134 lines
3.9 KiB
Matlab
134 lines
3.9 KiB
Matlab
function dynare_MC(var_list_,OutDir,data,rawdata,data_info)
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%
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% Adapted by M. Ratto from dynare_estimation.m and posteriorsmoother.m
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% (dynare_estimation.m and posteriorsmoother.m are part of DYNARE,
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% copyright M. Juillard)
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%
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% Part of the Sensitivity Analysis Toolbox for DYNARE
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%
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% Written by Marco Ratto, 2006
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% Joint Research Centre, The European Commission,
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% (http://eemc.jrc.ec.europa.eu/),
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% marco.ratto@jrc.it
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%
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% Disclaimer: This software is not subject to copyright protection and is in the public domain.
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% It is an experimental system. The Joint Research Centre of European Commission
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% assumes no responsibility whatsoever for its use by other parties
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% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
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% characteristic. We would appreciate acknowledgement if the software is used.
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% Reference:
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% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
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%
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global M_ options_ oo_ estim_params_
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global bayestopt_
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% if options_.filtered_vars ~= 0 & options_.filter_step_ahead == 0
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% options_.filter_step_ahead = 1;
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% end
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% if options_.filter_step_ahead ~= 0
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% options_.nk = max(options_.filter_step_ahead);
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% else
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% options_.nk = 0;
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% end
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%
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options_.filter_step_ahead=1;
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options_.nk = 1;
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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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if isempty(options_.datafile)
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error('ESTIMATION: datafile option is missing')
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end
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if isempty(options_.varobs)
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error('ESTIMATION: VAROBS is missing')
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end
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gend = data_info.gend;
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n_varobs = size(options_.varobs,1);
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data_index = data_info.data_index;
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number_of_observations = data_info.number_of_observations;
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no_more_missing_observations = data_info.no_more_missing_observations;
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missing_value = data_info.missing_value;
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offset = npar-np;
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fname_=M_.fname;
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options_ = set_default_option(options_,'opt_gsa',1);
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options_gsa_ = options_.opt_gsa;
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if options_gsa_.pprior,
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namfile=[fname_,'_prior'];
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else
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namfile=[fname_,'_mc'];
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end
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load([OutDir,'/',namfile],'lpmat', 'lpmat0', 'istable')
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% load(options_.mode_file)
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%%
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%%
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%%
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x=[lpmat0(istable,:) lpmat(istable,:)];
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clear lpmat lpmat0 istable %iunstable egg yys T
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B = size(x,1);
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff, aK] = DsgeSmoother(x(1,:)',gend,data,data_index,missing_value);
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n1=size(atT,1);
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nfil=B/40;
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stock_smooth = zeros(M_.endo_nbr,gend,40);
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stock_filter = zeros(M_.endo_nbr,gend+1,40);
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stock_ys = zeros(40, M_.endo_nbr);
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logpo2=zeros(B,1);
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%%
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h = waitbar(0,'MC smoother ...');
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delete([OutDir,'/',namfile,'_*.mat'])
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ib=0;
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ifil=0;
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opt_gsa=options_.opt_gsa;
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for b=1:B
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ib=ib+1;
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deep = x(b,:)';
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set_all_parameters(deep);
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[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
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%deep(1:offset) = xparam1(1:offset);
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logpo2(b,1) = DsgeLikelihood(deep,gend,data,data_index,number_of_observations,no_more_missing_observations);
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if opt_gsa.lik_only==0,
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff, aK] = DsgeSmoother(deep,gend,data,data_index,missing_value);
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stock_smooth(:,:,ib)=atT(1:M_.endo_nbr,:);
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% stock_filter(:,:,ib)=updated_variables(1:M_.endo_nbr,:);
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stock_filter(:,:,ib)=aK(1,1:M_.endo_nbr,:);
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stock_ys(ib,:)=ys';
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if ib==40,
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ib=0;
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ifil=ifil+1;
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save([OutDir,'/',namfile,'_',num2str(ifil)],'stock_smooth','stock_filter','stock_ys')
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stock_smooth = zeros(M_.endo_nbr,gend,40);
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stock_filter = zeros(M_.endo_nbr,gend+1,40);
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stock_ys = zeros(40, M_.endo_nbr);
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end
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end
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waitbar(b/B,h,['MC smoother ...',num2str(b),'/',num2str(B)]);
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end
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close(h)
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if opt_gsa.lik_only==0,
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if ib>0,
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ifil=ifil+1;
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stock_smooth = stock_smooth(:,:,1:ib);
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stock_filter = stock_filter(:,:,1:ib);
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stock_ys = stock_ys(1:ib,:);
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save([OutDir,'/',namfile,'_',num2str(ifil)],'stock_smooth','stock_filter','stock_ys')
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end
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end
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stock_gend=gend;
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stock_data=data;
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save([OutDir,'/',namfile],'x','logpo2','stock_gend','stock_data','-append')
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