131 lines
3.8 KiB
Matlab
131 lines
3.8 KiB
Matlab
function M = set_all_parameters(xparam1,estim_params,M)
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%@info:
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%! @deftypefn {Function File} {@var{M} =} dynSeries (@var{xparams1},@var{estim_params},@var{M})
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%! @anchor{set_all_parameters}
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%! @sp 1
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%! Update parameter values (deep parameters and covariance matrices).
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%! @sp 2
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%! @strong{Inputs}
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%! @sp 1
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%! @table @ @var
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%! @item xparam1
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%! N*1 vector of doubles, the values of the N estimated parameters.
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%! @item estim_params
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%! Dynare structure describing the estimated parameters.
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%! @item M
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%! Dynare structure describing the model.
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%! @end table
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%! @sp 1
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%! @strong{Outputs}
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%! @sp 1
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%! @table @ @var
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%! @item M
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%! Dynare structure describing the model, with updated parameters and covariances matrices.
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%! @end table
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%! @sp 2
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%! @strong{This function is called by:}
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%! @sp 1
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%! @ref{DsgeSmoother}, @ref{dynare_estimation_1}, @ref{@@gsa/filt_mc_}, @ref{identification_analysis}, @ref{PosteriorFilterSmootherAndForecast}, @ref{prior_posterior_statistics_core}, @ref{prior_sampler}
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%! @sp 2
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%! @strong{This function calls:}
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%! @sp 2
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%! @end deftypefn
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%@eod:
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% Copyright (C) 2003-2012 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 <http://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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np = estim_params.np;
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Sigma_e = M.Sigma_e;
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H = M.H;
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% setting shocks variance
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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
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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 ~isempty(M.Correlation_matrix)
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Sigma_e = diag(sqrt(diag(Sigma_e)))*M.Correlation_matrix*diag(sqrt(diag(Sigma_e))); % use of old correlation matrix is correct due to the diagonal structure and later only using the hence correctly updated diagonal entries of Sigma_e
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end
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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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M.Correlation_matrix(k1,k2) = xparam1(i+offset);
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M.Correlation_matrix(k2,k1) = M.Correlation_matrix(k1,k2);
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Sigma_e(k1,k2) = xparam1(i+offset)*sqrt(Sigma_e(k1,k1)*Sigma_e(k2,k2));
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Sigma_e(k2,k1) = Sigma_e(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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% setting measurement error covariances
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if ~isempty(M.Correlation_matrix_ME)
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H = diag(sqrt(diag(H)))*M.Correlation_matrix_ME*diag(sqrt(diag(H)));
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end
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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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M.Correlation_matrix_ME(k1,k2) = xparam1(i+offset);
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M.Correlation_matrix_ME(k2,k1) = M.Correlation_matrix_ME(k1,k2);
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H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
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H(k2,k1) = H(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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% setting structural parameters
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%
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if np
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M.params(estim_params.param_vals(:,1)) = xparam1(offset+1:end);
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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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end
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if nvn || ncn
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M.H = H;
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end |