function xparam1=get_all_parameters(estim_params_,M_) % function xparam1=get_parameters % gets parameters values from M_.params into xparam1 (inverse mapping to set_all_parameters) % This is called if a model was calibrated before estimation to back out % parameter values % % INPUTS % estim_params_: Dynare structure describing the estimated parameters. % M_: Dynare structure describing the model. % % OUTPUTS % xparam1: N*1 double vector of parameters from calibrated model that are to be estimated % % SPECIAL REQUIREMENTS % none % Copyright © 2013-2017 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 . if ~isempty(estim_params_) nvx = estim_params_.nvx; ncx = estim_params_.ncx; nvn = estim_params_.nvn; ncn = estim_params_.ncn; np = estim_params_.np; else nvx = 0; ncx = 0; nvn = 0; ncn = 0; np = 0; end Sigma_e = M_.Sigma_e; Correlation_matrix = M_.Correlation_matrix; H = M_.H; Correlation_matrix_ME = M_.Correlation_matrix_ME; xparam1=NaN(nvx+ncx+nvn+ncn+np,1); % stderrs of the exogenous shocks if nvx var_exo = estim_params_.var_exo; for i=1:nvx k = var_exo(i,1); xparam1(i)=sqrt(Sigma_e(k,k)); end end % update offset offset = nvx; % setting measument error variance if nvn for i=1:nvn k = estim_params_.nvn_observable_correspondence(i,1); xparam1(offset+i)=sqrt(H(k,k)); end end % update offset offset = nvx+nvn; % correlations among shocks (ncx) if ncx corrx = estim_params_.corrx; for i=1:ncx k1 = corrx(i,1); k2 = corrx(i,2); xparam1(i+offset)=Correlation_matrix(k1,k2); end end % update offset offset = nvx+nvn+ncx; if ncn corrn_observable_correspondence = estim_params_.corrn_observable_correspondence; for i=1:ncn k1 = corrn_observable_correspondence(i,1); k2 = corrn_observable_correspondence(i,2); xparam1(i+offset)=Correlation_matrix_ME(k1,k2); end end % update offset offset = nvx+ncx+nvn+ncn; % structural parameters if np xparam1(offset+1:end)=M_.params(estim_params_.param_vals(:,1)); end