93 lines
3.7 KiB
Matlab
93 lines
3.7 KiB
Matlab
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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% Copyright © 2005-2021 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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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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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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oo_.([field_name '_mode']).parameters.(name) = xparam1(ip);
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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 = M_.exo_names{k};
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oo_.([field_name '_mode']).shocks_std.(name)= xparam1(ip);
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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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oo_.([field_name '_mode']).measurement_errors_std.(name) = xparam1(ip);
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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 = [M_.exo_names{k1} '_' M_.exo_names{k2}];
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oo_.([field_name '_mode']).shocks_corr.(name) = xparam1(ip);
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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 = [M_.endo_names{k1} '_' M_.endo_names{k2}];
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oo_.([field_name '_mode']).measurement_errors_corr.(name) = xparam1(ip);
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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 |