function [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_) % function [rmse_MC, ixx] = filt_mc_(OutDir) % inputs (from opt_gsa structure) % vvarvecm = options_gsa_.var_rmse; % loadSA = options_gsa_.load_rmse; % pfilt = options_gsa_.pfilt_rmse; % alpha = options_gsa_.alpha_rmse; % alpha2 = options_gsa_.alpha2_rmse; % istart = options_gsa_.istart_rmse; % alphaPC = 0.5; % % Written by Marco Ratto % Joint Research Centre, The European Commission, % (http://eemc.jrc.ec.europa.eu/), % marco.ratto@jrc.it % Reference: % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006. % Copyright (C) 2012 Dynare Team % % This file is part of Dynare. % % Dynare is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % Dynare is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with Dynare. If not, see . global bayestopt_ estim_params_ M_ options_ oo_ % options_gsa_=options_.opt_gsa; vvarvecm = options_gsa_.var_rmse; loadSA = options_gsa_.load_rmse; pfilt = options_gsa_.pfilt_rmse; alpha = options_gsa_.alpha_rmse; % alpha2 = options_gsa_.alpha2_rmse; alpha2 = 0; pvalue = options_gsa_.alpha2_rmse; istart = options_gsa_.istart_rmse; alphaPC = 0.5; fname_ = M_.fname; lgy_ = M_.endo_names; dr_ = oo_.dr; disp(' ') disp(' ') disp('Starting sensitivity analysis') disp('for the fit of EACH observed series ...') disp(' ') disp('Deleting old SA figures...') a=dir([OutDir,filesep,'*.*']); tmp1='0'; if options_.opt_gsa.ppost, tmp=['_rmse_post']; else if options_.opt_gsa.pprior tmp=['_rmse_prior']; else tmp=['_rmse_mc']; end if options_gsa_.lik_only, tmp1 = [tmp,'_post_SA']; tmp = [tmp,'_lik_SA']; end end for j=1:length(a), if strmatch([fname_,tmp],a(j).name), disp(a(j).name) delete([OutDir,filesep,a(j).name]) end, if strmatch([fname_,tmp1],a(j).name), disp(a(j).name) delete([OutDir,filesep,a(j).name]) end, end disp('done !') nshock=estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_.ncn; npar=estim_params_.np; if ~isempty(options_.mode_file), load(options_.mode_file,'xparam1'), end if options_.opt_gsa.ppost, c=load([fname_,'_mean.mat'],'xparam1'); xparam1_mean=c.xparam1; clear c elseif ~isempty(options_.mode_file) && exist([fname_,'_mean.mat'])==2, c=load([fname_,'_mean.mat'],'xparam1'); xparam1_mean=c.xparam1; clear c end if options_.opt_gsa.ppost, fnamtmp=[fname_,'_post']; DirectoryName = CheckPath('metropolis',M_.dname); else if options_.opt_gsa.pprior fnamtmp=[fname_,'_prior']; DirectoryName = CheckPath(['gsa' filesep 'prior'],M_.dname); else fnamtmp=[fname_,'_mc']; DirectoryName = CheckPath(['gsa' filesep 'mc'],M_.dname); end end if ~loadSA, if exist('xparam1','var') M_ = set_all_parameters(xparam1,estim_params_,M_); steady_(M_,options_,oo_); ys_mode=oo_.steady_state; end if exist('xparam1_mean','var') M_ = set_all_parameters(xparam1_mean,estim_params_,M_); steady_(M_,options_,oo_); ys_mean=oo_.steady_state; end % eval(options_.datafile) obs = dat_fil_(options_.datafile); %stock_gend=data_info.gend; %stock_data = data_info.data; load([DirectoryName filesep M_.fname '_data.mat']); filfilt = dir([DirectoryName filesep M_.fname '_filter_step_ahead*.mat']); filparam = dir([DirectoryName filesep M_.fname '_param*.mat']); x=[]; logpo2=[]; sto_ys=[]; for j=1:length(filparam), %load([DirectoryName filesep M_.fname '_param',int2str(j),'.mat']); if isempty(strmatch([M_.fname '_param_irf'],filparam(j).name)) load([DirectoryName filesep filparam(j).name]); x=[x; stock]; logpo2=[logpo2; stock_logpo]; sto_ys=[sto_ys; stock_ys]; clear stock stock_logpo stock_ys; end end nruns=size(x,1); nfilt=floor(pfilt*nruns); if options_.opt_gsa.ppost || (options_.opt_gsa.ppost==0 && options_.opt_gsa.lik_only==0) disp(' ') disp('Computing RMSE''s...') fobs = options_.first_obs; nobs=options_.nobs; for i=1:size(vvarvecm,1), vj=deblank(vvarvecm(i,:)); eval(['vobs =obs.',vj,'(fobs:fobs-1+nobs);']) if options_.prefilter == 1 %eval([vj,'=',vj,'-bayestopt_.mean_varobs(i);']) %eval([vj,'=',vj,'-mean(',vj,',1);']) vobs = vobs-mean(vobs,1); end jxj = strmatch(vj,lgy_(dr_.order_var,:),'exact'); js = strmatch(vj,lgy_,'exact'); if exist('xparam1','var') % if isfield(oo_,'FilteredVariables') % eval(['rmse_mode(i) = sqrt(mean((vobs(istart:end)-oo_.steady_state(js)-oo_.FilteredVariables.',vj,'(istart:end-1)).^2));']) % else [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,stock_gend,stock_data,{},0); y0 = squeeze(aK(1,jxj,:)) + ... kron(ys_mode(js,:),ones(size(aK,3),1)); % y0 = ahat(jxj,:)' + ... % kron(ys_mode(js,:),ones(size(ahat,2),1)); rmse_mode(i) = sqrt(mean((vobs(istart:end)-y0(istart:end-1)).^2)); % end end y0=zeros(nobs+1,nruns); nbb=0; for j=1:length(filfilt), load([DirectoryName filesep M_.fname '_filter_step_ahead',num2str(j),'.mat']); nb = size(stock,4); % y0(:,nbb+1:nbb+nb)=squeeze(stock(1,js,:,:)) + ... % kron(sto_ys(nbb+1:nbb+nb,js)',ones(size(stock,3),1)); y0(:,nbb+1:nbb+nb)=squeeze(stock(1,js,1:nobs+1,:)) + ... kron(sto_ys(nbb+1:nbb+nb,js)',ones(nobs+1,1)); %y0(:,:,size(y0,3):size(y0,3)+size(stock,3))=stock; nbb=nbb+nb; clear stock; end y0M=mean(y0,2); for j=1:nruns, rmse_MC(j,i) = sqrt(mean((vobs(istart:end)-y0(istart:end-1,j)).^2)); end if exist('xparam1_mean','var') %eval(['rmse_pmean(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0M(istart:end-1)).^2));']) [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1_mean,stock_gend,stock_data,{},0); y0 = squeeze(aK(1,jxj,:)) + ... kron(ys_mean(js,:),ones(size(aK,3),1)); % y0 = ahat(jxj,:)' + ... % kron(ys_mean(js,:),ones(size(ahat,2),1)); rmse_pmean(i) = sqrt(mean((vobs(istart:end)-y0(istart:end-1)).^2)); end end clear stock_filter; end for j=1:nruns, lnprior(j,1) = priordens(x(j,:)',bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4); end likelihood=logpo2(:)-lnprior(:); disp('... done!') if options_.opt_gsa.ppost save([OutDir,filesep,fnamtmp,'.mat'], 'x', 'logpo2', 'likelihood', 'rmse_MC', 'rmse_mode','rmse_pmean') else if options_.opt_gsa.lik_only save([OutDir,filesep,fnamtmp, '.mat'], 'x', 'logpo2','likelihood', '-append') else save([OutDir,filesep,fnamtmp, '.mat'], 'x', 'logpo2','likelihood', 'rmse_MC','-append') if exist('xparam1_mean','var') save([OutDir,filesep,fnamtmp, '.mat'], 'rmse_pmean','-append') end if exist('xparam1','var') save([OutDir,filesep,fnamtmp,'.mat'], 'rmse_mode','-append') end end end else if options_.opt_gsa.lik_only && options_.opt_gsa.ppost==0 load([OutDir,filesep,fnamtmp, '.mat'],'x','logpo2','likelihood'); else load([OutDir,filesep,fnamtmp, '.mat'],'x','logpo2','likelihood','rmse_MC','rmse_mode','rmse_pmean'); end lnprior=logpo2(:)-likelihood(:); nruns=size(x,1); nfilt=floor(pfilt*nruns); end % smirnov tests nfilt0=nfilt*ones(size(vvarvecm,1),1); logpo2=logpo2(:); if ~options_.opt_gsa.ppost [dum, ipost]=sort(-logpo2); [dum, ilik]=sort(-likelihood); end if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only if options_.opt_gsa.pprior anam='rmse_prior_post'; else anam='rmse_mc_post'; end stab_map_1(x, ipost(1:nfilt), ipost(nfilt+1:end), anam, 1,[],OutDir); stab_map_2(x(ipost(1:nfilt),:),alpha2,pvalue,anam, OutDir); if options_.opt_gsa.pprior anam='rmse_prior_lik'; else anam='rmse_mc_lik'; end stab_map_1(x, ilik(1:nfilt), ilik(nfilt+1:end), anam, 1,[],OutDir); stab_map_2(x(ilik(1:nfilt),:),alpha2,pvalue,anam, OutDir); else for i=1:size(vvarvecm,1), [dum, ixx(:,i)]=sort(rmse_MC(:,i)); if options_.opt_gsa.ppost, %nfilt0(i)=length(find(rmse_MC(:,i)0.0001 ); vvarvecm=vvarvecm(ivar,:); rmse_MC=rmse_MC(:,ivar); disp(' ') % if options_.opt_gsa.ppost==0 && options_.opt_gsa.pprior, disp(['Sample filtered the ',num2str(pfilt*100),'% best RMSE''s for each observed series ...' ]) % else % disp(['Sample filtered the best RMSE''s smaller than RMSE at the posterior mean ...' ]) % end % figure, boxplot(rmse_MC) % set(gca,'xticklabel',vvarvecm) % saveas(gcf,[fname_,'_SA_RMSE']) disp(' ') disp(' ') disp('RMSE ranges after filtering:') if options_.opt_gsa.ppost==0 && options_.opt_gsa.pprior, disp([' best ',num2str(pfilt*100),'% filtered remaining 90%']) disp([' min max min max posterior mode']) else disp([' best filtered remaining ']) disp([' min max min max posterior mean']) end for j=1:size(vvarvecm,1), disp([vvarvecm(j,:), sprintf('%15.5g',[min(rmse_MC(ixx(1:nfilt0(j),j),j)) ... max(rmse_MC(ixx(1:nfilt0(j),j),j)) ... min(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ... max(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ... rmse_txt(j)])]) % disp([vvarvecm(j,:), sprintf('%15.5g',[min(logpo2(ixx(1:nfilt,j))) ... % max(logpo2(ixx(1:nfilt,j))) ... % min(logpo2(ixx(nfilt+1:end,j))) ... % max(logpo2(ixx(nfilt+1:end,j)))])]) end SP=zeros(npar+nshock,size(vvarvecm,1)); for j=1:size(vvarvecm,1), ns=find(PP(:,j)1),:); snam=param_names(find(nsp>0),:); % snam0=bayestopt_.name(find(nsp==0)); % snam1=bayestopt_.name(find(nsp==1)); % snam2=bayestopt_.name(find(nsp>1)); % snam=bayestopt_.name(find(nsp>0)); nsnam=(find(nsp>1)); disp(' ') disp(' ') disp('These parameters do not affect significantly the fit of ANY observed series:') disp(snam0) disp(' ') disp('These parameters affect ONE single observed series:') disp(snam1) disp(' ') disp('These parameters affect MORE THAN ONE observed series: trade off exists!') disp(snam2) %pnam=bayestopt_.name(end-estim_params_.np+1:end); pnam=bayestopt_.name; % plot trade-offs if ~options_.nograph a00=jet(size(vvarvecm,1)); for ix=1:ceil(length(nsnam)/5), if options_.opt_gsa.ppost temp_name='RMSE Posterior Tradeoffs: Log Posterior'; else if options_.opt_gsa.pprior temp_name='RMSE Prior Tradeoffs: Log Posterior'; else temp_name='RMSE MC Tradeoffs: Log Posterior'; end end hh = dyn_figure(options_,'name',[temp_name,' ',int2str(ix)]); for j=1+5*(ix-1):min(size(snam2,1),5*ix), subplot(2,3,j-5*(ix-1)) %h0=cumplot(x(:,nsnam(j)+nshock)); h0=cumplot(x(:,nsnam(j))); set(h0,'color',[0 0 0]) hold on, npx=find(SP(nsnam(j),:)==0); %a0=jet(nsp(nsnam(j))); % a0=a00(np,:); for i=1:size(vvarvecm,1), %h0=cumplot(x(ixx(1:nfilt,np(i)),nsnam(j)+nshock)); % h0=cumplot(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j))); if any(npx==i), h0=cumplot(x(ixx(1:nfilt0(i),i),nsnam(j))*NaN); else h0=cumplot(x(ixx(1:nfilt0(i),i),nsnam(j))); end set(h0,'color',a00(i,:)) end ydum=get(gca,'ylim'); %xdum=xparam1(nshock+nsnam(j)); if exist('xparam1') xdum=xparam1(nsnam(j)); h1=plot([xdum xdum],ydum); set(h1,'color',[0.85 0.85 0.85],'linewidth',2) end xlabel('') title([pnam{nsnam(j)}],'interpreter','none') end %subplot(3,2,6) if exist('OCTAVE_VERSION'), legend(char('base',vvarvecm),'location','eastoutside'); else h0=legend(char('base',vvarvecm),0); set(h0,'fontsize',6,'position',[0.7 0.1 0.2 0.3],'interpreter','none'); end %h0=legend({'base',vnam{np}}',0); %set(findobj(get(h0,'children'),'type','text'),'interpreter','none') if options_.opt_gsa.ppost dyn_saveas(hh,[ OutDir filesep fname_ '_rmse_post_' int2str(ix)],options_); else if options_.opt_gsa.pprior dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_' int2str(ix) ],options_); else dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_' int2str(ix)],options_); end end end end for j=1:size(SP,2), nsx(j)=length(find(SP(:,j))); end disp(' ') disp(' ') disp('Sensitivity table (significance and direction):') vav=char(zeros(1, size(param_names,2)+3 )); ibl = 12-size(vvarvecm,2); for j=1:size(vvarvecm,1), vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)]; end disp(vav) for j=1:npar+nshock, %estim_params_.np, %disp([param_names(j,:), sprintf('%8.5g',SP(j,:))]) disp([param_names(j,:),' ', sprintf('%12.3g',PP(j,:))]) disp([char(zeros(1, size(param_names,2)+3 )),sprintf(' (%6g)',SS(j,:))]) end disp(' ') disp(' ') disp('Starting bivariate analysis:') for i=1:size(vvarvecm,1) if options_.opt_gsa.ppost fnam = ['rmse_post_',deblank(vvarvecm(i,:))]; else if options_.opt_gsa.pprior fnam = ['rmse_prior_',deblank(vvarvecm(i,:))]; else fnam = ['rmse_mc_',deblank(vvarvecm(i,:))]; end end stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,pvalue,fnam, OutDir); % [pc,latent,explained] = pcacov(c0); % %figure, bar([explained cumsum(explained)]) % ifig=0; % j2=0; % for j=1:npar+nshock, % i2=find(abs(pc(:,j))>alphaPC); % if ~isempty(i2), % j2=j2+1; % if mod(j2,12)==1, % ifig=ifig+1; % figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]), % end % subplot(3,4,j2-(ifig-1)*12) % bar(pc(i2,j)), % set(gca,'xticklabel',bayestopt_.name(i2)), % set(gca,'xtick',[1:length(i2)]) % title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%']) % end % if (mod(j2,12)==0 | j==(npar+nshock)) & j2, % saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)]) % end % end % close all end end