261 lines
8.3 KiB
Matlab
261 lines
8.3 KiB
Matlab
function posterior_irf(type)
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% Metropolis-Hastings algorithm.
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%
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% INPUTS
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% o type [char] string specifying the joint density of the
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% deep parameters ('prior','posterior').
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%
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% OUTPUTS
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% None (oo_ and plots).
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%
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%
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% ALGORITHM
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% None.
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%
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% SPECIAL REQUIREMENTS
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% None.
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%
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%
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% part of DYNARE, copyright S. Adjemian, M. Juillard (2006)
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% Gnu Public License.
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global options_ estim_params_ oo_ M_ dsge_prior_weight
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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ncn = estim_params_.ncn;
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np = estim_params_.np ;
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npar = nvx+nvn+ncx+ncn+np;
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offset = npar-np;
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%
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MaxNumberOfPlotPerFigure = 9;% The square root must be an integer!
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nn = sqrt(MaxNumberOfPlotPerFigure);
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DirectoryName = CheckPath('Output');
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if strcmpi(type,'posterior')
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MhDirectoryName = CheckPath('metropolis');
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else
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MhDirectoryName = CheckPath('prior');
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end
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MAX_nirfs = ceil(options_.MaxNumberOfBytes/(options_.irf*length(oo_.steady_state)*M_.exo_nbr)/8)+50;
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MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
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if strcmpi(type,'posterior')
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load([ MhDirectoryName '/' M_.fname '_mh_history'])
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TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
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NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
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else% type = 'prior'
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NumberOfDraws = 500;
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end
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B = min([round(.5*NumberOfDraws),500]); options_.B = B;
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try delete([MhDirectoryName '\' M_.fname '_IRFs*']);
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catch disp('No _IRFs files to be deleted!')
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end
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irun = 0;
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irun2 = 0;
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NumberOfIRFfiles = 1;
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ifil2 = 1;
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if strcmpi(type,'posterior')
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h = waitbar(0,'Bayesian (posterior) IRFs...');
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else
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h = waitbar(0,'Bayesian (prior) IRFs...');
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end
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if B <= MAX_nruns
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stock_param = zeros(B, npar);
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else
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stock_param = zeros(MAX_nruns, npar);
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end
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if B >= MAX_nirfs
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stock_irf = zeros(options_.irf,M_.endo_nbr,M_.exo_nbr,MAX_nirfs);
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else
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stock_irf = zeros(options_.irf,M_.endo_nbr,M_.exo_nbr,B);
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end
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for b=1:B
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irun = irun+1;
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irun2 = irun2+1;
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deep = GetOneDraw(type);
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stock_param(irun2,:) = deep;
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set_parameters(deep);
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dr = resol(oo_.steady_state,0);
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SS(M_.exo_names_orig_ord,M_.exo_names_orig_ord) = M_.Sigma_e+1e-14*eye(M_.exo_nbr);
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cs = transpose(chol(SS));
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for i = 1:M_.exo_nbr
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if SS(i,i) > 1e-13
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y=irf(dr,SS(M_.exo_names_orig_ord,i), options_.irf, options_.drop,options_.replic,options_.order);
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if options_.relative_irf
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y = 100*y/cs(i,i);
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end
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for j = 1:M_.endo_nbr
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if max(y(j,:)) - min(y(j,:)) > 1e-10
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stock_irf(:,j,i,irun) = transpose(y(j,:));
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end
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end
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end
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end
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if irun == MAX_nirfs | irun == B | b == B
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if b == B
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stock_irf = stock_irf(:,:,:,1:irun);
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end
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save([MhDirectoryName '/' M_.fname '_irf' int2str(NumberOfIRFfiles)],'stock_irf');
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NumberOfIRFfiles = NumberOfIRFfiles+1;
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irun = 0;
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end
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if irun2 > MAX_nruns | b == B
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if b == B
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stock_param = stock_param(1:irun2,:);
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end
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stock = stock_param;
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save([MhDirectoryName '/' M_.fname '_param_irf' int2str(ifil2)],'stock');
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ifil2 = ifil2 + 1;
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irun2 = 1;
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end
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waitbar(b/B,h);
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end
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NumberOfIRFfiles = NumberOfIRFfiles-1;
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ifil2 = ifil2-1;
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close(h);
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ReshapeMatFiles('irf')
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varlist = options_.varlist;
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if isempty(varlist)
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varlist = M_.endo_names;
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SelecVariables = transpose(1:M_.endo_nbr);
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nvar = M_.endo_nbr;
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else
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nvar = size(varlist,1);
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SelecVariables = [];
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for i=1:nvar
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if ~isempty(strmatch(varlist(i,:),M_.endo_names,'exact'))
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SelecVariables = [SelecVariables;strmatch(varlist(i,:),M_.endo_names,'exact')];
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end
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end
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end
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MeanIRF = zeros(options_.irf,nvar,M_.exo_nbr);
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MedianIRF = zeros(options_.irf,nvar,M_.exo_nbr);
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StdIRF = zeros(options_.irf,nvar,M_.exo_nbr);
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DistribIRF = zeros(options_.irf,9,nvar,M_.exo_nbr);
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HPDIRF = zeros(options_.irf,2,nvar,M_.exo_nbr);
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if options_.TeX
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varlist_TeX = [];
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for i=1:nvar
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varlist_TeX = strvcat(varlist_TeX,M_.endo_names_tex(SelecVariables(i),:));
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end
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end
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fprintf('MH: Posterior IRFs...\n');
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tit(M_.exo_names_orig_ord,:) = M_.exo_names;
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kdx = 0;
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for file = 1:NumberOfIRFfiles
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load([MhDirectoryName '/' M_.fname '_IRFs' int2str(file)]);
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for i = 1:M_.exo_nbr
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for j = 1:nvar
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for k = 1:size(STOCK_IRF,1)
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kk = k+kdx;
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[MeanIRF(kk,j,i),MedianIRF(kk,j,i),VarIRF(kk,j,i),HPDIRF(kk,:,j,i),DistribIRF(kk,:,j,i)] = ...
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posterior_moments(squeeze(STOCK_IRF(k,SelecVariables(j),i,:)),0);
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end
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end
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end
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kdx = kdx + size(STOCK_IRF,1);
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end
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clear STOCK_IRF;
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for i = 1:M_.exo_nbr
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for j = 1:nvar
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name = [deblank(M_.endo_names(SelecVariables(j),:)) '_' deblank(tit(i,:))];
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eval(['oo_.PosteriorIRF.Mean.' name ' = MeanIRF(:,j,i);']);
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eval(['oo_.PosteriorIRF.Median.' name ' = MedianIRF(:,j,i);']);
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eval(['oo_.PosteriorIRF.Var.' name ' = VarIRF(:,j,i);']);
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eval(['oo_.PosteriorIRF.Distribution.' name ' = DistribIRF(:,:,j,i);']);
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eval(['oo_.PosteriorIRF.HPDinf.' name ' = HPDIRF(:,1,j,i);']);
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eval(['oo_.PosteriorIRF.HPDsup.' name ' = HPDIRF(:,2,j,i);']);
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end
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end
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%%
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%% Finally i build the plots.
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%%
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if options_.TeX
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fidTeX = fopen([DirectoryName '/' M_.fname '_BayesianIRF.TeX'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by PosteriorIRF.m (Dynare).\n');
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fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
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fprintf(fidTeX,' \n');
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titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
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end
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%%
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subplotnum = 0;
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for i=1:M_.exo_nbr
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NAMES = [];
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if options_.TeX
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TEXNAMES = [];
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end
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figunumber = 0;
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for j=1:nvar
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if max(abs(MeanIRF(:,j,i))) > 10^(-6)
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subplotnum = subplotnum+1;
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if options_.nograph
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if subplotnum == 1 & options_.relative_irf
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hh = figure('Name',['Relative response to orthogonalized shock to ' tit(i,:)],'Visible','off');
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elseif subplotnum == 1 & ~options_.relative_irf
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hh = figure('Name',['Orthogonalized shock to ' tit(i,:)],'Visible','off');
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end
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else
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if subplotnum == 1 & options_.relative_irf
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hh = figure('Name',['Relative response to orthogonalized shock to ' tit(i,:)]);
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elseif subplotnum == 1 & ~options_.relative_irf
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hh = figure('Name',['Orthogonalized shock to ' tit(i,:)]);
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end
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end
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set(0,'CurrentFigure',hh)
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subplot(nn,nn,subplotnum);
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plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
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hold on
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for k = 1:9
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plot(1:options_.irf,DistribIRF(:,k,j,i),'-g','linewidth',0.5)
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end
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plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',1)
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xlim([1 options_.irf]);
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hold off
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name = deblank(varlist(j,:));
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NAMES = strvcat(NAMES,name);
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if options_.TeX
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texname = deblank(varlist_TeX(j,:));
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TEXNAMES = strvcat(TEXNAMES,['$' texname '$']);
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end
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title(name,'Interpreter','none')
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end
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if subplotnum == MaxNumberOfPlotPerFigure | (j == nvar & subplotnum>0)
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figunumber = figunumber+1;
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set(hh,'visible','on')
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eval(['print -depsc2 ' DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber)]);
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eval(['print -dpdf ' DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber)]);
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saveas(hh,[DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber) '.fig']);
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set(hh,'visible','off')
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if options_.nograph, close(hh), end
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if options_.TeX
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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for jj = 1:size(TEXNAMES,1)
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fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
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end
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_Bayesian_IRF_%s}\n',M_.fname,deblank(tit(i,:)));
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if options_.relative_irf
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fprintf(fidTeX,['\\caption{Bayesian relative IRF.}']);
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else
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fprintf(fidTeX,'\\caption{Bayesian IRF.}');
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end
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fprintf(fidTeX,'\\label{Fig:BayesianIRF:%s}\n',deblank(tit(i,:)));
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fprintf(fidTeX,'\\end{figure}\n');
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fprintf(fidTeX,' \n');
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end
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subplotnum = 0;
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end
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end% loop over selected endo_var
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end% loop over exo_var
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%%
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if options_.TeX
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fprintf(fidTeX,'%% End of TeX file.\n');
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fclose(fidTeX);
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end
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fprintf('MH: Posterior IRFs, done!\n');
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