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Move definition of prior distribution names to separate function to make it useable in other contexts.

time-shift
Johannes Pfeifer 2020-06-26 12:06:32 +02:00
parent d7a8b6de4f
commit e92054e16b
2 changed files with 24 additions and 6 deletions
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21
matlab/distributions/prior_dist_names.m Normal file
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@ -0,0 +1,21 @@
function pnames=prior_dist_names
%function pnames=prior_dist_names
% Provides the name strings for the prior distribution codes in bayestopt_.pshape
% Copyright (C) 2020 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
pnames={''; 'beta'; 'gamm'; 'norm'; 'invg'; 'unif'; 'invg2'; ''; 'weibl'};

9
matlab/dynare_estimation_1.m
View File

@ -149,9 +149,6 @@ ncn = estim_params_.ncn; % Covariance of the measurement innovations (number of
np = estim_params_.np ; % Number of deep parameters.
nx = nvx+nvn+ncx+ncn+np; % Total number of parameters to be estimated.
% Set the names of the priors.
pnames = {''; 'beta'; 'gamm'; 'norm'; 'invg'; 'unif'; 'invg2'; ''; 'weibl'};
dr = oo_.dr;
if ~isempty(estim_params_)
@ -357,7 +354,7 @@ end
if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
% display results table and store parameter estimates and standard errors in results
oo_ = display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, pnames, 'Posterior', 'posterior');
oo_ = display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Posterior', 'posterior');
% Laplace approximation to the marginal log density:
if options_.cova_compute
estim_params_nbr = size(xparam1,1);
@ -378,7 +375,7 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
end
elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
oo_=display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, pnames, 'Maximum Likelihood', 'mle');
oo_=display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Maximum Likelihood', 'mle');
end
if np > 0
@ -490,7 +487,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
% Store posterior statistics by parameter name
oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, pnames);
oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, prior_dist_names);
if ~options_.nograph
oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
end