-) remove useless burn-in iterations;
-) removed obsolete commented lines or unused variables; -) when Asymptotic Hessian is available, take SVD of that for identification patterns; -) when computing analytic Hessian, scores are not necessary (no_DLIK=1); -) fixes in output info when Hessian is available.time-shift
Marco Ratto
parent
7c92b2308a
commit
6b71b6800d
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@ -151,12 +151,6 @@ disp(' ')
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if iload <=0,
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iteration = 0;
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burnin_iteration = 0;
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if SampleSize==1,
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BurninSampleSize=0;
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else
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BurninSampleSize=50;
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end
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loop_indx = 0;
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file_index = 0;
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run_index = 0;
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@ -186,11 +180,7 @@ if iload <=0,
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end
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else
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if nargin==2,
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if burnin_iteration>=BurninSampleSize,
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params = pdraws0(iteration+1,:);
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else
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params = pdraws0(burnin_iteration+1,:);
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end
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params = pdraws0(iteration+1,:);
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else
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params = prior_draw();
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end
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@ -210,57 +200,15 @@ if iload <=0,
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oo_.exo_steady_state', M_.params, ...
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oo_.dr.ys, 1);
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if burnin_iteration<BurninSampleSize,
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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)=[oo_.dr.ys(oo_.dr.order_var); vec(g1)];
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[gam,stationary_vars] = th_autocovariances(oo0.dr,bayestopt_.mfys,M_,options_);
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if exist('OCTAVE_VERSION')
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warning('off')
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else
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warning off,
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end
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sdy = sqrt(diag(gam{1}));
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sy = sdy*sdy';
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if useautocorr,
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sy=sy-diag(diag(sy))+eye(length(sy));
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gam{1}=gam{1}./sy;
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else
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for j=1:nlags,
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gam{j+1}=gam{j+1}.*sy;
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end
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end
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dum = dyn_vech(gam{1});
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for j=1:nlags,
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dum = [dum; vec(gam{j+1})];
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end
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GAM(:,burnin_iteration)=[oo_.dr.ys(bayestopt_.mfys); dum];
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% warning warning_old_state;
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else
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iteration = iteration + 1;
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run_index = run_index + 1;
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if iteration==1 && BurninSampleSize,
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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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stoJJ = zeros([length(indJJ),nparam,MAX_tau]);
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delete([IdentifDirectoryName '/' M_.fname '_identif_*.mat'])
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end
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end
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iteration = iteration + 1;
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run_index = run_index + 1;
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if iteration,
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[JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
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derivatives_info.DT=dA;
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derivatives_info.DOm=dOm;
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derivatives_info.DYss=dYss;
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if BurninSampleSize == 0,
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if iteration==1,
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indJJ = (find(max(abs(JJ'))>1.e-8));
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indH = (find(max(abs(H'))>1.e-8));
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indLRE = (find(max(abs(gp'))>1.e-8));
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@ -280,44 +228,15 @@ if iload <=0,
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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 = (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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siJnorm(iteration,:) = vnorm(siJ./repmat(GAM(:,iteration),1,nparam)).*params;
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siHnorm(iteration,:) = vnorm(siH./repmat(TAU(:,iteration),1,nparam)).*params;
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siLREnorm(iteration,:) = vnorm(siLRE./repmat(LRE(:,iteration),1,nparam-offset)).*params(offset+1:end);
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if iteration ==1,
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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 = 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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normH = max(abs(stoH(:,:,run_index))')';
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normJ = max(abs(stoJJ(:,:,run_index))')';
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normLRE = max(abs(stoLRE(:,:,run_index))')';
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% normH = TAU(:,iteration);
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% normJ = GAM(:,iteration);
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% normLRE = LRE(:,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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@ -360,7 +279,8 @@ if iload <=0,
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options_.periods = data_info.gend+100;
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info = stoch_simul(options_.varobs);
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datax=oo_.endo_simul(options_.varobs_id,100+1:end);
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% datax=data;
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% datax=data;
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derivatives_info.no_DLIK=1;
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[fval,cost_flag,ys,trend_coeff,info,DLIK,AHess] = DsgeLikelihood(params',data_info.gend,datax,data_info.data_index,data_info.number_of_observations,data_info.no_more_missing_observations,derivatives_info);
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cparam = inv(-AHess);
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normaliz(indok) = sqrt(diag(cparam))';
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@ -380,34 +300,19 @@ if iload <=0,
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end
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ide_strength_J(indok) = (1./(normaliz(indok)'./abs(params(indok)')));
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ide_strength_J_prior(indok) = (1./(normaliz(indok)'./normaliz1(indok)'));
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% indok = find(max(idemodel.indno{iteration},[],1)==0);
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% ide_strength_H(iteration,:)=zeros(1,nparam);
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% mim=inv(siH(:,indok)'*siH(:,indok))*siH(:,indok)';
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% % mim=mim*diag(GAM(:,iteration))*mim';
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% % MIM=inv(mim);
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% mim=mim.*repmat(TAU(:,iteration),1,length(indok))';
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% MIM=inv(mim*mim');
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% deltaM = sqrt(diag(MIM));
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% tildaM = MIM./((deltaM)*(deltaM'));
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% rhoM=sqrt(1-1./diag(inv(tildaM)));
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% deltaM = deltaM.*params(indok)';
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% ide_strength_H(iteration,indok) = (1./[sqrt(diag(inv(MIM)))./params(indok)']);
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% inok = find((abs(GAM(:,iteration))==0));
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% isok = find((abs(GAM(:,iteration))));
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% quant(isok,:) = siJ(isok,:)./repmat(GAM(isok,iteration),1,nparam);
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% quant(inok,:) = siJ(inok,:)./repmat(mean(abs(GAM(:,iteration))),length(inok),nparam);
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ide_strength_J(params==0)=ide_strength_J_prior(params==0);
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quant = siJ./repmat(sqrt(diag(cmm)),1,nparam);
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siJnorm(iteration,:) = vnorm(quant).*normaliz;
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% siJnorm(iteration,:) = vnorm(siJ(inok,:)).*normaliz;
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quant=[];
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inok = find((abs(TAU(:,iteration))==0));
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inok = find((abs(TAU(:,iteration))<1.e-8));
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isok = find((abs(TAU(:,iteration))));
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quant(isok,:) = siH(isok,:)./repmat(TAU(isok,iteration),1,nparam);
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quant(inok,:) = siH(inok,:)./repmat(mean(abs(TAU(:,iteration))),length(inok),nparam);
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siHnorm(iteration,:) = vnorm(quant).*normaliz;
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% siHnorm(iteration,:) = vnorm(siH./repmat(TAU(:,iteration),1,nparam)).*normaliz;
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quant=[];
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inok = find((abs(LRE(:,iteration))==0));
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inok = find((abs(LRE(:,iteration))<1.e-8));
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isok = find((abs(LRE(:,iteration))));
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quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,iteration),1,np);
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quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE(:,iteration))),length(inok),np);
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@ -454,107 +359,16 @@ if iload <=0,
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end
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save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
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'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
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'siJnorm', 'siHnorm', 'siLREnorm', 'TAU', 'GAM', 'LRE')
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else
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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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'siJnorm', 'siHnorm', 'siLREnorm', 'TAU', 'GAM', 'LRE')
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identFiles = dir([IdentifDirectoryName '/' M_.fname '_identif_*']);
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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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if iload>1,
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idemodel0=idemodel;
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idemoments0=idemoments;
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idelre0 = idelre;
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iteration = 0;
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h = waitbar(0,'Monte Carlo identification checks ...');
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for file_index=1:length(identFiles)
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load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
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for index=1:size(stoH,3),
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iteration = iteration+1;
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normH = max(abs(stoH(:,:,index))')';
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normJ = max(abs(stoJJ(:,:,index))')';
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normLRE = max(abs(stoLRE(:,:,index))')';
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% normH = TAU(:,iteration);
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% normJ = GAM(:,iteration);
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% normLRE = LRE(:,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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idemodel.ino(iteration), idemoments.ino(iteration)] = ...
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identification_checks(stoH(:,:,index)./normH(:,ones(nparam,1)), ...
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stoJJ(:,:,index)./normJ(:,ones(nparam,1)), ...
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stoLRE(:,:,index)./normLRE(:,ones(size(stoLRE,2),1)));
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waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
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end
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end
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close(h);
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save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idemodel', 'idemoments', 'idelre', '-append')
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end
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iteration = 0;
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h = waitbar(0,'Monte Carlo identification checks ...');
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for file_index=1:length(identFiles)
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load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ', 'stoLRE')
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for index=1:size(stoH,3),
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iteration = iteration+1;
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fobj(iteration)=sum(((GAM(:,iteration)-GAM(:,1))).^2);
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fobjH(iteration)=sum(((TAU(:,iteration)-TAU(:,1))).^2);
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fobjR(iteration)=sum(((GAM(:,iteration)./GAM(:,1)-1)).^2);
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fobjHR(iteration)=sum(((TAU(:,iteration)./TAU(:,1)-1)).^2);
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FOC(iteration,:) = (GAM(:,iteration)-GAM(:,1))'*stoJJ(:,:,index);
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FOCH(iteration,:) = (TAU(:,iteration)-TAU(:,1))'*stoH(:,:,index);
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FOCR(iteration,:) = ((GAM(:,iteration)./GAM(:,1)-1)./GAM(:,1))'*stoJJ(:,:,index);
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FOCHR(iteration,:) = ((TAU(:,iteration)./TAU(:,1)-1)./TAU(:,1))'*stoH(:,:,index);
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waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)])
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end
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end
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close(h);
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end
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% if SampleSize>1,
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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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%
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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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%
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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,np)));
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%
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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,np));
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%
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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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%
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% if exist('OCTAVE_VERSION')
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% warning('off'),
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% else
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% warning off,
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% end
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%
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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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% 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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@ -573,21 +387,9 @@ disp_identification(pdraws, idemodel, idemoments, name, advanced)
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figure('Name','Identification in the moments'),
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subplot(211)
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% mmm = mean(siHmean);
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% [ss, is] = sort(mmm);
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% myboxplot(siHmean(:,is))
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% if SampleSize>1,
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% mmm = mean(ide_strength_J);
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% else
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mmm = (ide_strength_J);
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% end
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mmm = (ide_strength_J);
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[ss, is] = sort(mmm);
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% if SampleSize>1,
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% myboxplot(log(ide_strength_J(:,is)))
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% else
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bar(log([ide_strength_J(:,is)' ide_strength_J_prior(:,is)']))
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% end
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% set(gca,'ylim',[0 1.05])
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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@ -596,30 +398,21 @@ for ip=1:nparam,
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end
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legend('relative to param value','relative to prior std','Location','Best')
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if flag_score,
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title('Identification strength using likelihood scores (log-scale)')
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title('Identification strength using asymptotic Information matrix (log-scale)')
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else
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title('Identification strength in the moments (log-scale)')
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end
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subplot(212)
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% mmm = mean(siJmean);
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% [ss, is] = sort(mmm);
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% myboxplot(siJmean(:,is))
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if SampleSize > 1,
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mmm = mean(siJnorm);
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else
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mmm = (siJnorm);
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end
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% [ss, is] = sort(mmm);
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% if SampleSize > 1,
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% myboxplot(log(siJnorm(:,is)))
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% else
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bar(mmm(is))
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% end
|
||||
% set(gca,'ylim',[0 1.05])
|
||||
bar(mmm(is))
|
||||
set(gca,'xlim',[0 nparam+1])
|
||||
set(gca,'xticklabel','')
|
||||
dy = get(gca,'ylim');
|
||||
% dy=dy(2)-dy(1);
|
||||
for ip=1:nparam,
|
||||
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
|
|
@ -634,43 +427,22 @@ if advanced,
|
|||
mmm = (siLREnorm);
|
||||
end
|
||||
mmm = [NaN(1, offset), mmm];
|
||||
% [ss, is] = sort(mmm);
|
||||
% if SampleSize ==1,
|
||||
bar(mmm(is))
|
||||
% else
|
||||
% myboxplot(log(siLREnorm(:,is)))
|
||||
% end
|
||||
% mmm = mean(siLREmean);
|
||||
% [ss, is] = sort(mmm);
|
||||
% myboxplot(siLREmean(:,is))
|
||||
% set(gca,'ylim',[0 1.05])
|
||||
bar(mmm(is))
|
||||
set(gca,'xticklabel','')
|
||||
% for ip=1:np,
|
||||
% text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
for ip=1:nparam,
|
||||
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
title('Sensitivity in the LRE model')
|
||||
|
||||
subplot(212)
|
||||
% mmm = mean(siHmean);
|
||||
% [ss, is] = sort(mmm);
|
||||
% myboxplot(siHmean(:,is))
|
||||
if SampleSize>1,
|
||||
mmm = mean(siHnorm);
|
||||
else
|
||||
mmm = (siHnorm);
|
||||
end
|
||||
% [ss, is] = sort(mmm);
|
||||
% if SampleSize>1,
|
||||
% myboxplot(log(siHnorm(:,is)))
|
||||
% else
|
||||
bar(mmm(is))
|
||||
% end
|
||||
% set(gca,'ylim',[0 1.05])
|
||||
bar(mmm(is))
|
||||
set(gca,'xticklabel','')
|
||||
dy = get(gca,'ylim');
|
||||
% dy=dy(2)-dy(1);
|
||||
for ip=1:nparam,
|
||||
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
|
||||
end
|
||||
|
|
@ -704,18 +476,34 @@ if advanced,
|
|||
ax=colormap;
|
||||
ax(1,:)=[0.9 0.9 0.9];
|
||||
colormap(ax);
|
||||
if nparam>10,
|
||||
set(gca,'xtick',(5:5:nparam))
|
||||
set(gca,'ytick',(5:5:nparam))
|
||||
end
|
||||
set(gca,'xgrid','on')
|
||||
set(gca,'ygrid','on')
|
||||
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_collinearity_', int2str(j)])
|
||||
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
|
||||
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
|
||||
if options_.nograph, close(gcf); end
|
||||
end
|
||||
disp('')
|
||||
[U,S,V]=svd(siJ./normJ(:,ones(nparam,1)),0);
|
||||
if nparam<5,
|
||||
f1 = figure('name','Identification patterns (moments)');
|
||||
if flag_score,
|
||||
[U,S,V]=svd(AHess,0);
|
||||
if nparam<5,
|
||||
f1 = figure('name','Identification patterns (Information matrix)');
|
||||
else
|
||||
f1 = figure('name','Identification patterns (Information matrix): SMALLEST SV');
|
||||
f2 = figure('name','Identification patterns (Information matrix): HIGHEST SV');
|
||||
end
|
||||
else
|
||||
f1 = figure('name','Identification patterns (moments): SMALLEST SV');
|
||||
f2 = figure('name','Identification patterns (moments): HIGHEST SV');
|
||||
[U,S,V]=svd(siJ./normJ(:,ones(nparam,1)),0);
|
||||
if nparam<5,
|
||||
f1 = figure('name','Identification patterns (moments)');
|
||||
else
|
||||
f1 = figure('name','Identification patterns (moments): SMALLEST SV');
|
||||
f2 = figure('name','Identification patterns (moments): HIGHEST SV');
|
||||
end
|
||||
end
|
||||
for j=1:min(nparam,8),
|
||||
if j<5,
|
||||
|
|
@ -729,15 +517,9 @@ if advanced,
|
|||
if j<5
|
||||
bar(abs(V(:,end-j+1))),
|
||||
Stit = S(end-j+1,end-j+1);
|
||||
% if j==4 || j==nparam,
|
||||
% xlabel('SMALLEST singular values'),
|
||||
% end,
|
||||
else
|
||||
bar(abs(V(:,jj))),
|
||||
Stit = S(jj,jj);
|
||||
% if j==8 || j==nparam,
|
||||
% xlabel('LARGEST singular values'),
|
||||
% end,
|
||||
end
|
||||
set(gca,'xticklabel','')
|
||||
if j==4 || j==nparam || j==8,
|
||||
|
|
|
|||
Loading…
Reference in New Issue