Add function for updating measurement error covariance matrix

(cherry picked from commit 69fc7acb)

matlab/set_measurement_errors.m

+function M_ = set_measurement_errors(xparam1,estim_params_,M_)
+% function M_=set_measurement_errors(xparam1,estim_params_,M_)
+% Sets parameters value (except measurement errors)
+% This is called for computations such as IRF and forecast
+% when measurement errors aren't taken into account; in contrast to
+% set_parameters.m, the global M_-structure is not altered
+%
+% INPUTS
+%    xparam1:   vector of parameters to be estimated (initial values)
+%    M_:        Dynare model-structure
+%
+% OUTPUTS
+%    M_:        Dynare model-structure
+%
+% SPECIAL REQUIREMENTS
+%    none
+
+% Copyright (C) 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 <http://www.gnu.org/licenses/>.
+
+H = M_.H;
+Correlation_matrix_ME = M_.Correlation_matrix_ME;
+
+% setting measument error variance; on the diagonal of Covariance matrix; used later
+% for updating covariances
+offset = estim_params_.nvx;
+
+if estim_params_.nvn
+    for i=1:estim_params_.nvn
+        k = estim_params_.nvn_observable_correspondence(i,1);
+        H(k,k) = xparam1(i+offset)^2;
+    end
+end
+
+% update offset
+offset = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx;
+
+% setting measurement error covariances
+if estim_params_.ncn
+    corrn_observable_correspondence = estim_params_.corrn_observable_correspondence;
+    for i=1:estim_params_.ncn
+        k1 = corrn_observable_correspondence(i,1);
+        k2 = corrn_observable_correspondence(i,2);
+        Correlation_matrix_ME(k1,k2) = xparam1(i+offset);
+        Correlation_matrix_ME(k2,k1) = Correlation_matrix_ME(k1,k2);
+    end
+end
+%build covariance matrix from correlation matrix and variances already on
+%diagonal
+H = diag(sqrt(diag(H)))*Correlation_matrix_ME*diag(sqrt(diag(H)));
+%if calibrated covariances, set them now to their stored value
+if isfield(estim_params_,'calibrated_covariances_ME')
+    H(estim_params_.calibrated_covariances_ME.position)=estim_params_.calibrated_covariances_ME.cov_value;
+end
+
+M_.H = H;
+M_.Correlation_matrix_ME=Correlation_matrix_ME;