Description of evaluate

0001 function oo = evaluate_smoother(parameters)
0002 % Evaluate the smoother at parameters.
0003 %
0004 % INPUTS
0005 %    o parameters  a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
0006 %                  the (estimated) parameters of the model.
0007 %
0008 %
0009 % OUTPUTS
0010 %    o oo       [structure]  results:
0011 %                              - SmoothedVariables
0012 %                              - SmoothedShocks
0013 %                              - SmoothedVariables
0014 %                              - SmoothedVariables
0015 %                              - SmoothedVariables
0016 %                              - SmoothedVariables
0017 %                              - SmoothedVariables
0018 %                              - SmoothedVariables
0019 %
0020 % SPECIAL REQUIREMENTS
0021 %    None
0022 %
0023 % REMARKS
0024 % [1] This function use persistent variables for the dataset and the description of the missing observations. Consequently, if this function
0025 %     is called more than once (by changing the value of parameters) the sample *must not* change.
0026 
0027 % Copyright (C) 2010-2011 Dynare Team
0028 %
0029 % This file is part of Dynare.
0030 %
0031 % Dynare is free software: you can redistribute it and/or modify
0032 % it under the terms of the GNU General Public License as published by
0033 % the Free Software Foundation, either version 3 of the License, or
0034 % (at your option) any later version.
0035 %
0036 % Dynare is distributed in the hope that it will be useful,
0037 % but WITHOUT ANY WARRANTY; without even the implied warranty of
0038 % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
0039 % GNU General Public License for more details.
0040 %
0041 % You should have received a copy of the GNU General Public License
0042 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
0043 
0044 global options_ M_ bayestopt_ oo_
0045 
0046 persistent load_data
0047 persistent gend  data  data_index  number_of_observations  no_more_missing_observations
0048 
0049 if nargin==0
0050     parameters = 'posterior_mode';
0051 end
0052 
0053 if ischar(parameters)
0054     switch parameters
0055       case 'posterior_mode'
0056         parameters = get_posterior_parameters('mode');
0057       case 'posterior_mean'
0058         parameters = get_posterior_parameters('mean');
0059       case 'posterior_median'
0060         parameters = get_posterior_parameters('median');
0061       case 'prior_mode'
0062         parameters = bayestopt_.p5(:);
0063       case 'prior_mean'
0064         parameters = bayestopt_.p1;
0065       case 'calibration'
0066         parameters = [];
0067       otherwise
0068         disp('evaluate_smoother:: If the input argument is a string, then it has to be equal to:')
0069         disp('                     ''posterior_mode'', ')
0070         disp('                     ''posterior_mean'', ')
0071         disp('                     ''posterior_median'', ')
0072         disp('                     ''prior_mode'' or')
0073         disp('                     ''prior_mean''.')
0074         disp('                     ''calibration''.')
0075         error
0076     end
0077 end
0078 
0079 if isempty(load_data)
0080     % Get the data.
0081     n_varobs = size(options_.varobs,1);
0082     rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
0083     options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
0084     gend = options_.nobs;
0085     rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
0086     % Transform the data.
0087     if options_.loglinear
0088         if ~options_.logdata
0089             rawdata = log(rawdata);
0090         end
0091     end
0092     % Test if the data set is real.
0093     if ~isreal(rawdata)
0094         error('There are complex values in the data! Probably  a wrong transformation')
0095     end
0096     % Detrend the data.
0097     options_.missing_data = any(any(isnan(rawdata)));
0098     if options_.prefilter == 1
0099         if options_.missing_data
0100             bayestopt_.mean_varobs = zeros(n_varobs,1);
0101             for variable=1:n_varobs
0102                 rdx = find(~isnan(rawdata(:,variable)));
0103                 m = mean(rawdata(rdx,variable));
0104                 rawdata(rdx,variable) = rawdata(rdx,variable)-m;
0105                 bayestopt_.mean_varobs(variable) = m;
0106             end
0107         else
0108             bayestopt_.mean_varobs = mean(rawdata,1)';
0109             rawdata = rawdata-repmat(bayestopt_.mean_varobs',gend,1);
0110         end
0111     end
0112     data = transpose(rawdata);
0113     % Handle the missing observations.
0114     [data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data,gend,n_varobs);
0115     missing_value = ~(number_of_observations == gend*n_varobs);
0116     % Determine if a constant is needed.
0117     if options_.steadystate_flag% if the *_steadystate.m file is provided.
0118         [ys,tchek] = feval([M_.fname '_steadystate'],...
0119                            [zeros(M_.exo_nbr,1);...
0120                             oo_.exo_det_steady_state]);
0121         if size(ys,1) < M_.endo_nbr
0122             if length(M_.aux_vars) > 0
0123                 ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
0124                                                             M_.fname,...
0125                                                             zeros(M_.exo_nbr,1),...
0126                                                             oo_.exo_det_steady_state,...
0127                                                             M_.params,...
0128                                                             options_.bytecode);
0129             else
0130                 error([M_.fname '_steadystate.m doesn''t match the model']);
0131             end
0132         end
0133         oo_.steady_state = ys;
0134     else% if the steady state file is not provided.
0135         [dd,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
0136         oo_.steady_state = dd.ys; clear('dd');
0137     end
0138     if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
0139         options_.noconstant = 1;
0140     else
0141         options_.noconstant = 0;
0142     end
0143     load_data = 1;
0144 end
0145 
0146 pshape_original   = bayestopt_.pshape;
0147 bayestopt_.pshape = Inf(size(bayestopt_.pshape));
0148 clear('priordens')%
0149 
0150 [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] ...
0151     = DsgeSmoother(parameters,gend,data,data_index,missing_value);
0152 oo.Smoother.SteadyState = ys;
0153 oo.Smoother.TrendCoeffs = trend_coeff;
0154 if options_.filter_covariance
0155     oo.Smoother.variance = P;
0156 end
0157 i_endo = bayestopt_.smoother_saved_var_list;
0158 if options_.nk ~= 0
0159     oo.FilteredVariablesKStepAhead = ...
0160         aK(options_.filter_step_ahead,i_endo,:);
0161     if ~isempty(PK)
0162         oo.FilteredVariablesKStepAheadVariances = ...
0163             PK(options_.filter_step_ahead,i_endo,i_endo,:);
0164     end
0165     if ~isempty(decomp)
0166         oo.FilteredVariablesShockDecomposition = ...
0167             decomp(options_.filter_step_ahead,i_endo,:,:);
0168     end
0169 end
0170 dr = oo_.dr;
0171 order_var = oo_.dr.order_var;
0172 for i=bayestopt_.smoother_saved_var_list'
0173     i1 = order_var(bayestopt_.smoother_var_list(i));
0174     eval(['oo.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
0175     eval(['oo.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
0176     eval(['oo.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
0177 end
0178 for i=1:M_.exo_nbr
0179     eval(['oo.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
0180 end
0181 
0182 oo.dr = oo_.dr;
0183 
0184 bayestopt_.pshape = pshape_original;
evaluate_smoother

PURPOSE

SYNOPSIS

DESCRIPTION

CROSS-REFERENCE INFORMATION

SOURCE CODE
