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Cleaner comments of work in progress ... 

git-svn-id: https://www.dynare.org/svn/dynare/trunk@3001 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
ratto 2009-09-30 13:28:47 +00:00
parent 3ef53218b8
commit 081cdf3cb0
1 changed files with 59 additions and 50 deletions
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109
matlab/dynare_identification.m
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@ -1,4 +1,4 @@
function [pdraws, TAU, GAM0, H, JJ] = dynare_identification(iload, pdraws0)
function [pdraws, TAU, GAM, H, JJ] = dynare_identification(iload, pdraws0)
% main
%
@ -206,55 +206,55 @@ if nargout>3 & iload,
end
end
mTAU = mean(TAU');
mGAM = mean(GAM');
sTAU = std(TAU');
sGAM = std(GAM');
if nargout>=3,
GAM0=GAM;
end
if useautocorr,
idiag = find(vech(eye(size(options_.varobs,1))));
GAM(idiag,:) = GAM(idiag,:)./(sGAM(idiag)'*ones(1,SampleSize));
% siJmean(idiag,:) = siJmean(idiag,:)./(sGAM(idiag)'*ones(1,nparam));
% siJmean = siJmean./(max(siJmean')'*ones(size(params)));
end
[pcc, dd] = eig(cov(GAM'));
[latent, isort] = sort(-diag(dd));
latent = -latent;
pcc=pcc(:,isort);
siPCA = (siJmean'*abs(pcc')).^2';
siPCA = siPCA./(max(siPCA')'*ones(1,nparam)).*(latent*ones(1,nparam));
siPCA = sum(siPCA,1);
siPCA = siPCA./max(siPCA);
[pcc, dd] = eig(corrcoef(GAM'));
[latent, isort] = sort(-diag(dd));
latent = -latent;
pcc=pcc(:,isort);
siPCA2 = (siJmean'*abs(pcc')).^2';
siPCA2 = siPCA2./(max(siPCA2')'*ones(1,nparam)).*(latent*ones(1,nparam));
siPCA2 = sum(siPCA2,1);
siPCA2 = siPCA2./max(siPCA2);
[pcc, dd] = eig(cov(TAU'));
[latent, isort] = sort(-diag(dd));
latent = -latent;
pcc=pcc(:,isort);
siHPCA = (siHmean'*abs(pcc')).^2';
siHPCA = siHPCA./(max(siHPCA')'*ones(1,nparam)).*(latent*ones(1,nparam));
siHPCA = sum(siHPCA,1);
siHPCA = siHPCA./max(siHPCA);
[pcc, dd] = eig(corrcoef(TAU'));
[latent, isort] = sort(-diag(dd));
latent = -latent;
pcc=pcc(:,isort);
siHPCA2 = (siHmean'*abs(pcc')).^2';
siHPCA2 = siHPCA2./(max(siHPCA2')'*ones(1,nparam)).*(latent*ones(1,nparam));
siHPCA2 = sum(siHPCA2,1);
siHPCA2 = siHPCA2./max(siHPCA2);
% mTAU = mean(TAU');
% mGAM = mean(GAM');
% sTAU = std(TAU');
% sGAM = std(GAM');
% if nargout>=3,
% GAM0=GAM;
% end
% if useautocorr,
% idiag = find(vech(eye(size(options_.varobs,1))));
% GAM(idiag,:) = GAM(idiag,:)./(sGAM(idiag)'*ones(1,SampleSize));
% % siJmean(idiag,:) = siJmean(idiag,:)./(sGAM(idiag)'*ones(1,nparam));
% % siJmean = siJmean./(max(siJmean')'*ones(size(params)));
% end
%
% [pcc, dd] = eig(cov(GAM'));
% [latent, isort] = sort(-diag(dd));
% latent = -latent;
% pcc=pcc(:,isort);
% siPCA = (siJmean'*abs(pcc')).^2';
% siPCA = siPCA./(max(siPCA')'*ones(1,nparam)).*(latent*ones(1,nparam));
% siPCA = sum(siPCA,1);
% siPCA = siPCA./max(siPCA);
%
% [pcc, dd] = eig(corrcoef(GAM'));
% [latent, isort] = sort(-diag(dd));
% latent = -latent;
% pcc=pcc(:,isort);
% siPCA2 = (siJmean'*abs(pcc')).^2';
% siPCA2 = siPCA2./(max(siPCA2')'*ones(1,nparam)).*(latent*ones(1,nparam));
% siPCA2 = sum(siPCA2,1);
% siPCA2 = siPCA2./max(siPCA2);
%
% [pcc, dd] = eig(cov(TAU'));
% [latent, isort] = sort(-diag(dd));
% latent = -latent;
% pcc=pcc(:,isort);
% siHPCA = (siHmean'*abs(pcc')).^2';
% siHPCA = siHPCA./(max(siHPCA')'*ones(1,nparam)).*(latent*ones(1,nparam));
% siHPCA = sum(siHPCA,1);
% siHPCA = siHPCA./max(siHPCA);
%
% [pcc, dd] = eig(corrcoef(TAU'));
% [latent, isort] = sort(-diag(dd));
% latent = -latent;
% pcc=pcc(:,isort);
% siHPCA2 = (siHmean'*abs(pcc')).^2';
% siHPCA2 = siHPCA2./(max(siHPCA2')'*ones(1,nparam)).*(latent*ones(1,nparam));
% siHPCA2 = sum(siHPCA2,1);
% siHPCA2 = siHPCA2./max(siHPCA2);
disp_identification(pdraws, idemodel, idemoments)
@ -341,3 +341,12 @@ for ip=1:nparam,
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Multicollinearity in the moments')
figure,
subplot(221)
hist(log10(idemodel.cond))
title('log10 of Condition number in the model')
subplot(222)
hist(log10(idemoments.cond))
title('log10 of Condition number in the moments')