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-bf33cf982152time-shift
ratto
parent
863dee7acd
commit
869d054174
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@ -1,4 +1,4 @@
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function [pdraws, idemodel, idemoments] = dynare_identification(iload)
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function [pdraws, TAU, GAM0, H, JJ] = dynare_identification(iload, pdraws0)
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% main
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%
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@ -29,15 +29,25 @@ options_ = set_default_option(options_,'datafile',[]);
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options_.mode_compute = 0;
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[data,rawdata]=dynare_estimation_init([],1);
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% computes a first linear solution to set up various variables
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dynare_resolve;
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if nargin==2,
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options_.prior_mc=size(pdraws0,1);
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else
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options_.prior_mc=2000;
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end
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SampleSize = options_.prior_mc;
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% results = prior_sampler(0,M_,bayestopt_,options_,oo_);
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prior_draw(1,bayestopt_);
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if ~(exist('sylvester3mr','file')==2),
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dynareroot = strrep(which('dynare'),'dynare.m','');
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addpath([dynareroot 'gensylv'])
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end
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IdentifDirectoryName = CheckPath('identification');
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indx = estim_params_.param_vals(:,1);
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@ -48,47 +58,120 @@ end
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useautocorr = 1;
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nlags = 3;
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nparam = length(bayestopt_.name);
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options_.ar=nlags;
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if iload ==0,
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MaxNumberOfBytes=options_.MaxNumberOfBytes;
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if iload <=0,
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iteration = 0;
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burnin_iteration = 0;
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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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h = waitbar(0,'Monte Carlo identification checks ...');
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while iteration < SampleSize,
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loop_indx = loop_indx+1;
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params = prior_draw();
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if nargin==2 & burnin_iteration>=50,
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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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set_all_parameters(params);
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[A,B,ys,info]=dynare_resolve;
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if info(1)==0,
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iteration = iteration + 1;
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oo0=oo_;
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tau=[vec(A); vech(B*M_.Sigma_e*B')];
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[JJ, H, GAM] = getJJ(A, B, M_,oo_,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
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siJ = abs(JJ(find(GAM),:).*(1./GAM(find(GAM))*params));
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siH = abs(H(find(abs(tau)>1.e-10),:).*(1./tau(find(abs(tau)>1.e-10))*params));
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stock_params(iteration,:) = params;
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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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if burnin_iteration<50,
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burnin_iteration = burnin_iteration + 1;
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TAU(:,burnin_iteration)=tau;
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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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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 = 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)=dum;
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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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iteration = iteration + 1;
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run_index = run_index + 1;
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if iteration==1,
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indJJ = (find(std(GAM')>1.e-10));
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indH = (find(std(TAU')>1.e-10));
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TAU = zeros(length(indH),SampleSize);
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GAM = zeros(length(indJJ),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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end
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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.ind(:,iteration), idemoments.ind(:,iteration), ...
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idemodel.indno{iteration}, idemoments.indno{iteration}] = ...
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identification_checks(H,JJ, bayestopt_);
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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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GAM(:,iteration)=gam(indJJ);
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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 = 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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else
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siJmean = siJ./SampleSize+siJmean;
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siHmean = siH./SampleSize+siHmean;
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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.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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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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end
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save([IdentifDirectoryName '/' M_.fname '_identif_' int2str(file_index)], 'stoH', 'stoJJ')
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run_index = 0;
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end
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waitbar(iteration/SampleSize,h)
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end
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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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@ -96,15 +179,126 @@ close(h)
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save([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', ...
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'siHmean', 'siJmean', 'stock_params')
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'siHmean', 'siJmean', 'TAU', 'GAM')
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else
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load([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments', ...
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'siHmean', 'siJmean', 'stock_params')
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'siHmean', 'siJmean', 'TAU', 'GAM')
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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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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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end
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end
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mTAU = mean(TAU');
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mGAM = mean(GAM');
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sTAU = std(TAU');
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sGAM = std(GAM');
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if nargout>=3,
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GAM0=GAM;
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end
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if useautocorr,
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idiag = find(vech(eye(size(options_.varobs,1))));
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GAM(idiag,:) = GAM(idiag,:)./(sGAM(idiag)'*ones(1,SampleSize));
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% siJmean(idiag,:) = siJmean(idiag,:)./(sGAM(idiag)'*ones(1,nparam));
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% siJmean = siJmean./(max(siJmean')'*ones(size(params)));
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end
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[pcc, dd] = eig(cov(GAM'));
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[latent, isort] = sort(-diag(dd));
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latent = -latent;
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pcc=pcc(:,isort);
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siPCA = (siJmean'*pcc').^2';
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siPCA = siPCA./(max(siPCA')'*ones(1,nparam)).*(latent*ones(1,nparam));
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siPCA = sum(siPCA,1);
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siPCA = siPCA./max(siPCA);
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[pcc, dd] = eig(corrcoef(GAM'));
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[latent, isort] = sort(-diag(dd));
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latent = -latent;
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pcc=pcc(:,isort);
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siPCA2 = (siJmean'*pcc').^2';
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siPCA2 = siPCA2./(max(siPCA2')'*ones(1,nparam)).*(latent*ones(1,nparam));
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siPCA2 = sum(siPCA2,1);
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siPCA2 = siPCA2./max(siPCA2);
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[pcc, dd] = eig(cov(TAU'));
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[latent, isort] = sort(-diag(dd));
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latent = -latent;
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pcc=pcc(:,isort);
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siHPCA = (siHmean'*pcc').^2';
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siHPCA = siHPCA./(max(siHPCA')'*ones(1,nparam)).*(latent*ones(1,nparam));
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siHPCA = sum(siHPCA,1);
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siHPCA = siHPCA./max(siHPCA);
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[pcc, dd] = eig(corrcoef(TAU'));
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[latent, isort] = sort(-diag(dd));
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latent = -latent;
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pcc=pcc(:,isort);
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siHPCA2 = (siHmean'*pcc').^2';
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siHPCA2 = siHPCA2./(max(siHPCA2')'*ones(1,nparam)).*(latent*ones(1,nparam));
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siHPCA2 = sum(siHPCA2,1);
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siHPCA2 = siHPCA2./max(siHPCA2);
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disp_identification(pdraws, idemodel, idemoments)
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figure,
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% myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
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subplot(221)
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bar(siHPCA)
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% set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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set(gca,'xlim',[0.5 nparam+0.5])
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for ip=1:nparam,
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text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title('Sensitivity in TAU''s PCA')
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subplot(222)
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% myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
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bar(siHPCA2)
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% set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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set(gca,'xlim',[0.5 nparam+0.5])
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for ip=1:nparam,
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text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title('Sensitivity in standardized TAU''s PCA')
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subplot(223)
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% myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
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bar(siPCA)
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% set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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set(gca,'xlim',[0.5 nparam+0.5])
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for ip=1:nparam,
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text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title('Sensitivity in moments'' PCA')
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subplot(224)
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% myboxplot(siPCA(1:(max(find(cumsum(latent)./length(indJJ)<0.99))+1),:))
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bar(siPCA2)
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% set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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set(gca,'xlim',[0.5 nparam+0.5])
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for ip=1:nparam,
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text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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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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myboxplot(siHmean)
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set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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@ -113,7 +307,7 @@ for ip=1:nparam,
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end
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title('Sensitivity in the model')
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figure,
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subplot(222)
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myboxplot(siJmean)
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set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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@ -122,7 +316,7 @@ for ip=1:nparam,
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end
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title('Sensitivity in the moments')
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figure,
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subplot(223)
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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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@ -131,7 +325,7 @@ for ip=1:nparam,
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end
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title('Multicollinearity in the model')
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figure,
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subplot(224)
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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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Reference in New Issue