72 lines
2.5 KiB
Matlab
72 lines
2.5 KiB
Matlab
function M_ = set_measurement_errors(xparam1,estim_params_,M_)
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% function M_=set_measurement_errors(xparam1,estim_params_,M_)
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% Sets parameters value (except measurement errors)
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% This is called for computations such as IRF and forecast
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% when measurement errors aren't taken into account; in contrast to
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% set_parameters.m, the global M_-structure is not altered
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%
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% INPUTS
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% xparam1: vector of parameters to be estimated (initial values)
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% M_: Dynare model-structure
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%
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% OUTPUTS
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% M_: Dynare model-structure
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 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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H = M_.H;
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Correlation_matrix_ME = M_.Correlation_matrix_ME;
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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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offset = estim_params_.nvx;
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if estim_params_.nvn
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for i=1:estim_params_.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 = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx;
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% setting measurement error covariances
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if estim_params_.ncn
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corrn_observable_correspondence = estim_params_.corrn_observable_correspondence;
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for i=1:estim_params_.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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M_.H = H;
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M_.Correlation_matrix_ME=Correlation_matrix_ME;
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