Fill in posterior_mode with info from posterior samples.
parent
b830805cb6
commit
8bd963de64
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@ -72,7 +72,7 @@ skipline()
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try
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try
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disp(sprintf('Log data density is %f.',oo_.MarginalDensity.ModifiedHarmonicMean))
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disp(sprintf('Log data density is %f.',oo_.MarginalDensity.ModifiedHarmonicMean))
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catch
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catch
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[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_);
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[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
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disp(sprintf('Log data density is %f.',oo_.MarginalDensity.ModifiedHarmonicMean))
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disp(sprintf('Log data density is %f.',oo_.MarginalDensity.ModifiedHarmonicMean))
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end
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end
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num_draws=NumberOfDraws*options_.mh_nblck;
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num_draws=NumberOfDraws*options_.mh_nblck;
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@ -443,7 +443,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
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%% Estimation of the marginal density from the Mh draws:
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%% Estimation of the marginal density from the Mh draws:
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if options_.mh_replic
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if options_.mh_replic
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[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_);
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[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
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% Store posterior statistics by parameter name
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% Store posterior statistics by parameter name
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oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, pnames);
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oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, pnames);
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if ~options_.nograph
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if ~options_.nograph
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@ -1,4 +1,4 @@
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function [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_)
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function [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_)
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% function marginal = marginal_density()
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% function marginal = marginal_density()
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% Computes the marginal density
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% Computes the marginal density
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%
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%
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@ -58,6 +58,9 @@ lpost_mode = posterior_kernel_at_the_mode;
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xparam1 = posterior_mean;
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xparam1 = posterior_mean;
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hh = inv(SIGMA);
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hh = inv(SIGMA);
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fprintf(' Done!\n');
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fprintf(' Done!\n');
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if ~isfield(oo_,'posterior_mode')
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oo_=fill_mh_mode(posterior_mode',NaN(npar,1),M_,options_,estim_params_,bayestopt_,oo_,'posterior');
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end
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% save the posterior mean and the inverse of the covariance matrix
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% save the posterior mean and the inverse of the covariance matrix
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% (usefull if the user wants to perform some computations using
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% (usefull if the user wants to perform some computations using
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@ -120,4 +123,87 @@ while check_coverage
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end
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end
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end
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end
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oo_.MarginalDensity.ModifiedHarmonicMean = mean(marginal(:,2));
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oo_.MarginalDensity.ModifiedHarmonicMean = mean(marginal(:,2));
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return
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function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
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%function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
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%
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% INPUTS
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% o xparam1 [double] (p*1) vector of estimate parameters.
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% o stdh [double] (p*1) vector of estimate parameters.
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% o M_ Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
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% o estim_params_ Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
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% o options_ Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
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% o bayestopt_ Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
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% o oo_ Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
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%
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% OUTPUTS
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% o oo_ Matlab's structure gathering the results
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%
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% SPECIAL REQUIREMENTS
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% None.
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nvx = estim_params_.nvx; % Variance of the structural innovations (number of parameters).
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nvn = estim_params_.nvn; % Variance of the measurement innovations (number of parameters).
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ncx = estim_params_.ncx; % Covariance of the structural innovations (number of parameters).
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ncn = estim_params_.ncn; % Covariance of the measurement innovations (number of parameters).
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np = estim_params_.np ; % Number of deep parameters.
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nx = nvx+nvn+ncx+ncn+np; % Total number of parameters to be estimated.
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if np
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ip = nvx+nvn+ncx+ncn+1;
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for i=1:np
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name = bayestopt_.name{ip};
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eval(['oo_.' field_name '_mode.parameters.' name ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.parameters.' name ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if nvx
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ip = 1;
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for i=1:nvx
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k = estim_params_.var_exo(i,1);
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name = deblank(M_.exo_names(k,:));
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eval(['oo_.' field_name '_mode.shocks_std.' name ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.shocks_std.' name ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if nvn
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ip = nvx+1;
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for i=1:nvn
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name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
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eval(['oo_.' field_name '_mode.measurement_errors_std.' name ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.measurement_errors_std.' name ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if ncx
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ip = nvx+nvn+1;
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for i=1:ncx
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k1 = estim_params_.corrx(i,1);
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k2 = estim_params_.corrx(i,2);
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NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
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eval(['oo_.' field_name '_mode.shocks_corr.' NAME ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.shocks_corr.' NAME ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if ncn
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ip = nvx+nvn+ncx+1;
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for i=1:ncn
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k1 = estim_params_.corrn(i,1);
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k2 = estim_params_.corrn(i,2);
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NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
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eval(['oo_.' field_name '_mode.measurement_errors_corr.' NAME ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.measurement_errors_corr.' NAME ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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return
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