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15 results

SteadyStateModel.hh

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  • evaluate_smoother.m 5.91 KiB
    function oo = evaluate_smoother(parameters)
    % Evaluate the smoother at parameters.
    %
    % INPUTS
    %    o parameters  a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
    %                  the (estimated) parameters of the model.
    %
    %
    % OUTPUTS
    %    o oo       [structure]  results:
    %                              - SmoothedVariables
    %                              - SmoothedShocks
    %                              - SmoothedVariables
    %                              - SmoothedVariables
    %                              - SmoothedVariables
    %                              - SmoothedVariables
    %                              - SmoothedVariables
    %                              - SmoothedVariables
    %
    % SPECIAL REQUIREMENTS
    %    None
    %
    % REMARKS
    % [1] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
    %     is called more than once (by changing the value of parameters) the sample *must not* change.
    
    % Copyright (C) 2010-2012 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/>.
    
    global options_ M_ bayestopt_ oo_
    
    persistent dataset_
    
    if nargin==0
        parameters = 'posterior_mode';
    end
    
    if ischar(parameters)
        switch parameters
          case 'posterior_mode'
            parameters = get_posterior_parameters('mode');
          case 'posterior_mean'
            parameters = get_posterior_parameters('mean');
          case 'posterior_median'
            parameters = get_posterior_parameters('median');
          case 'prior_mode'
            parameters = bayestopt_.p5(:);
          case 'prior_mean'
            parameters = bayestopt_.p1;
          case 'calibration'
            if isempty(oo_.dr)
                error('You must run ''stoch_simul'' first.');
            end
            parameters = [];
          otherwise
            disp('evaluate_smoother:: If the input argument is a string, then it has to be equal to:')
            disp('                     ''posterior_mode'', ')
            disp('                     ''posterior_mean'', ')
            disp('                     ''posterior_median'', ')
            disp('                     ''prior_mode'' or')
            disp('                     ''prior_mean''.')
            disp('                     ''calibration''.')
            error
        end
    end
    
    if isempty(dataset_)
        % Load and transform data.
        transformation = [];
        if options_.loglinear && ~options_.logdata
            transformation = @log;
        end
        xls.sheet = options_.xls_sheet;
        xls.range = options_.xls_range;
    
        if ~isfield(options_,'nobs')
            options_.nobs = [];
        end
    
        dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
        options_.nobs = dataset_.info.ntobs;
    
        % Determine if a constant is needed.
        if options_.steadystate_flag% if the *_steadystate.m file is provided.
            [ys,params,info] = evaluate_steady_state(oo_.steady_state,M_,options_,oo_,1);
            if size(ys,1) < M_.endo_nbr
                if length(M_.aux_vars) > 0
                    ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
                                                                M_.fname,...
                                                                zeros(M_.exo_nbr,1),...
                                                                oo_.exo_det_steady_state,...
                                                                M_.params,...
                                                                options_.bytecode);
                else
                    error([M_.fname '_steadystate.m doesn''t match the model']);
                end
            end
            oo_.steady_state = ys;
        else% if the steady state file is not provided.
            [dd,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
            oo_.steady_state = dd.ys; clear('dd');
        end
        if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
            options_.noconstant = 1;
        else
            options_.noconstant = 0;
        end
    end
    
    pshape_original   = bayestopt_.pshape;
    bayestopt_.pshape = Inf(size(bayestopt_.pshape));
    clear('priordens')
    
    [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = ...
        DsgeSmoother(parameters,dataset_.info.ntobs,dataset_.data,dataset_.missing.aindex,dataset_.missing.state);
    
    oo.Smoother.SteadyState = ys;
    oo.Smoother.TrendCoeffs = trend_coeff;
    if options_.filter_covariance
        oo.Smoother.variance = P;
    end
    i_endo = bayestopt_.smoother_saved_var_list;
    if options_.nk ~= 0
        oo.FilteredVariablesKStepAhead = ...
            aK(options_.filter_step_ahead,i_endo,:);
        if ~isempty(PK)
            oo.FilteredVariablesKStepAheadVariances = ...
                PK(options_.filter_step_ahead,i_endo,i_endo,:);
        end
        if ~isempty(decomp)
            oo.FilteredVariablesShockDecomposition = ...
                decomp(options_.filter_step_ahead,i_endo,:,:);
        end
    end
    dr = oo_.dr;
    order_var = oo_.dr.order_var;
    for i=bayestopt_.smoother_saved_var_list'
        i1 = order_var(bayestopt_.smoother_var_list(i));
        eval(['oo.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
        eval(['oo.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
        eval(['oo.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
    end
    for i=1:M_.exo_nbr
        eval(['oo.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
    end
    
    oo.dr = oo_.dr;
    
    bayestopt_.pshape = pshape_original;