1) changed directory structure;
2) added plots for prior/likelihood/posterior int he filtered samples 3) added plots of densities for conflicts git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@738 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
ratto
parent
fe5af81556
commit
01754a4446
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@ -1,4 +1,4 @@
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function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, istart, alphaPC)
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function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, OutDir, istart, alphaPC)
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% copyright Marco Ratto 2006
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global bayestopt_ estim_params_ M_ options_ oo_
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@ -17,10 +17,10 @@ end
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if nargin<5 | isempty(alpha2),
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alpha2=0.5;
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end
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if nargin<6 | isempty(istart),
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if nargin<7 | isempty(istart),
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istart=1;
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end
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if nargin<7,
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if nargin<8,
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alphaPC=0.5;
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end
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@ -34,20 +34,20 @@ disp('Starting sensitivity analysis')
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disp('for the fit of EACH observed series ...')
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disp(' ')
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disp('Deleting old SA figures...')
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a=dir('*.fig');
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a=dir([OutDir,'\*.*']);
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if options_.opt_gsa.ppost,
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tmp=['_SA_fit_post'];
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else
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if options_.opt_gsa.pprior
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tmp=['_SA_fit_prior'];
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else
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tmp=['_SA_fit_mc'];
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end
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tmp=['_SA_fit_prior'];
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else
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tmp=['_SA_fit_mc'];
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end
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end
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for j=1:length(a),
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if strmatch([fname_,tmp],a(j).name),
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disp(a(j).name)
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delete(a(j).name)
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delete([OutDir,'\',a(j).name])
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end,
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end
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disp('done !')
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@ -57,9 +57,19 @@ nshock=estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_
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npar=estim_params_.np;
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for j=1:npar+nshock,
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if j>nshock
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xparam1(j)=oo_.posterior_mode.parameters.(bayestopt_.name{j});
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if isfield(oo_,'posterior_mode'),
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xparam1(j)=oo_.posterior_mode.parameters.(bayestopt_.name{j});
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end
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if isfield(oo_,'posterior_mean'),
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xparam1_mean(j)=oo_.posterior_mean.parameters.(bayestopt_.name{j});
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end
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else
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xparam1(j)=oo_.posterior_mode.shocks_std.(bayestopt_.name{j});
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if isfield(oo_,'posterior_mode'),
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xparam1(j)=oo_.posterior_mode.shocks_std.(bayestopt_.name{j});
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end
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if isfield(oo_,'posterior_mean'),
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xparam1_mean(j)=oo_.posterior_mean.shocks_std.(bayestopt_.name{j});
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end
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end
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end
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@ -74,20 +84,37 @@ else
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end
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end
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if ~loadSA,
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set_all_parameters(xparam1);
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steady_;
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if exist('xparam1','var')
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set_all_parameters(xparam1);
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steady_;
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ys_mode=oo_.steady_state;
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end
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if exist('xparam1_mean','var')
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set_all_parameters(xparam1_mean);
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steady_;
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ys_mean=oo_.steady_state;
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end
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eval(options_.datafile)
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if ~options_.opt_gsa.ppost
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load(fnamtmp);
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load([OutDir,'\',fnamtmp]);
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else
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load([DirectoryName '/' M_.fname '_data.mat']);
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filfilt = dir([DirectoryName '/' M_.fname '_filter*.mat']);
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filparam = dir([DirectoryName '/' M_.fname '_param*.mat']);
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x=[];
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logpo2=[];
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sto_ys=[];
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for j=1:length(filparam),
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load([DirectoryName '/' M_.fname '_param1.mat']);
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x=[x; stock];
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clear stock;
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%load([DirectoryName '/' M_.fname '_param',int2str(j),'.mat']);
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if isempty(strmatch([M_.fname '_param_irf'],filparam(j).name))
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load([DirectoryName '/' filparam(j).name]);
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x=[x; stock];
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logpo2=[logpo2; stock_logpo];
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sto_ys=[sto_ys; stock_ys];
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clear stock stock_logpo stock_ys;
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end
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end
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logpo2=-logpo2;
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end
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nruns=size(x,1);
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nfilt=floor(pfilt*nruns);
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@ -103,16 +130,20 @@ if ~loadSA,
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jxj = strmatch(vj,lgy_(dr_.order_var,:),'exact');
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js = strmatch(vj,lgy_,'exact');
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eval(['rmse_mode(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-oo_.steady_state(js)-oo_.FilteredVariables.',vj,'(istart:end-1)).^2));'])
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if exist('xparam1','var')
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eval(['rmse_mode(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-oo_.steady_state(js)-oo_.FilteredVariables.',vj,'(istart:end-1)).^2));'])
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end
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y0=zeros(nobs+1,nruns);
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if options_.opt_gsa.ppost
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nbb=0;
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for j=1:length(filfilt),
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load([DirectoryName '/' M_.fname '_filter',num2str(j),'.mat']);
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nb = size(stock,4);
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y0(:,nbb+1:nbb+nb)=squeeze(stock(1,jxj,:,:));
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%y0(:,:,size(y0,3):size(y0,3)+size(stock,3))=stock;
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y0(:,nbb+1:nbb+nb)=squeeze(stock(1,jxj,:,:)); + ...
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kron(sto_ys(nbb+1:nbb+nb,js)',ones(size(stock,3),1));
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%y0(:,:,size(y0,3):size(y0,3)+size(stock,3))=stock;
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nbb=nbb+nb;
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clear stock;
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end
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else
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y0 = squeeze(stock_filter(:,jxj,:)) + ...
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@ -122,36 +153,55 @@ if ~loadSA,
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for j=1:nruns,
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eval(['rmse_MC(j,i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0(istart:end-1,j)).^2));'])
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end
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eval(['rmse_pmean(j,i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0M(istart:end-1)).^2));'])
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if exist('xparam1_mean','var')
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%eval(['rmse_pmean(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0M(istart:end-1)).^2));'])
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[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1_mean,stock_gend,stock_data);
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y0 = ahat(jxj,:)' + ...
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kron(ys_mean(js,:),ones(size(ahat,2),1));
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eval(['rmse_pmean(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0(istart:end-1)).^2));'])
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end
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end
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clear stock_filter;
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for j=1:nruns,
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lnprior(j,1) = priordens(x(j,:),bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
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end
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likelihood=logpo2(:)+lnprior(:);
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disp('... done!')
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if options_.opt_gsa.ppost
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save(fnamtmp, 'x', 'rmse_MC', 'rmse_mode')
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save([OutDir,'\',fnamtmp], 'x', 'logpo2', 'likelihood', 'rmse_MC', 'rmse_mode','rmse_pmean')
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else
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save(fnamtmp, 'rmse_MC', 'rmse_mode','-append')
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save([OutDir,'\',fnamtmp], 'likelihood', 'rmse_MC', 'rmse_mode','rmse_pmean','-append')
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end
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else
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tmp=load(fnamtmp);
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x=tmp.x;
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logpo2=tmp.logpo2;
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rmse_MC=tmp.rmse_MC;
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rmse_mode=tmp.rmse_mode;
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clear tmp
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load([OutDir,'\',fnamtmp],'x','logpo2','likelihood','rmse_MC','rmse_mode','rmse_pmean');
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lnprior=likelihood(:)-logpo2(:);
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nruns=size(x,1);
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nfilt=floor(pfilt*nruns);
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end
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% smirnov tests
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nfilt0=nfilt*ones(size(vvarvecm,1),1);
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logpo2=logpo2(:);
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if ~options_.opt_gsa.ppost
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[dum, ipost]=sort(logpo2);
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[dum, ipost]=sort(logpo2);
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end
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for i=1:size(vvarvecm,1),
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[dum, ixx(:,i)]=sort(rmse_MC(:,i));
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if options_.opt_gsa.ppost,
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%nfilt0(i)=length(find(rmse_MC(:,i)<rmse_pmean(i)));
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rmse_txt=rmse_pmean;
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else
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if options_.opt_gsa.pprior,
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rmse_txt=rmse_mode;
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else
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%nfilt0(i)=length(find(rmse_MC(:,i)<rmse_pmean(i)));
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rmse_txt=rmse_pmean;
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end
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end
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for j=1:npar+nshock,
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[H,P,KSSTAT] = smirnov(x(ixx(nfilt+1:end,i),j),x(ixx(1:nfilt,i),j), alpha);
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[H1,P1,KSSTAT1] = smirnov(x(ixx(nfilt+1:end,i),j),x(ixx(1:nfilt,i),j),alpha,1);
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[H2,P2,KSSTAT2] = smirnov(x(ixx(nfilt+1:end,i),j),x(ixx(1:nfilt,i),j),alpha,-1);
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[H,P,KSSTAT] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j), alpha);
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[H1,P1,KSSTAT1] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,1);
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[H2,P2,KSSTAT2] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,-1);
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if H1 & H2==0,
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SS(j,i)=1;
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elseif H1==0,
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@ -162,6 +212,85 @@ for i=1:size(vvarvecm,1),
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PP(j,i)=P;
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end
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end
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figure('name','prior')
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for i=1:size(vvarvecm,1),
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subplot(3,3,i)
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h=cumplot(lnprior(ixx(1:nfilt0(i),i)));
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set(h,'color','red')
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hold on, cumplot(lnprior)
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h=cumplot(lnprior(ixx(nfilt0(i)+1:end,i)));
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set(h,'color','green')
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title(vvarvecm(i,:))
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end
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if options_.opt_gsa.ppost
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_post_lnprior'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_post_lnprior']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_post_lnprior']);
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else
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if options_.opt_gsa.pprior
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_prior_lnprior'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_prior_lnprior']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_prior_lnprior']);
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else
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_mc_lnprior'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_mc_lnprior']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_mc_lnprior']);
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end
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end
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close(gcf)
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figure('name','likelihood')
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for i=1:size(vvarvecm,1),
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subplot(3,3,i)
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h=cumplot(likelihood(ixx(1:nfilt0(i),i)));
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set(h,'color','red')
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hold on, h=cumplot(likelihood);
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h=cumplot(likelihood(ixx(nfilt0(i)+1:end,i)));
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set(h,'color','green')
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title(vvarvecm(i,:))
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end
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if options_.opt_gsa.ppost
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_post_lnlik'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_post_lnlik']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_post_lnlik']);
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else
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if options_.opt_gsa.pprior
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_prior_lnlik'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_prior_lnlik']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_prior_lnlik']);
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else
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_mc_lnlik'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_mc_lnlik']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_mc_lnlik']);
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end
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end
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close(gcf)
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figure('name','posterior')
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for i=1:size(vvarvecm,1),
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subplot(3,3,i)
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h=cumplot(logpo2(ixx(1:nfilt0(i),i)));
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set(h,'color','red')
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hold on, h=cumplot(logpo2);
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h=cumplot(logpo2(ixx(nfilt0(i)+1:end,i)));
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set(h,'color','green')
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title(vvarvecm(i,:))
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end
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if options_.opt_gsa.ppost
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_post_lnpost'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_post_lnpost']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_post_lnpost']);
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else
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if options_.opt_gsa.pprior
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_prior_lnpost'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_prior_lnpost']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_prior_lnpost']);
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else
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saveas(gcf,[OutDir,'\',fname_,'_SA_fit_mc_lnpost'])
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eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_mc_lnpost']);
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eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_mc_lnpost']);
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end
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end
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close(gcf)
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param_names='';
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for j=1:npar+nshock,
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param_names=str2mat(param_names, bayestopt_.name{j});
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@ -188,8 +317,11 @@ vvarvecm=vvarvecm(ivar,:);
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rmse_MC=rmse_MC(:,ivar);
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disp(' ')
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disp(['Sample filtered the ',num2str(pfilt*100),'% best RMSE''s for each observed series ...' ])
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if options_.opt_gsa.ppost==0 & options_.opt_gsa.pprior,
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disp(['Sample filtered the ',num2str(pfilt*100),'% best RMSE''s for each observed series ...' ])
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else
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disp(['Sample filtered the best RMSE''s smaller than RMSE at the posterior mean ...' ])
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end
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% figure, boxplot(rmse_MC)
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% set(gca,'xticklabel',vvarvecm)
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% saveas(gcf,[fname_,'_SA_RMSE'])
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@ -197,14 +329,23 @@ disp(['Sample filtered the ',num2str(pfilt*100),'% best RMSE''s for each observe
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disp(' ')
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disp(' ')
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disp('RMSE ranges after filtering:')
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disp([' best ',num2str(pfilt*100),'% filtered remaining 90%'])
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disp([' min max min max posterior mode'])
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if options_.opt_gsa.ppost==0 & options_.opt_gsa.pprior,
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disp([' best ',num2str(pfilt*100),'% filtered remaining 90%'])
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disp([' min max min max posterior mode'])
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else
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disp([' best filtered remaining '])
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disp([' min max min max posterior mean'])
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end
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for j=1:size(vvarvecm,1),
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disp([vvarvecm(j,:), sprintf('%15.5g',[min(rmse_MC(ixx(1:nfilt,j),j)) ...
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max(rmse_MC(ixx(1:nfilt,j),j)) ...
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min(rmse_MC(ixx(nfilt+1:end,j),j)) ...
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max(rmse_MC(ixx(nfilt+1:end,j),j)) ...
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rmse_mode(j)])])
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disp([vvarvecm(j,:), sprintf('%15.5g',[min(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
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max(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
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min(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
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max(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
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rmse_txt(j)])])
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% disp([vvarvecm(j,:), sprintf('%15.5g',[min(logpo2(ixx(1:nfilt,j))) ...
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% max(logpo2(ixx(1:nfilt,j))) ...
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% min(logpo2(ixx(nfilt+1:end,j))) ...
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% max(logpo2(ixx(nfilt+1:end,j)))])])
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end
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%stab_map_1(x, ipost(1:nfilt), ipost(nfilt+1:end), 'SA_post', 1);
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@ -265,7 +406,7 @@ for ix=1:ceil(length(nsnam)/6),
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a0=a00(np,:);
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for i=1:nsp(nsnam(j)), %size(vvarvecm,1),
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%h0=cumplot(x(ixx(1:nfilt,np(i)),nsnam(j)+nshock));
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h0=cumplot(x(ixx(1:nfilt,np(i)),nsnam(j)));
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h0=cumplot(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)));
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set(h0,'color',a0(i,:))
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end
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ydum=get(gca,'ylim');
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@ -274,17 +415,24 @@ for ix=1:ceil(length(nsnam)/6),
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h1=plot([xdum xdum],ydum);
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set(h1,'color',[0.85 0.85 0.85],'linewidth',2)
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h0=legend(str2mat('base',vvarvecm(np,:)),0);
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set(h0,'fontsize',6)
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%h0=legend({'base',vnam{np}}',0);
|
||||
xlabel('')
|
||||
set(findobj(get(h0,'children'),'type','text'),'interpreter','none')
|
||||
title([pnam{nsnam(j)}],'interpreter','none')
|
||||
if options_.opt_gsa.ppost
|
||||
saveas(gcf,[fname_,'_SA_fit_post_',num2str(ix)])
|
||||
saveas(gcf,[OutDir,'\',fname_,'_SA_fit_post_',num2str(ix)])
|
||||
eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_post_' int2str(ix)]);
|
||||
eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_post_' int2str(ix)]);
|
||||
else
|
||||
if options_.opt_gsa.pprior
|
||||
saveas(gcf,[fname_,'_SA_fit_prior_',num2str(ix)])
|
||||
saveas(gcf,[OutDir,'\',fname_,'_SA_fit_prior_',num2str(ix)])
|
||||
eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_prior_' int2str(ix)]);
|
||||
eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_prior_' int2str(ix)]);
|
||||
else
|
||||
saveas(gcf,[fname_,'_SA_fit_mc_',num2str(ix)])
|
||||
saveas(gcf,[OutDir,'\',fname_,'_SA_fit_mc_',num2str(ix)])
|
||||
eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_mc_' int2str(ix)]);
|
||||
eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_mc_' int2str(ix)]);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
@ -300,6 +448,56 @@ bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
|
|||
kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton.
|
||||
%kernel_function = 'uniform'; % Gaussian kernel for Fast Fourrier Transform approximaton.
|
||||
|
||||
for ix=1:ceil(length(nsnam)/6),
|
||||
figure,
|
||||
for j=1+6*(ix-1):min(size(snam2,1),6*ix),
|
||||
subplot(2,3,j-6*(ix-1))
|
||||
optimal_bandwidth = mh_optimal_bandwidth(x(:,nsnam(j)),size(x,1),bandwidth,kernel_function);
|
||||
[x1,f1] = kernel_density_estimate(x(:,nsnam(j)),number_of_grid_points,...
|
||||
optimal_bandwidth,kernel_function);
|
||||
h0 = plot(x1, f1);
|
||||
hold on,
|
||||
np=find(SP(nsnam(j),:));
|
||||
%a0=jet(nsp(nsnam(j)));
|
||||
a0=a00(np,:);
|
||||
for i=1:nsp(nsnam(j)), %size(vvarvecm,1),
|
||||
optimal_bandwidth = mh_optimal_bandwidth(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)),nfilt,bandwidth,kernel_function);
|
||||
[x1,f1] = kernel_density_estimate(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)),number_of_grid_points,...
|
||||
optimal_bandwidth,kernel_function);
|
||||
h0 = plot(x1, f1);
|
||||
set(h0,'color',a0(i,:))
|
||||
end
|
||||
ydum=get(gca,'ylim');
|
||||
set(gca,'ylim',[0 ydum(2)]);
|
||||
%xdum=xparam1(nshock+nsnam(j));
|
||||
xdum=xparam1(nsnam(j));
|
||||
h1=plot([xdum xdum],[0 ydum(2)]);
|
||||
set(h1,'color',[0.85 0.85 0.85],'linewidth',2)
|
||||
h0=legend(str2mat('base',vvarvecm(np,:)),0);
|
||||
set(h0,'fontsize',6)
|
||||
%h0=legend({'base',vnam{np}}',0);
|
||||
xlabel('')
|
||||
set(findobj(get(h0,'children'),'type','text'),'interpreter','none')
|
||||
title([pnam{nsnam(j)}],'interpreter','none')
|
||||
if options_.opt_gsa.ppost
|
||||
saveas(gcf,[OutDir,'\',fname_,'_SA_fit_post_dens_',num2str(ix)])
|
||||
eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_post_dens_' int2str(ix)]);
|
||||
eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_post_dens_' int2str(ix)]);
|
||||
else
|
||||
if options_.opt_gsa.pprior
|
||||
saveas(gcf,[OutDir,'\',fname_,'_SA_fit_prior_dens_',num2str(ix)])
|
||||
eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_prior_dens_' int2str(ix)]);
|
||||
eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_prior_dens_' int2str(ix)]);
|
||||
else
|
||||
saveas(gcf,[OutDir,'\',fname_,'_SA_fit_mc_dens_',num2str(ix)])
|
||||
eval(['print -depsc2 ' OutDir '\' fname_ '_SA_fit_mc_dens_' int2str(ix)]);
|
||||
eval(['print -dpdf ' OutDir '\' fname_ '_SA_fit_mc_dens_' int2str(ix)]);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
close all
|
||||
|
||||
% for j=1:size(SP,2),
|
||||
% nfig=0;
|
||||
% np=find(SP(:,j));
|
||||
|
|
@ -368,13 +566,13 @@ for i=1:size(vvarvecm,1)
|
|||
if options_.opt_gsa.ppost
|
||||
fnam = ['SA_fit_post_',deblank(vvarvecm(i,:))];
|
||||
else
|
||||
if options_.opt_gsa.pprior
|
||||
fnam = ['SA_fit_prior_',deblank(vvarvecm(i,:))];
|
||||
else
|
||||
fnam = ['SA_fit_mc_',deblank(vvarvecm(i,:))];
|
||||
if options_.opt_gsa.pprior
|
||||
fnam = ['SA_fit_prior_',deblank(vvarvecm(i,:))];
|
||||
else
|
||||
fnam = ['SA_fit_mc_',deblank(vvarvecm(i,:))];
|
||||
end
|
||||
end
|
||||
end
|
||||
stab_map_2(x(ixx(1:nfilt,i),:),alpha2,fnam, 1);
|
||||
stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,fnam, OutDir);
|
||||
|
||||
% [pc,latent,explained] = pcacov(c0);
|
||||
% %figure, bar([explained cumsum(explained)])
|
||||
|
|
|
|||
Loading…
Reference in New Issue