66 lines
1.7 KiB
Matlab
66 lines
1.7 KiB
Matlab
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function [pdraws, idemodel, idemoments] = dynare_identification()
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% main
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global M_ options_ oo_ bayestopt_ estim_params_
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options_ = set_default_option(options_,'datafile',[]);
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options_.mode_compute = 0;
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[data,rawdata]=dynare_estimation_init([],1);
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% computes a first linear solution to set up various variables
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dynare_resolve;
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options_.prior_mc=2000;
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SampleSize = options_.prior_mc;
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% results = prior_sampler(0,M_,bayestopt_,options_,oo_);
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prior_draw(1,bayestopt_);
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IdentifDirectoryName = CheckPath('identification');
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indx = estim_params_.param_vals(:,1);
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indexo=[];
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if ~isempty(estim_params_.var_exo)
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indexo = estim_params_.var_exo(:,1);
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end
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useautocorr = 0;
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nlags = 3;
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iteration = 0;
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loop_indx = 0;
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h = waitbar(0,'Monte Carlo identification checks ...');
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while iteration < SampleSize,
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loop_indx = loop_indx+1;
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params = prior_draw();
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set_all_parameters(params);
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[JJ, H] = getJJ(M_,oo_,options_,0,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
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if ~isempty(JJ),
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iteration = iteration + 1;
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pdraws(iteration,:) = params';
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[idemodel.Mco(:,iteration), idemoments.Mco(:,iteration), ...
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idemodel.Pco(:,:,iteration), idemoments.Pco(:,:,iteration), ...
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idemodel.cond(iteration), idemoments.cond(iteration), ...
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idemodel.ee(:,iteration), idemoments.ee(:,iteration), ...
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idemodel.ind(:,iteration), idemoments.ind(:,iteration), ...
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idemodel.indno{iteration}, idemoments.indno{iteration}] = ...
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identification_checks(H,JJ, bayestopt_);
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waitbar(iteration/SampleSize,h)
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end
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end
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close(h)
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save([IdentifDirectoryName '/' M_.fname '_identif'], 'pdraws', 'idemodel', 'idemoments')
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disp_identification(pdraws, idemodel, idemoments)
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