adding function evaluate_smoother that calls DsgeSmoother with a given type of parameter set
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function oo = evaluate_smoother(parameters)
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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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%
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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 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_
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persistent load_data
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persistent gend data data_index number_of_observations no_more_missing_observations
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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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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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error
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end
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end
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if isempty(load_data)
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% Get the data.
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n_varobs = size(options_.varobs,1);
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rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
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options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
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gend = options_.nobs;
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rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
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% Transform the data.
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if options_.loglinear
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if ~options_.logdata
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rawdata = log(rawdata);
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end
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end
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% Test if the data set is real.
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if ~isreal(rawdata)
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error('There are complex values in the data! Probably a wrong transformation')
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end
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% Detrend the data.
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options_.missing_data = any(any(isnan(rawdata)));
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if options_.prefilter == 1
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if options_.missing_data
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bayestopt_.mean_varobs = zeros(n_varobs,1);
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for variable=1:n_varobs
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rdx = find(~isnan(rawdata(:,variable)));
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m = mean(rawdata(rdx,variable));
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rawdata(rdx,variable) = rawdata(rdx,variable)-m;
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bayestopt_.mean_varobs(variable) = m;
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end
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else
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bayestopt_.mean_varobs = mean(rawdata,1)';
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rawdata = rawdata-repmat(bayestopt_.mean_varobs',gend,1);
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end
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end
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data = transpose(rawdata);
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% Handle the missing observations.
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[data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data,gend,n_varobs);
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missing_value = ~(number_of_observations == gend*n_varobs);
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% Determine if a constant is needed.
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if options_.steadystate_flag% if the *_steadystate.m file is provided.
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[ys,tchek] = feval([M_.fname '_steadystate'],...
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[zeros(M_.exo_nbr,1);...
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oo_.exo_det_steady_state]);
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if size(ys,1) < M_.endo_nbr
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if length(M_.aux_vars) > 0
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ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
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M_.fname,...
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zeros(M_.exo_nbr,1),...
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oo_.exo_det_steady_state,...
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M_.params,...
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options_.bytecode);
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else
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error([M_.fname '_steadystate.m doesn''t match the model']);
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end
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end
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oo_.steady_state = ys;
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else% if the steady state file is not provided.
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[dd,info] = resol(oo_.steady_state,0);
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oo_.steady_state = dd.ys; clear('dd');
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end
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if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
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options_.noconstant = 1;
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else
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options_.noconstant = 0;
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end
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load_data = 1;
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end
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pshape_original = bayestopt_.pshape;
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bayestopt_.pshape = Inf(size(bayestopt_.pshape));
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clear('priordens')%
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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,gend,data,data_index,number_of_observations);
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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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dr = oo_.dr;
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order_var = oo_.dr.order_var;
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for i=bayestopt_.smoother_saved_var_list'
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i1 = 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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eval(['oo.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
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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
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oo.dr = oo_.dr;
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bayestopt_.pshape = pshape_original;
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