function x0 = stab_map_(OutputDirectoryName) % % function x0 = stab_map_(OutputDirectoryName) % % Mapping of stability regions in the prior ranges applying % Monte Carlo filtering techniques. % % INPUTS (from opt_gsa structure) % Nsam = MC sample size % fload = 0 to run new MC; 1 to load prevoiusly generated analysis % alpha2 = significance level for bivariate sensitivity analysis % [abs(corrcoef) > alpha2] % prepSA = 1: save transition matrices for mapping reduced form % = 0: no transition matrix saved (default) % pprior = 1: sample from prior ranges (default): sample saved in % _prior.mat file % = 0: sample from posterior ranges: sample saved in % _mc.mat file % OUTPUT: % x0: one parameter vector for which the model is stable. % % GRAPHS % 1) Pdf's of marginal distributions under the stability (dotted % lines) and unstability (solid lines) regions % 2) Cumulative distributions of: % - stable subset (dotted lines) % - unacceptable subset (solid lines) % 3) Bivariate plots of significant correlation patterns % ( abs(corrcoef) > alpha2) under the stable and unacceptable subsets % % USES lptauSEQ, % stab_map_1, stab_map_2 % % Part of the Sensitivity Analysis Toolbox for DYNARE % % Written by Marco Ratto, 2006 % Joint Research Centre, The European Commission, % (http://eemc.jrc.ec.europa.eu/), % marco.ratto@jrc.it % % Disclaimer: This software is not subject to copyright protection and is in the public domain. % It is an experimental system. The Joint Research Centre of European Commission % assumes no responsibility whatsoever for its use by other parties % and makes no guarantees, expressed or implied, about its quality, reliability, or any other % characteristic. We would appreciate acknowledgement if the software is used. % Reference: % M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006. % %global bayestopt_ estim_params_ dr_ options_ ys_ fname_ global bayestopt_ estim_params_ options_ oo_ M_ opt_gsa=options_.opt_gsa; Nsam = opt_gsa.Nsam; fload = opt_gsa.load_stab; ksstat = opt_gsa.ksstat; alpha2 = opt_gsa.alpha2_stab; prepSA = (opt_gsa.redform | opt_gsa.identification); pprior = opt_gsa.pprior; ilptau = opt_gsa.ilptau; nliv = opt_gsa.morris_nliv; ntra = opt_gsa.morris_ntra; dr_ = oo_.dr; %if isfield(dr_,'ghx'), ys_ = oo_.dr.ys; nspred = dr_.nspred; %size(dr_.ghx,2); nboth = dr_.nboth; nfwrd = dr_.nfwrd; %end fname_ = M_.fname; nshock = estim_params_.nvx; nshock = nshock + estim_params_.nvn; nshock = nshock + estim_params_.ncx; nshock = nshock + estim_params_.ncn; lpmat0=[]; pshape = bayestopt_.pshape(nshock+1:end); p1 = bayestopt_.p1(nshock+1:end); p2 = bayestopt_.p2(nshock+1:end); p3 = bayestopt_.p3(nshock+1:end); p4 = bayestopt_.p4(nshock+1:end); if nargin==0, OutputDirectoryName=''; end opt=options_; options_.periods=0; options_.nomoments=1; options_.irf=0; options_.noprint=1; options_.simul=0; if fload==0, % if prepSA % T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),Nsam/2); % end if isfield(dr_,'ghx'), egg=zeros(length(dr_.eigval),Nsam); end yys=zeros(length(dr_.ys),Nsam); if opt_gsa.morris if opt_gsa.morris == 1 [lpmat, OutFact] = Sampling_Function_2(nliv, estim_params_.np, ntra, ones(estim_params_.np, 1), zeros(estim_params_.np,1), []); lpmat = lpmat.*(nliv-1)/nliv+1/nliv/2; Nsam=size(lpmat,1); elseif opt_gsa.morris==2 lpmat = prep_ide(Nsam,estim_params_.np,5); Nsam=size(lpmat,1); end else if estim_params_.np<52 & ilptau>0, [lpmat] = lptauSEQ(Nsam,estim_params_.np); % lptau if estim_params_.np>30 | ilptau==2, % scrambled lptau for j=1:estim_params_.np, lpmat(:,j)=lpmat(randperm(Nsam),j); end end else ilptau==0 %[lpmat] = rand(Nsam,estim_params_.np); for j=1:estim_params_.np, lpmat(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube end end end prior_draw_gsa(1); if pprior, for j=1:nshock, lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube if opt_gsa.prior_range lpmat0(:,j)=lpmat0(:,j).*(bayestopt_.ub(j)-bayestopt_.lb(j))+bayestopt_.lb(j); end end if opt_gsa.prior_range for j=1:estim_params_.np, lpmat(:,j)=lpmat(:,j).*(bayestopt_.ub(j+nshock)-bayestopt_.lb(j+nshock))+bayestopt_.lb(j+nshock); end else xx=prior_draw_gsa(0,[lpmat0 lpmat]); lpmat0=xx(:,1:nshock); lpmat=xx(:,nshock+1:end); clear xx; end else % for j=1:nshock, % xparam1(j) = oo_.posterior_mode.shocks_std.(bayestopt_.name{j}); % sd(j) = oo_.posterior_std.shocks_std.(bayestopt_.name{j}); % lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube % lb = max(bayestopt_.lb(j), xparam1(j)-2*sd(j)); % ub1=xparam1(j)+(xparam1(j) - lb); % define symmetric range around the mode! % ub = min(bayestopt_.ub(j),ub1); % if ub30 & estim_params_.np<52 % lpmat(:,j) = lpmat(randperm(Nsam),j).*(ub-lb)+lb; % else % lpmat(:,j) = lpmat(:,j).*(ub-lb)+lb; % end % end %load([fname_,'_mode']) eval(['load ' options_.mode_file ';']'); d = chol(inv(hh)); lp=randn(Nsam*2,nshock+estim_params_.np)*d+kron(ones(Nsam*2,1),xparam1'); for j=1:Nsam*2, lnprior(j) = any(lp(j,:)'<=bayestopt_.lb | lp(j,:)'>=bayestopt_.ub); end ireal=[1:2*Nsam]; ireal=ireal(find(lnprior==0)); lp=lp(ireal,:); Nsam=min(Nsam, length(ireal)); lpmat0=lp(1:Nsam,1:nshock); lpmat=lp(1:Nsam,nshock+1:end); clear lp lnprior ireal; end % h = waitbar(0,'Please wait...'); istable=[1:Nsam]; jstab=0; iunstable=[1:Nsam]; iindeterm=zeros(1,Nsam); iwrong=zeros(1,Nsam); for j=1:Nsam, M_.params(estim_params_.param_vals(:,1)) = lpmat(j,:)'; %try stoch_simul([]); try [Tt,Rr,SteadyState,info] = dynare_resolve(bayestopt_.restrict_var_list,... bayestopt_.restrict_columns,... bayestopt_.restrict_aux); if ~exist('T') T=zeros(size(dr_.ghx,1),size(dr_.ghx,2)+size(dr_.ghu,2),Nsam); end catch if isfield(oo_.dr,'eigval'), oo_.dr=rmfield(oo_.dr,'eigval'); end if isfield(oo_.dr,'ghx'), oo_.dr=rmfield(oo_.dr,'ghx'); end disp('No solution could be found'), end dr_ = oo_.dr; if isfield(dr_,'ghx'), egg(:,j) = sort(dr_.eigval); iunstable(j)=0; if prepSA jstab=jstab+1; T(:,:,jstab) = [dr_.ghx dr_.ghu]; [A,B] = ghx2transition(squeeze(T(:,:,jstab)), ... bayestopt_.restrict_var_list, ... bayestopt_.restrict_columns, ... bayestopt_.restrict_aux); end if ~exist('nspred'), nspred = dr_.nspred; %size(dr_.ghx,2); nboth = dr_.nboth; nfwrd = dr_.nfwrd; end else istable(j)=0; if isfield(dr_,'eigval') egg(:,j) = sort(dr_.eigval); if exist('nspred') if any(isnan(egg(1:nspred,j))) iwrong(j)=j; else if (nboth | nfwrd) & abs(egg(nspred+1,j))<=options_.qz_criterium, iindeterm(j)=j; end end end else if exist('egg'), egg(:,j)=ones(size(egg,1),1).*NaN; end iwrong(j)=j; end end ys_=real(dr_.ys); yys(:,j) = ys_; ys_=yys(:,1); waitbar(j/Nsam,h,['MC iteration ',int2str(j),'/',int2str(Nsam)]) end close(h) if prepSA, T=T(:,:,1:jstab); end istable=istable(find(istable)); % stable params iunstable=iunstable(find(iunstable)); % unstable params iindeterm=iindeterm(find(iindeterm)); % indeterminacy iwrong=iwrong(find(iwrong)); % dynare could not find solution % % map stable samples % istable=[1:Nsam]; % for j=1:Nsam, % if any(isnan(egg(1:nspred,j))) % istable(j)=0; % else % if abs(egg(nspred,j))>=options_.qz_criterium; %(1-(options_.qz_criterium-1)); %1-1.e-5; % istable(j)=0; % %elseif (dr_.nboth | dr_.nfwrd) & abs(egg(nspred+1,j))<=options_.qz_criterium; %1+1.e-5; % elseif (nboth | nfwrd) & abs(egg(nspred+1,j))<=options_.qz_criterium; %1+1.e-5; % istable(j)=0; % end % end % end % istable=istable(find(istable)); % stable params % % % map unstable samples % iunstable=[1:Nsam]; % for j=1:Nsam, % %if abs(egg(dr_.npred+1,j))>1+1.e-5 & abs(egg(dr_.npred,j))<1-1.e-5; % %if (dr_.nboth | dr_.nfwrd), % if ~any(isnan(egg(1:5,j))) % if (nboth | nfwrd), % if abs(egg(nspred+1,j))>options_.qz_criterium & abs(egg(nspred,j))0 & length(iunstable)ksstat); disp('Smirnov statistics in driving acceptable behaviour') for j=1:estim_params_.np, disp([M_.param_names(estim_params_.param_vals(j,1),:),' d-stat = ', num2str(dproba(j),3)]) end disp(' '); if ~isempty(indstab) stab_map_1(lpmat, istable, iunstable, aname, 1, indstab, OutputDirectoryName); end ixun=iunstable(find(~ismember(iunstable,[iindeterm,iwrong]))); if ~isempty(iindeterm), [proba, dproba] = stab_map_1(lpmat, [1:Nsam], iindeterm, [aname, '_indet'],0); indindet=find(dproba>ksstat); disp('Smirnov statistics in driving indeterminacy') for j=1:estim_params_.np, disp([M_.param_names(estim_params_.param_vals(j,1),:),' d-stat = ', num2str(dproba(j),3)]) end disp(' '); if ~isempty(indindet) stab_map_1(lpmat, istable, iindeterm, [aname, '_indet'], 1, indindet, OutputDirectoryName); end end if ~isempty(ixun), [proba, dproba] = stab_map_1(lpmat, [1:Nsam], ixun, [aname, '_unst'],0); indunst=find(dproba>ksstat); disp('Smirnov statistics in driving instability') for j=1:estim_params_.np, disp([M_.param_names(estim_params_.param_vals(j,1),:),' d-stat = ', num2str(dproba(j),3)]) end disp(' '); if ~isempty(indunst) stab_map_1(lpmat, istable, ixun, [aname, '_unst'], 1, indunst, OutputDirectoryName); end end disp(' ') disp('Starting bivariate analysis:') c0=corrcoef(lpmat(istable,:)); c00=tril(c0,-1); stab_map_2(lpmat(istable,:),alpha2, asname, OutputDirectoryName); if length(iunstable)>3, stab_map_2(lpmat(iunstable,:),alpha2, auname, OutputDirectoryName); end if length(iindeterm)>3, stab_map_2(lpmat(iindeterm,:),alpha2, aindname, OutputDirectoryName); end if length(ixun)>3, stab_map_2(lpmat(ixun,:),alpha2, aunstname, OutputDirectoryName); end x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock); x0 = [x0; lpmat(istable(1),:)']; if istable(end)~=Nsam M_.params(estim_params_.param_vals(:,1)) = lpmat(istable(1),:)'; stoch_simul([]); end else if length(iunstable)==0, disp('All parameter values in the specified ranges are stable!') x0=0.5.*(bayestopt_.ub(1:nshock)-bayestopt_.lb(1:nshock))+bayestopt_.lb(1:nshock); x0 = [x0; lpmat(istable(1),:)']; else disp('All parameter values in the specified ranges are not acceptable!') x0=[]; end end options_.periods=opt.periods; if isfield(opt,'nomoments'), options_.nomoments=opt.nomoments; end options_.irf=opt.irf; options_.noprint=opt.noprint; if isfield(opt,'simul'), options_.simul=opt.simul; end