Commit ea86e8ce authored by Michel Juillard's avatar Michel Juillard

adding function evaluate_smoother that calls DsgeSmoother with a given type of parameter set

parent 641f03b8
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 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 load_data
persistent gend data data_index number_of_observations no_more_missing_observations
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;
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''.')
error
end
end
if isempty(load_data)
% Get the data.
n_varobs = size(options_.varobs,1);
rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
% Transform the data.
if options_.loglinear
if ~options_.logdata
rawdata = log(rawdata);
end
end
% Test if the data set is real.
if ~isreal(rawdata)
error('There are complex values in the data! Probably a wrong transformation')
end
% Detrend the data.
options_.missing_data = any(any(isnan(rawdata)));
if options_.prefilter == 1
if options_.missing_data
bayestopt_.mean_varobs = zeros(n_varobs,1);
for variable=1:n_varobs
rdx = find(~isnan(rawdata(:,variable)));
m = mean(rawdata(rdx,variable));
rawdata(rdx,variable) = rawdata(rdx,variable)-m;
bayestopt_.mean_varobs(variable) = m;
end
else
bayestopt_.mean_varobs = mean(rawdata,1)';
rawdata = rawdata-repmat(bayestopt_.mean_varobs',gend,1);
end
end
data = transpose(rawdata);
% Handle the missing observations.
[data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data,gend,n_varobs);
missing_value = ~(number_of_observations == gend*n_varobs);
% Determine if a constant is needed.
if options_.steadystate_flag% if the *_steadystate.m file is provided.
[ys,tchek] = feval([M_.fname '_steadystate'],...
[zeros(M_.exo_nbr,1);...
oo_.exo_det_steady_state]);
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] = resol(oo_.steady_state,0);
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
load_data = 1;
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,gend,data,data_index,number_of_observations);
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;
\ No newline at end of file
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