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