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

matlab/evaluate_smoother.m

+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;
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