 changed files with 539 additions and 527 deletions
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12
matlab/PlotPosteriorDistributions.m
View File

@ -88,7 +88,7 @@ for i=1:npar
eval(['f1 = oo_.posterior_density.shocks_std.' name '(:,2);'])
eval(['oo_.prior_density.shocks_std.' name '(:,1) = x2;'])
eval(['oo_.prior_density.shocks_std.' name '(:,2) = f2;'])
if options_.posterior_mode_estimation
if ~options_.mh_posterior_mode_estimation
eval(['pmod = oo_.posterior_mode.shocks_std.' name ';'])
end
elseif i <= nvx+nvn
@ -97,7 +97,7 @@ for i=1:npar
eval(['f1 = oo_.posterior_density.measurement_errors_std.' name '(:,2);'])
eval(['oo_.prior_density.mearsurement_errors_std.' name '(:,1) = x2;'])
eval(['oo_.prior_density.measurement_errors_std.' name '(:,2) = f2;'])
if options_.posterior_mode_estimation
if ~options_.mh_posterior_mode_estimation
eval(['pmod = oo_.posterior_mode.measurement_errors_std.' name ';'])
end
elseif i <= nvx+nvn+ncx
@ -109,7 +109,7 @@ for i=1:npar
eval(['f1 = oo_.posterior_density.shocks_corr.' name '(:,2);'])
eval(['oo_.prior_density.shocks_corr.' name '(:,1) = x2;'])
eval(['oo_.prior_density.shocks_corr.' name '(:,2) = f2;'])
if options_.posterior_mode_estimation
if ~options_.mh_posterior_mode_estimation
eval(['pmod = oo_.posterior_mode.shocks_corr.' name ';'])
end
elseif i <= nvx+nvn+ncx+ncn
@ -121,7 +121,7 @@ for i=1:npar
eval(['f1 = oo_.posterior_density.measurement_errors_corr.' name '(:,2);'])
eval(['oo_.prior_density.mearsurement_errors_corr.' name '(:,1) = x2;'])
eval(['oo_.prior_density.measurement_errors_corr.' name '(:,2) = f2;'])
if options_.posterior_mode_estimation
if ~options_.mh_posterior_mode_estimation
eval(['pmod = oo_.posterior_mode.measurement_errors_corr.' name ';'])
end
else
@ -131,7 +131,7 @@ for i=1:npar
eval(['f1 = oo_.posterior_density.parameters.' name '(:,2);'])
eval(['oo_.prior_density.parameters.' name '(:,1) = x2;'])
eval(['oo_.prior_density.parameters.' name '(:,2) = f2;'])
if options_.posterior_mode_estimation
if ~options_.mh_posterior_mode_estimation
eval(['pmod = oo_.posterior_mode.parameters.' name ';'])
end
end
@ -146,7 +146,7 @@ for i=1:npar
set(hh,'color',[0.7 0.7 0.7]);
hold on;
plot(x1,f1,'-k','linewidth',2);
if options_.posterior_mode_estimation
if ~options_.mh_posterior_mode_estimation
plot( [pmod pmod], [0.0 1.1*top0], '--g', 'linewidth', 2);
end
box on;

10
matlab/compute_mh_covariance_matrix.m
View File

@ -1,7 +1,7 @@
function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix()
% function [m0,s0] = compute_mh_covariance_matrix()
% Estimation of the posterior covariance matrix and expectation.
% Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
% estimated mode, using the draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
% a file <M_.fname>_mh_mode.mat.
%
% INPUTS
% None.
@ -10,12 +10,12 @@ function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at
% o posterior_mean [double] (n*1) vector, posterior expectation of the parameters.
% o posterior_covariance [double] (n*n) matrix, posterior covariance of the parameters (computed from previous metropolis hastings).
% o posterior_mode [double] (n*1) vector, posterior mode of the parameters.
% o posterior_kernel_at_the_mode [double] scalar.
% o posterior_kernel_at_the_mode [double] scalar.
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2008 Dynare Team
% Copyright (C) 2006-2008, 2010 Dynare Team
%
% This file is part of Dynare.
%

1042
matlab/dynare_estimation_1.m
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File diff suppressed because it is too large Load Diff

2
matlab/global_initialization.m
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@ -176,7 +176,7 @@ options_.moments_varendo = 0;
options_.nk = 1;
options_.noconstant = 0;
options_.nodiagnostic = 0;
options_.posterior_mode_estimation = 1;
options_.mh_posterior_mode_estimation = 0;
options_.prefilter = 0;
options_.presample = 0;
options_.prior_trunc = 1e-10;