Added first order moments
Added LRE analysis for trivial no-identification git-svn-id: https://www.dynare.org/svn/dynare/trunk@3360 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
ratto
parent
a018e231de
commit
cda0f571b4
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@ -1,4 +1,4 @@
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function [pdraws, TAU, GAM, H, JJ] = dynare_identification(options_ident, pdraws0)
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function [pdraws, TAU, GAM, LRE, gp, H, JJ] = dynare_identification(options_ident, pdraws0)
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% main
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%
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@ -77,6 +77,7 @@ if iload <=0,
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run_index = 0;
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h = waitbar(0,'Monte Carlo identification checks ...');
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[I,J]=find(M_.lead_lag_incidence');
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while iteration < SampleSize,
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loop_indx = loop_indx+1;
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@ -101,10 +102,14 @@ if iload <=0,
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% bayestopt_.restrict_aux, M_.exo_nbr);
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% tau=[vec(Aa); vech(Bb*M_.Sigma_e*Bb')];
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tau=[oo_.dr.ys(oo_.dr.order_var); vec(A); vech(B*M_.Sigma_e*B')];
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yy0=oo_.dr.ys(I);
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[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', M_.params,1);
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if burnin_iteration<50,
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burnin_iteration = burnin_iteration + 1;
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pdraws(burnin_iteration,:) = params;
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TAU(:,burnin_iteration)=tau;
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LRE(:,burnin_iteration)=vec(g1);
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[gam,stationary_vars] = th_autocovariances(oo0.dr,bayestopt_.mfys,M_,options_);
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sdy = sqrt(diag(gam{1}));
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sy = sdy*sdy';
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@ -127,8 +132,10 @@ if iload <=0,
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if iteration==1,
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indJJ = (find(std(GAM')>1.e-8));
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indH = (find(std(TAU')>1.e-8));
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indLRE = (find(std(LRE')>1.e-8));
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TAU = zeros(length(indH),SampleSize);
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GAM = zeros(length(indJJ),SampleSize);
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LRE = zeros(length(indLRE),SampleSize);
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MAX_tau = min(SampleSize,ceil(MaxNumberOfBytes/(length(indH)*nparam)/8));
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MAX_gam = min(SampleSize,ceil(MaxNumberOfBytes/(length(indJJ)*nparam)/8));
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stoH = zeros([length(indH),nparam,MAX_tau]);
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@ -139,43 +146,56 @@ if iload <=0,
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if iteration,
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TAU(:,iteration)=tau(indH);
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[JJ, H, gam] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
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vg1 = vec(g1);
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LRE(:,iteration)=vg1(indLRE);
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[JJ, H, gam, gp] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
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GAM(:,iteration)=gam(indJJ);
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stoLRE(:,:,run_index) = gp(indLRE,:);
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stoH(:,:,run_index) = H(indH,:);
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stoJJ(:,:,run_index) = JJ(indJJ,:);
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% use relative changes
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% siJ = abs(JJ(indJJ,:).*(1./gam(indJJ)*params));
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% siH = abs(H(indH,:).*(1./tau(indH)*params));
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% use prior uncertainty
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siJ = abs(JJ(indJJ,:));
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siH = abs(H(indH,:));
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siJ = (JJ(indJJ,:));
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siH = (H(indH,:));
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siLRE = (gp(indLRE,:));
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% siJ = abs(JJ(indJJ,:).*(ones(length(indJJ),1)*bayestopt_.p2'));
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% siH = abs(H(indH,:).*(ones(length(indH),1)*bayestopt_.p2'));
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% siJ = abs(JJ(indJJ,:).*(1./mGAM'*bayestopt_.p2'));
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% siH = abs(H(indH,:).*(1./mTAU'*bayestopt_.p2'));
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if iteration ==1,
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siJmean = siJ./SampleSize;
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siHmean = siH./SampleSize;
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siJmean = abs(siJ)./SampleSize;
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siHmean = abs(siH)./SampleSize;
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siLREmean = abs(siLRE)./SampleSize;
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derJmean = (siJ)./SampleSize;
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derHmean = (siH)./SampleSize;
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derLREmean = (siLRE)./SampleSize;
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else
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siJmean = siJ./SampleSize+siJmean;
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siHmean = siH./SampleSize+siHmean;
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siJmean = abs(siJ)./SampleSize+siJmean;
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siHmean = abs(siH)./SampleSize+siHmean;
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siLREmean = abs(siLRE)./SampleSize+siLREmean;
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derJmean = (siJ)./SampleSize+derJmean;
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derHmean = (siH)./SampleSize+derHmean;
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derLREmean = (siLRE)./SampleSize+derLREmean;
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end
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pdraws(iteration,:) = params;
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[idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), ...
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idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), ...
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idemodel.cond(iteration), idemoments.cond(iteration), ...
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idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), ...
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[idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), idelre.Mco(:,iteration), ...
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idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), idelre.Pco(:,:,iteration), ...
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idemodel.cond(iteration), idemoments.cond(iteration), idelre.cond(iteration), ...
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idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), idelre.ee(:,:,iteration), ...
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idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
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idemodel.indno{iteration}, idemoments.indno{iteration}] = ...
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identification_checks(H(indH,:),JJ(indJJ,:), bayestopt_);
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identification_checks(H(indH,:),JJ(indJJ,:), gp(indLRE,:), bayestopt_);
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if run_index==MAX_tau | iteration==SampleSize,
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file_index = file_index + 1;
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if run_index<MAX_tau,
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stoH = stoH(:,:,1:run_index);
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stoJJ = stoJJ(:,:,1:run_index);
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stoLRE = stoLRE(:,:,1:run_index);
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end
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save([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ')
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save([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
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run_index = 0;
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end
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@ -185,34 +205,66 @@ if iload <=0,
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end
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end
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siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
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siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
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siHmean = siHmean./(max(siHmean')'*ones(size(params)));
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siJmean = siJmean./(max(siJmean')'*ones(size(params)));
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close(h)
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save([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', ...
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'siHmean', 'siJmean', 'TAU', 'GAM')
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save([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
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'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
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else
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load([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', ...
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'siHmean', 'siJmean', 'TAU', 'GAM')
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options_ident.prior_mc=size(pdraws,1);
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SampleSize = options_ident.prior_mc;
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options_.options_ident = options_ident;
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load([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
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'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
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options_ident.prior_mc=size(pdraws,1);
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SampleSize = options_ident.prior_mc;
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options_.options_ident = options_ident;
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end
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offset = estim_params_.nvx;
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offset = offset + estim_params_.nvn;
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offset = offset + estim_params_.ncx;
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offset = offset + estim_params_.ncn;
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siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
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siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
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siLREmean = siLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
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derJmean = derJmean.*(ones(length(indJJ),1)*std(pdraws));
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derHmean = derHmean.*(ones(length(indH),1)*std(pdraws));
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derLREmean = derLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
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derHmean = abs(derHmean./(max(siHmean')'*ones(1,size(pdraws,2))));
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derJmean = abs(derJmean./(max(siJmean')'*ones(1,size(pdraws,2))));
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derLREmean = abs(derLREmean./(max(siLREmean')'*ones(1,estim_params_.np)));
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siHmean = siHmean./(max(siHmean')'*ones(1,size(pdraws,2)));
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siJmean = siJmean./(max(siJmean')'*ones(1,size(pdraws,2)));
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siLREmean = siLREmean./(max(siLREmean')'*ones(1,estim_params_.np));
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tstJmean = derJmean*0;
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tstHmean = derHmean*0;
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tstLREmean = derLREmean*0;
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for j=1:nparam,
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indd = 1:length(siJmean(:,j));
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tstJmean(indd,j) = abs(derJmean(indd,j))./siJmean(indd,j);
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indd = 1:length(siHmean(:,j));
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tstHmean(indd,j) = abs(derHmean(indd,j))./siHmean(indd,j);
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if j>offset
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indd = 1:length(siLREmean(:,j-offset));
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tstLREmean(indd,j-offset) = abs(derLREmean(indd,j-offset))./siLREmean(indd,j-offset);
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end
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end
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if nargout>3 & iload,
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filnam = dir([IdentifDirectoryName '/' M_.fname '_identif_*.mat']);
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H=[];
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JJ = [];
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gp = [];
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for j=1:length(filnam),
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load([IdentifDirectoryName '/' M_.fname '_identif_',int2str(j),'.mat']);
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H = cat(3,H, stoH(:,abs(iload),:));
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JJ = cat(3,JJ, stoJJ(:,abs(iload),:));
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gp = cat(3,gp, stoLRE(:,abs(iload),:));
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end
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end
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@ -317,7 +369,16 @@ disp_identification(pdraws, idemodel, idemoments)
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% title('Sensitivity in standardized moments'' PCA')
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figure,
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subplot(221)
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subplot(231)
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myboxplot(siLREmean)
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set(gca,'ylim',[0 1.05])
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set(gca,'xticklabel','')
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for ip=1:estim_params_.np,
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text(ip,-0.02,deblank(M_.param_names(estim_params_.param_vals(ip,1),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title('Sensitivity in the LRE model')
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subplot(232)
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myboxplot(siHmean)
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set(gca,'ylim',[0 1.05])
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set(gca,'xticklabel','')
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@ -326,7 +387,7 @@ for ip=1:nparam,
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end
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title('Sensitivity in the model')
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subplot(222)
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subplot(233)
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myboxplot(siJmean)
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set(gca,'ylim',[0 1.05])
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set(gca,'xticklabel','')
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@ -335,7 +396,16 @@ for ip=1:nparam,
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end
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title('Sensitivity in the moments')
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subplot(223)
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subplot(234)
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myboxplot(idelre.Mco')
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set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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for ip=1:estim_params_.np,
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text(ip,-0.02,deblank(M_.param_names(estim_params_.param_vals(ip,1),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title('Multicollinearity in the LRE model')
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subplot(235)
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myboxplot(idemodel.Mco')
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set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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@ -344,7 +414,7 @@ for ip=1:nparam,
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end
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title('Multicollinearity in the model')
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subplot(224)
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subplot(236)
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myboxplot(idemoments.Mco')
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set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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@ -364,6 +434,34 @@ title('log10 of Condition number in the model')
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subplot(222)
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hist(log10(idemoments.cond))
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title('log10 of Condition number in the moments')
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subplot(223)
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hist(log10(idelre.cond))
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title('log10 of Condition number in the LRE model')
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saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND'])
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eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
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eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
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ifig=0;
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nbox = min(estim_params_.np-1,12);
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for j=1:estim_params_.np,
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if mod(j,12)==1,
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ifig = ifig+1;
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figure('name','Partial correlations in the LRE model'),
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iplo=0;
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end
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iplo=iplo+1;
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mmm = mean(squeeze(idelre.Pco(:,j,:))');
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[sss, immm] = sort(-mmm);
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subplot(3,4,iplo),
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myboxplot(squeeze(idelre.Pco(immm(2:nbox+1),j,:))'),
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set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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for ip=1:nbox, %estim_params_.np,
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text(ip,-0.02,deblank(M_.param_names(estim_params_.param_vals(immm(ip+1),1),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title(deblank(M_.param_names(estim_params_.param_vals(j,1),:))),
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if j==estim_params_.np | mod(j,12)==0
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saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_LRE',int2str(ifig)])
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eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE',int2str(ifig)]);
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eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE',int2str(ifig)]);
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end
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end
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