From a5559bb0165ccf593293eea79831275f21f68cf6 Mon Sep 17 00:00:00 2001 From: Marco Ratto Date: Mon, 2 May 2011 11:15:25 +0200 Subject: [PATCH] eliminated obsolete commented lines; --- matlab/dynare_identification.m | 355 --------------------------------- 1 file changed, 355 deletions(-) diff --git a/matlab/dynare_identification.m b/matlab/dynare_identification.m index 34d9f1f7b..dacad92ba 100644 --- a/matlab/dynare_identification.m +++ b/matlab/dynare_identification.m @@ -304,154 +304,6 @@ if iload <=0, end end -% set_all_parameters(params); -% [A,B,ys,info]=dynare_resolve; -% -% -% if info(1)==0, -% oo0=oo_; -% tau=[oo_.dr.ys(oo_.dr.order_var); vec(A); dyn_vech(B*M_.Sigma_e*B')]; -% yy0=oo_.dr.ys(I); -% [residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, ... -% oo_.exo_steady_state', M_.params, ... -% oo_.dr.ys, 1); -% -% iteration = iteration + 1; -% run_index = run_index + 1; -% -% if iteration, -% [JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, M_,oo0,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr); -% derivatives_info.DT=dA; -% derivatives_info.DOm=dOm; -% derivatives_info.DYss=dYss; -% if iteration==1, -% indJJ = (find(max(abs(JJ'))>1.e-8)); -% indH = (find(max(abs(H'))>1.e-8)); -% indLRE = (find(max(abs(gp'))>1.e-8)); -% TAU = zeros(length(indH),SampleSize); -% GAM = zeros(length(indJJ),SampleSize); -% LRE = zeros(length(indLRE),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]); -% delete([IdentifDirectoryName '/' M_.fname '_identif_*.mat']) -% end -% TAU(:,iteration)=tau(indH); -% vg1 = [oo_.dr.ys(oo_.dr.order_var); vec(g1)]; -% LRE(:,iteration)=vg1(indLRE); -% GAM(:,iteration)=gam(indJJ); -% stoLRE(:,:,run_index) = gp(indLRE,:); -% stoH(:,:,run_index) = H(indH,:); -% stoJJ(:,:,run_index) = JJ(indJJ,:); -% % use prior uncertainty -% siJ = (JJ(indJJ,:)); -% siH = (H(indH,:)); -% -% siLRE = (gp(indLRE,:)); -% pdraws(iteration,:) = params; -% normH = max(abs(stoH(:,:,run_index))')'; -% normJ = max(abs(stoJJ(:,:,run_index))')'; -% normLRE = max(abs(stoLRE(:,:,run_index))')'; -% [idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), idelre.Mco(:,iteration), ... -% idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), idelre.Pco(:,:,iteration), ... -% idemodel.cond(iteration), idemoments.cond(iteration), idelre.cond(iteration), ... -% idemodel.ee(:,:,iteration), idemoments.ee(:,:,iteration), idelre.ee(:,:,iteration), ... -% idemodel.ind(:,iteration), idemoments.ind(:,iteration), ... -% idemodel.indno{iteration}, idemoments.indno{iteration}, ... -% idemodel.ino(iteration), idemoments.ino(iteration)] = ... -% identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1))); -% % identification_checks(H(indH,:),JJ(indJJ,:), gp(indLRE,:), bayestopt_); -% indok = find(max(idemoments.indno{iteration},[],1)==0); -% if iteration ==1, -% ide_strength_J=NaN(1,nparam); -% ide_strength_J_prior=NaN(1,nparam); -% end -% if iteration ==1 && advanced, -% [pars, cosnJ] = ident_bruteforce(JJ(indJJ,:)./normJ(:,ones(nparam,1)),max_dim_cova_group,options_.TeX,name_tex); -% end -% if iteration ==1 && ~isempty(indok), -% normaliz = abs(params); -% if prior_exist, -% if ~isempty(estim_params_.var_exo), -% normaliz1 = estim_params_.var_exo(:,7); % normalize with prior standard deviation -% else -% normaliz1=[]; -% end -% if ~isempty(estim_params_.param_vals), -% normaliz1 = [normaliz1; estim_params_.param_vals(:,7)]'; % normalize with prior standard deviation -% end -% % normaliz = max([normaliz; normaliz1]); -% normaliz1(isinf(normaliz1)) = 1; -% -% else -% normaliz1 = ones(1,nparam); -% end -% replic = max([replic, length(indJJ)*3]); -% try, -% options_.irf = 0; -% options_.noprint = 1; -% options_.order = 1; -% options_.periods = data_info.gend+100; -% info = stoch_simul(options_.varobs); -% datax=oo_.endo_simul(options_.varobs_id,100+1:end); -% % datax=data; -% derivatives_info.no_DLIK=1; -% [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); -% cparam = inv(-AHess); -% normaliz(indok) = sqrt(diag(cparam))'; -% cmm = siJ*((-AHess)\siJ'); -% flag_score=1; -% catch, -% cmm = simulated_moment_uncertainty(indJJ, periods, replic); -% % Jinv=(siJ(:,indok)'*siJ(:,indok))\siJ(:,indok)'; -% % MIM=inv(Jinv*cmm*Jinv'); -% MIM=siJ(:,indok)'*(cmm\siJ(:,indok)); -% deltaM = sqrt(diag(MIM)); -% tildaM = MIM./((deltaM)*(deltaM')); -% rhoM=sqrt(1-1./diag(inv(tildaM))); -% deltaM = deltaM.*normaliz(indok)'; -% normaliz(indok) = sqrt(diag(inv(MIM)))'; -% flag_score=0; -% end -% ide_strength_J(indok) = (1./(normaliz(indok)'./abs(params(indok)'))); -% ide_strength_J_prior(indok) = (1./(normaliz(indok)'./normaliz1(indok)')); -% ide_strength_J(params==0)=ide_strength_J_prior(params==0); -% quant = siJ./repmat(sqrt(diag(cmm)),1,nparam); -% siJnorm(iteration,:) = vnorm(quant).*normaliz; -% % siJnorm(iteration,:) = vnorm(siJ(inok,:)).*normaliz; -% quant=[]; -% inok = find((abs(TAU(:,iteration))<1.e-8)); -% isok = find((abs(TAU(:,iteration)))); -% quant(isok,:) = siH(isok,:)./repmat(TAU(isok,iteration),1,nparam); -% quant(inok,:) = siH(inok,:)./repmat(mean(abs(TAU(:,iteration))),length(inok),nparam); -% siHnorm(iteration,:) = vnorm(quant).*normaliz; -% % siHnorm(iteration,:) = vnorm(siH./repmat(TAU(:,iteration),1,nparam)).*normaliz; -% quant=[]; -% inok = find((abs(LRE(:,iteration))<1.e-8)); -% isok = find((abs(LRE(:,iteration)))); -% quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,iteration),1,np); -% quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE(:,iteration))),length(inok),np); -% siLREnorm(iteration,:) = vnorm(quant).*normaliz(offset+1:end); -% % siLREnorm(iteration,:) = vnorm(siLRE./repmat(LRE(:,iteration),1,nparam-offset)).*normaliz(offset+1:end); -% end, -% if run_index==MAX_tau || iteration==SampleSize, -% file_index = file_index + 1; -% if run_index 1, -% waitbar(iteration/SampleSize,h,['MC Identification checks ',int2str(iteration),'/',int2str(SampleSize)]) -% end -% end -% end end @@ -534,213 +386,6 @@ if SampleSize > 1, end end end -% if prior_exist, -% tittxt = 'Prior mean - '; -% else -% tittxt = ''; -% end -% figure('Name',[tittxt, 'Identification using info from observables']), -% subplot(211) -% mmm = (idehess_prior.ide_strength_J); -% [ss, is] = sort(mmm); -% bar(log([idehess_prior.ide_strength_J(:,is)' idehess_prior.ide_strength_J_prior(:,is)'])) -% set(gca,'xlim',[0 nparam+1]) -% set(gca,'xticklabel','') -% dy = get(gca,'ylim'); -% for ip=1:nparam, -% text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none') -% end -% legend('relative to param value','relative to prior std','Location','Best') -% if idehess_prior.flag_score, -% title('Identification strength in the asymptotic Information matrix (log-scale)') -% else -% title('Identification strength in the moments (log-scale)') -% end -% subplot(212) -% -% if SampleSize > 1, -% mmm = mean(siJnorm)'; -% mmm = [idemoments_prior.siJnorm'./max(idemoments_prior.siJnorm') mmm./max(mmm)]; -% else -% mmm = (siJnorm)'./max(siJnorm); -% end -% bar(mmm(is,:)) -% set(gca,'xlim',[0 nparam+1]) -% set(gca,'xticklabel','') -% dy = get(gca,'ylim'); -% for ip=1:nparam, -% text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none') -% end -% if SampleSize > 1, -% legend('Prior mean','MC average','Location','Best') -% end -% title('Sensitivity in the moments') -% -% if advanced, -% figure('Name','Identification in the model'), -% subplot(211) -% if SampleSize > 1, -% mmm = mean(siLREnorm); -% else -% mmm = (siLREnorm); -% end -% mmm = [NaN(1, offset), mmm]; -% bar(mmm(is)) -% set(gca,'xticklabel','') -% 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) -% if SampleSize>1, -% mmm = mean(siHnorm); -% else -% mmm = (siHnorm); -% end -% bar(mmm(is)) -% set(gca,'xticklabel','') -% dy = get(gca,'ylim'); -% for ip=1:nparam, -% text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none') -% end -% title('Sensitivity in the model') -% -% if options_.nograph, close(gcf); end -% -% % identificaton patterns -% for j=1:size(idemoments_prior.cosnJ,2), -% pax=NaN(nparam,nparam); -% fprintf('\n\n') -% disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)']) -% fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn') -% for i=1:nparam, -% namx=''; -% for in=1:j, -% dumpindx = idemoments_prior.pars{i,j}(in); -% if isnan(dumpindx), -% namx=[namx ' ' sprintf('%-15s','--')]; -% else -% namx=[namx ' ' sprintf('%-15s',name{dumpindx})]; -% pax(i,dumpindx)=idemoments_prior.cosnJ(i,j); -% end -% end -% fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments_prior.cosnJ(i,j)) -% end -% figure('name',['Collinearity patterns with ', int2str(j) ,' parameter(s)']), -% imagesc(pax,[0 1]); -% set(gca,'xticklabel','') -% set(gca,'yticklabel','') -% for ip=1:nparam, -% text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none') -% text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none') -% end -% colorbar; -% 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('') -% if idehess_prior.flag_score, -% [U,S,V]=svd(idehess_prior.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 -% [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, -% figure(f1), -% jj=j; -% else -% figure(f2), -% jj=j-4; -% end -% subplot(4,1,jj), -% if j<5 -% bar(abs(V(:,end-j+1))), -% Stit = S(end-j+1,end-j+1); -% else -% bar(abs(V(:,jj))), -% Stit = S(jj,jj); -% end -% set(gca,'xticklabel','') -% if j==4 || j==nparam || j==8, -% for ip=1:nparam, -% text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none') -% end -% end -% title(['Singular value ',num2str(Stit)]) -% end -% figure(f1); -% saveas(f1,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_1']) -% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']); -% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']); -% if nparam>4, -% figure(f2), -% saveas(f2,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_2']) -% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']); -% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']); -% end -% -% if SampleSize>1, -% options_.nograph=1; -% figure('Name','Condition Number'), -% 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') -% subplot(223) -% hist(log10(idelre.cond)) -% title('log10 of Condition number in the LRE model') -% saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND']) -% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']); -% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']); -% if options_.nograph, close(gcf); end -% ncut=floor(SampleSize/10*9); -% [~,is]=sort(idelre.cond); -% [proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberLRE', 1, [], IdentifDirectoryName); -% [~,is]=sort(idemodel.cond); -% [proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberModel', 1, [], IdentifDirectoryName); -% [~,is]=sort(idemoments.cond); -% [proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments', 1, [], IdentifDirectoryName); -% % [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName); -% for j=1:nparam, -% % ibeh=find(idemoments.Mco(j,:)<0.9); -% % inonbeh=find(idemoments.Mco(j,:)>=0.9); -% % if ~isempty(ibeh) && ~isempty(inonbeh) -% % [proba, dproba] = stab_map_1(pdraws, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName); -% % end -% [~,is]=sort(idemoments.Mco(:,j)); -% [proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName); -% end -% end -% -% -% end - if exist('OCTAVE_VERSION') warning('on'),