121 lines
4.5 KiB
Matlab
121 lines
4.5 KiB
Matlab
function oo_=evaluate_smoother(parameters,var_list)
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% Evaluate the smoother at parameters.
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%
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% INPUTS
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% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
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% the (estimated) parameters of the model.
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% o var_list subset of endogenous variables
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%
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%
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% OUTPUTS
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% o oo [structure] results:
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% - SmoothedVariables
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% - SmoothedShocks
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% - SmoothedVariables
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% - SmoothedVariables
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% - SmoothedVariables
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% - SmoothedVariables
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% - SmoothedVariables
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% - SmoothedVariables
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%
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% SPECIAL REQUIREMENTS
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% None
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%
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% REMARKS
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% [1] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
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% is called more than once (by changing the value of parameters) the sample *must not* change.
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% Copyright (C) 2010-2013 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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global options_ M_ bayestopt_ oo_ estim_params_ % estim_params_ may be emty
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persistent dataset_ dataset_info
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if ischar(parameters) && strcmp(parameters,'calibration')
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options_.smoother=1;
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end
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if isempty(dataset_) || isempty(bayestopt_)
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[dataset_,dataset_info,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list, M_.fname, [], M_, options_, oo_, estim_params_, bayestopt_);
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end
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if nargin==0
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parameters = 'posterior_mode';
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end
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if ischar(parameters)
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switch parameters
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case 'posterior_mode'
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parameters = get_posterior_parameters('mode');
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case 'posterior_mean'
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parameters = get_posterior_parameters('mean');
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case 'posterior_median'
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parameters = get_posterior_parameters('median');
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case 'prior_mode'
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parameters = bayestopt_.p5(:);
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case 'prior_mean'
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parameters = bayestopt_.p1;
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case 'calibration'
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if isempty(oo_.dr)
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error('You must run ''stoch_simul'' first.');
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end
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parameters = [];
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otherwise
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disp('evaluate_smoother:: If the input argument is a string, then it has to be equal to:')
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disp(' ''posterior_mode'', ')
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disp(' ''posterior_mean'', ')
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disp(' ''posterior_median'', ')
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disp(' ''prior_mode'' or')
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disp(' ''prior_mean''.')
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disp(' ''calibration''.')
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error
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end
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end
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = ...
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DsgeSmoother(parameters,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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if options_.filter_covariance
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oo_.Smoother.Variance = P;
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end
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i_endo = bayestopt_.smoother_saved_var_list;
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if options_.nk ~= 0
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oo_.FilteredVariablesKStepAhead = ...
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aK(options_.filter_step_ahead,i_endo,:);
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if ~isempty(PK)
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oo_.FilteredVariablesKStepAheadVariances = ...
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PK(options_.filter_step_ahead,i_endo,i_endo,:);
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end
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if ~isempty(decomp)
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oo_.FilteredVariablesShockDecomposition = ...
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decomp(options_.filter_step_ahead,i_endo,:,:);
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end
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end
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for i=bayestopt_.smoother_saved_var_list'
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i1 = oo_.dr.order_var(bayestopt_.smoother_var_list(i));
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eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
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if options_.nk>0
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eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
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end
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eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
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end
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for i=1:M_.exo_nbr
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eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
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end |