Commit 869d0541 authored by ratto's avatar ratto
Browse files

New version with

-) more complete data saving on disk;
-) new output arguments;
-) new plots.

git-svn-id: https://www.dynare.org/svn/dynare/trunk@2995 ac1d8469-bf42-47a9-8791-bf33cf982152
parent 863dee7a
function [pdraws, idemodel, idemoments] = dynare_identification(iload)
function [pdraws, TAU, GAM0, H, JJ] = dynare_identification(iload, pdraws0)
% main
%
......@@ -29,15 +29,25 @@ options_ = set_default_option(options_,'datafile',[]);
options_.mode_compute = 0;
[data,rawdata]=dynare_estimation_init([],1);
% computes a first linear solution to set up various variables
dynare_resolve;
if nargin==2,
options_.prior_mc=size(pdraws0,1);
else
options_.prior_mc=2000;
end
SampleSize = options_.prior_mc;
% results = prior_sampler(0,M_,bayestopt_,options_,oo_);
prior_draw(1,bayestopt_);
if ~(exist('sylvester3mr','file')==2),
dynareroot = strrep(which('dynare'),'dynare.m','');
addpath([dynareroot 'gensylv'])
end
IdentifDirectoryName = CheckPath('identification');
indx = estim_params_.param_vals(:,1);
......@@ -48,47 +58,120 @@ end
useautocorr = 1;
nlags = 3;
nparam = length(bayestopt_.name);
options_.ar=nlags;
if iload ==0,
MaxNumberOfBytes=options_.MaxNumberOfBytes;
if iload <=0,
iteration = 0;
burnin_iteration = 0;
loop_indx = 0;
file_index = 0;
run_index = 0;
h = waitbar(0,'Monte Carlo identification checks ...');
while iteration < SampleSize,
loop_indx = loop_indx+1;
params = prior_draw();
if nargin==2 & burnin_iteration>=50,
params = pdraws0(iteration+1,:);
else
params = prior_draw();
end
set_all_parameters(params);
[A,B,ys,info]=dynare_resolve;
if info(1)==0,
iteration = iteration + 1;
oo0=oo_;
tau=[vec(A); vech(B*M_.Sigma_e*B')];
[JJ, H, GAM] = getJJ(A, B, M_,oo_,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
siJ = abs(JJ(find(GAM),:).*(1./GAM(find(GAM))*params));
siH = abs(H(find(abs(tau)>1.e-10),:).*(1./tau(find(abs(tau)>1.e-10))*params));
stock_params(iteration,:) = params;
if iteration ==1,
siJmean = siJ./SampleSize;
siHmean = siH./SampleSize;
if burnin_iteration<50,
burnin_iteration = burnin_iteration + 1;
TAU(:,burnin_iteration)=tau;
[gam,stationary_vars] = th_autocovariances(oo0.dr,bayestopt_.mfys,M_,options_);
sdy = sqrt(diag(gam{1}));
sy = sdy*sdy';
if useautocorr,
sy=sy-diag(diag(sy))+eye(length(sy));
gam{1}=gam{1}./sy;
else
for j=1:nlags,
gam{j+1}=gam{j+1}.*sy;
end
end
dum = vech(gam{1});
for j=1:nlags,
dum = [dum; vec(gam{j+1})];
end
GAM(:,burnin_iteration)=dum;
else
siJmean = siJ./SampleSize+siJmean;
siHmean = siH./SampleSize+siHmean;
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));
TAU = zeros(length(indH),SampleSize);
GAM = zeros(length(indJJ),SampleSize);
MAX_tau = min(SampleSize,ceil(MaxNumberOfBytes/(length(indH)*nparam)/8));
MAX_gam = min(SampleSize,ceil(MaxNumberOfBytes/(length(indJJ)*nparam)/8));
stoH = zeros([length(indH),nparam,MAX_tau]);
stoJJ = zeros([length(indJJ),nparam,MAX_tau]);
end
end
pdraws(iteration,:) = params';
[idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), ...
idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), ...
idemodel.cond(iteration), idemoments.cond(iteration), ...
idemodel.ee(:,iteration), idemoments.ee(:,iteration), ...
idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
idemodel.indno{iteration}, idemoments.indno{iteration}] = ...
identification_checks(H,JJ, bayestopt_);
if iteration,
TAU(:,iteration)=tau(indH);
[JJ, H, gam] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
GAM(:,iteration)=gam(indJJ);
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));
% use prior uncertainty
siJ = abs(JJ(indJJ,:));
siH = abs(H(indH,:));
% siJ = abs(JJ(indJJ,:).*(ones(length(indJJ),1)*bayestopt_.p2'));
% siH = abs(H(indH,:).*(ones(length(indH),1)*bayestopt_.p2'));
% siJ = abs(JJ(indJJ,:).*(1./mGAM'*bayestopt_.p2'));
% siH = abs(H(indH,:).*(1./mTAU'*bayestopt_.p2'));
if iteration ==1,
siJmean = siJ./SampleSize;
siHmean = siH./SampleSize;
else
siJmean = siJ./SampleSize+siJmean;
siHmean = siH./SampleSize+siHmean;
end
pdraws(iteration,:) = params;
[idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), ...
idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), ...
idemodel.cond(iteration), idemoments.cond(iteration), ...
idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), ...
idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
idemodel.indno{iteration}, idemoments.indno{iteration}] = ...
identification_checks(H(indH,:),JJ(indJJ,:), bayestopt_);
if run_index==MAX_tau | iteration==SampleSize,
file_index = file_index + 1;
if run_index<MAX_tau,
stoH = stoH(:,:,1:run_index);
stoJJ = stoJJ(:,:,1:run_index);
end
save([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ')
run_index = 0;
end
waitbar(iteration/SampleSize,h)
end
end
end
siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
siHmean = siHmean./(max(siHmean')'*ones(size(params)));
siJmean = siJmean./(max(siJmean')'*ones(size(params)));
......@@ -96,15 +179,126 @@ close(h)
save([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', ...
'siHmean', 'siJmean', 'stock_params')
'siHmean', 'siJmean', 'TAU', 'GAM')
else
load([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', ...
'siHmean', 'siJmean', 'stock_params')
'siHmean', 'siJmean', 'TAU', 'GAM')
end
if nargout>3 & iload,
filnam = dir([IdentifDirectoryName '/' M_.fname '_identif_*.mat']);
H=[];
JJ = [];
for j=1:length(filnam),
load([IdentifDirectoryName '/' M_.fname '_identif_',int2str(j),'.mat']);
H = cat(3,H, stoH(:,abs(iload),:));
JJ = cat(3,JJ, stoJJ(:,abs(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'*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'*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'*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'*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)
figure,
% myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
subplot(221)
bar(siHPCA)
% set(gca,'ylim',[0 1])
set(gca,'xticklabel','')
set(gca,'xlim',[0.5 nparam+0.5])
for ip=1:nparam,
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Sensitivity in TAU''s PCA')
subplot(222)
% myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
bar(siHPCA2)
% set(gca,'ylim',[0 1])
set(gca,'xticklabel','')
set(gca,'xlim',[0.5 nparam+0.5])
for ip=1:nparam,
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Sensitivity in standardized TAU''s PCA')
subplot(223)
% myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
bar(siPCA)
% set(gca,'ylim',[0 1])
set(gca,'xticklabel','')
set(gca,'xlim',[0.5 nparam+0.5])
for ip=1:nparam,
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Sensitivity in moments'' PCA')
subplot(224)
% myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
bar(siPCA2)
% set(gca,'ylim',[0 1])
set(gca,'xticklabel','')
set(gca,'xlim',[0.5 nparam+0.5])
for ip=1:nparam,
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
title('Sensitivity in standardized moments'' PCA')
figure,
subplot(221)
myboxplot(siHmean)
set(gca,'ylim',[0 1])
set(gca,'xticklabel','')
......@@ -113,7 +307,7 @@ for ip=1:nparam,
end
title('Sensitivity in the model')
figure,
subplot(222)
myboxplot(siJmean)
set(gca,'ylim',[0 1])
set(gca,'xticklabel','')
......@@ -122,7 +316,7 @@ for ip=1:nparam,
end
title('Sensitivity in the moments')
figure,
subplot(223)
myboxplot(idemodel.Mco')
set(gca,'ylim',[0 1])
set(gca,'xticklabel','')
......@@ -131,7 +325,7 @@ for ip=1:nparam,
end
title('Multicollinearity in the model')
figure,
subplot(224)
myboxplot(idemoments.Mco')
set(gca,'ylim',[0 1])
set(gca,'xticklabel','')
......
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