111 lines
3.6 KiB
Matlab
111 lines
3.6 KiB
Matlab
function M_=set_shocks_param(M_,estim_params_,xparam1)
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% function M_=set_shocks_param(M_,estim_params_,xparam1)
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% Set the structural and measurement error variances and covariances
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% Inputs
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% - M_ [structure] Matlab's structure describing the model
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% - estim_params_ [structure] characterizing parameters to be estimated
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% - xparam1 [double] parameter vector
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% Outputs:
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% - M_ [structure] Matlab's structure describing the model
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%
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% Notes: closely follows set_all_parameters.m
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% Copyright © 2012-2023 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;
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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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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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% setting shocks variance on the diagonal of Covariance matrix; used later
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% for updating covariances
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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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Sigma_e(k,k) = xparam1(i)^2;
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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; on the diagonal of Covariance matrix; used later
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% for updating covariances
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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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H(k,k) = xparam1(i+offset)^2;
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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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% setting shocks covariances
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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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Correlation_matrix(k1,k2) = xparam1(i+offset);
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Correlation_matrix(k2,k1) = Correlation_matrix(k1,k2);
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end
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end
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%build covariance matrix from correlation matrix and variances already on
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%diagonal
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Sigma_e = diag(sqrt(diag(Sigma_e)))*Correlation_matrix*diag(sqrt(diag(Sigma_e)));
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%if calibrated covariances, set them now to their stored value
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if isfield(estim_params_,'calibrated_covariances')
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Sigma_e(estim_params_.calibrated_covariances.position)=estim_params_.calibrated_covariances.cov_value;
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end
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% update offset
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offset = nvx+nvn+ncx;
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% setting measurement error covariances
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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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Correlation_matrix_ME(k1,k2) = xparam1(i+offset);
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Correlation_matrix_ME(k2,k1) = Correlation_matrix_ME(k1,k2);
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end
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end
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%build covariance matrix from correlation matrix and variances already on
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%diagonal
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H = diag(sqrt(diag(H)))*Correlation_matrix_ME*diag(sqrt(diag(H)));
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%if calibrated covariances, set them now to their stored value
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if isfield(estim_params_,'calibrated_covariances_ME')
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H(estim_params_.calibrated_covariances_ME.position)=estim_params_.calibrated_covariances_ME.cov_value;
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end
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% updating matrices in M
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if nvx || ncx
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M_.Sigma_e = Sigma_e;
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M_.Correlation_matrix=Correlation_matrix;
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end
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if nvn || ncn
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M_.H = H;
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M_.Correlation_matrix_ME=Correlation_matrix_ME;
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end |