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1) save output plots; 

2) modified burnin_iteraton for pdraw0 in input;
3) minor changes.

git-svn-id: https://www.dynare.org/svn/dynare/trunk@3033 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
ratto 2009-10-09 08:22:01 +00:00
parent 173ce64d7e
commit 6e25472ad9
1 changed files with 21 additions and 10 deletions
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31
matlab/dynare_identification.m
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@ -75,8 +75,12 @@ h = waitbar(0,'Monte Carlo identification checks ...');
while iteration < SampleSize,
loop_indx = loop_indx+1;
if nargin==2 & burnin_iteration>=50,
params = pdraws0(iteration+1,:);
if nargin==2,
if burnin_iteration>=50,
params = pdraws0(iteration+1,:);
else
params = pdraws0(burnin_iteration+1,:);
end
else
params = prior_draw();
end
@ -91,9 +95,10 @@ while iteration < SampleSize,
% bayestopt_.restrict_columns, ...
% bayestopt_.restrict_aux, M_.exo_nbr);
% tau=[vec(Aa); vech(Bb*M_.Sigma_e*Bb')];
tau=[vec(A); vech(B*M_.Sigma_e*B')];
tau=[oo_.dr.ys(oo_.dr.order_var); vec(A); vech(B*M_.Sigma_e*B')];
if burnin_iteration<50,
burnin_iteration = burnin_iteration + 1;
pdraws(burnin_iteration,:) = params;
TAU(:,burnin_iteration)=tau;
[gam,stationary_vars] = th_autocovariances(oo0.dr,bayestopt_.mfys,M_,options_);
sdy = sqrt(diag(gam{1}));
@ -110,13 +115,13 @@ while iteration < SampleSize,
for j=1:nlags,
dum = [dum; vec(gam{j+1})];
end
GAM(:,burnin_iteration)=dum;
GAM(:,burnin_iteration)=[oo_.dr.ys(bayestopt_.mfys); dum];
else
iteration = iteration + 1;
run_index = run_index + 1;
if iteration==1,
indJJ = (find(std(GAM')>1.e-10));
indH = (find(std(TAU')>1.e-10));
indJJ = (find(std(GAM')>1.e-8));
indH = (find(std(TAU')>1.e-8));
TAU = zeros(length(indH),SampleSize);
GAM = zeros(length(indJJ),SampleSize);
MAX_tau = min(SampleSize,ceil(MaxNumberOfBytes/(length(indH)*nparam)/8));
@ -134,8 +139,8 @@ while iteration < SampleSize,
stoH(:,:,run_index) = H(indH,:);
stoJJ(:,:,run_index) = JJ(indJJ,:);
% use relative changes
siJ = abs(JJ(indJJ,:).*(1./gam(indJJ)*params));
siH = abs(H(indH,:).*(1./tau(indH)*params));
% siJ = abs(JJ(indJJ,:).*(1./gam(indJJ)*params));
% siH = abs(H(indH,:).*(1./tau(indH)*params));
% use prior uncertainty
siJ = abs(JJ(indJJ,:));
siH = abs(H(indH,:));
@ -308,7 +313,7 @@ disp_identification(pdraws, idemodel, idemoments)
figure,
subplot(221)
myboxplot(siHmean)
set(gca,'ylim',[0 1])
set(gca,'ylim',[0 1.05])
set(gca,'xticklabel','')
for ip=1:nparam,
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
@ -317,7 +322,7 @@ title('Sensitivity in the model')
subplot(222)
myboxplot(siJmean)
set(gca,'ylim',[0 1])
set(gca,'ylim',[0 1.05])
set(gca,'xticklabel','')
for ip=1:nparam,
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
@ -341,6 +346,9 @@ for ip=1:nparam,
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Multicollinearity in the moments')
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident'])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident']);
figure,
@ -350,3 +358,6 @@ title('log10 of Condition number in the model')
subplot(222)
hist(log10(idemoments.cond))
title('log10 of Condition number in the moments')
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND'])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);