207 lines
7.3 KiB
Matlab
207 lines
7.3 KiB
Matlab
function dynare_estimation(var_list,dname)
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% function dynare_estimation(var_list)
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% runs the estimation of the model
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%
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% INPUTS
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% var_list: selected endogenous variables vector
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%
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% OUTPUTS
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% none
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2013 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_ oo_ M_ oo_recursive_
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%% Decide if a DSGE or DSGE-VAR has to be estimated.
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if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
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options_.dsge_var = 1;
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end
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var_list = check_list_of_variables(options_, M_, var_list);
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options_.varlist = var_list;
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if isfield(options_,'nobs')
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nobs = sort(options_.nobs);
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else
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nobs = [];
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end
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nnobs = length(nobs);
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horizon = options_.forecast;
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if nargin<2 || ~exist(dname) || isempty(dname)
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dname = M_.fname;
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end
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M_.dname = dname;
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if options_.mode_compute && options_.analytic_derivation,
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analytic_derivation0=options_.analytic_derivation;
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options_.analytic_derivation=1;
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end
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if nnobs > 1
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for i=1:nnobs
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options_.nobs = nobs(i);
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M_.dname = [dname '_' int2str(nobs(i))];
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dynare_estimation_1(var_list,M_.dname);
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if isequal(i,1)
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options_.mode_file = [M_.fname '_mode'];
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end
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if options_.recursive_estimation_restart
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for j=1:options_.recursive_estimation_restart
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dynare_estimation_1(var_list,M_.dname);
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end
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end
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oo_recursive_{nobs(i)} = oo_;
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end
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else
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dynare_estimation_1(var_list,dname);
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end
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if options_.mode_compute && options_.analytic_derivation,
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options_.analytic_derivation=analytic_derivation0;
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end
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if nnobs > 1 && horizon > 0
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mh_replic = options_.mh_replic;
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rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
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gend = options_.nobs;
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data_plot_end_point=min(options_.first_obs+gend-1+horizon,size(rawdata,1)); %compute last observation that can be plotted
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rawdata = rawdata(options_.first_obs:data_plot_end_point,:);
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% Take the log of the variables if needed
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if options_.loglinear && ~options_.logdata % and if the data are not in logs, then...
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rawdata = log(rawdata);
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end
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endo_names = M_.endo_names;
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n_varobs = size(options_.varobs,1);
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if isempty(var_list)
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var_list = endo_names;
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nvar = size(endo_names,1);
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SelecVariables = transpose(1:nvar);
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else
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nvar = size(var_list,1);
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SelecVariables = [];
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for i=1:nvar
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if ~isempty(strmatch(var_list(i,:),endo_names,'exact'))
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SelecVariables = [SelecVariables;strmatch(var_list(i,:),endo_names, ...
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'exact')];
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else
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error(['Estimation: ' var_list(i,:) ' isn''t an endogenous' ...
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'variable'])
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end
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end
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end
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IdObs = zeros(n_varobs,1);
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for j=1:n_varobs
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iobs = strmatch(options_.varobs(j,:),var_list,'exact');
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if ~isempty(iobs)
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IdObs(j,1) = iobs;
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end
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end
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time_offset=min(3,gend-1); %for observables, plot 3 previous periods unless data is shorter
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k = time_offset+min(nobs(end)-nobs(1)+horizon, ...
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size(rawdata,1)-nobs(1));
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data2 = rawdata(end-k+1:end,:);
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[nbplt,nr,nc,lr,lc,nstar] = pltorg(nvar);
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m = 1;
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plot_index=0;
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OutputDirectoryName = CheckPath('graphs',M_.fname);
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for i = 1:size(var_list,1)
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if mod(i,nstar) == 1
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plot_index=plot_index+1;
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hfig = dyn_figure(options_,'Name',['Out of sample forecasts (',num2str(plot_index),')']);
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m = 1;
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end
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subplot(nr,nc,m)
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hold on
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if any(i==IdObs)
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k2 = find(i==IdObs);
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offsetx = 3;
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plot(nobs(1)-offsetx+1:nobs(1)-offsetx+k,data2(end-k+1:end,k2)','-k','linewidth',2);
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else
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offsetx = 0;
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end
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vname = deblank(var_list(i,:));
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maxlag = M_.maximum_lag;
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for j=1:nnobs
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if mh_replic > 0
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eval(['oo_.RecursiveForecast.Mean.' vname '(j,:) =' ...
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'oo_recursive_{' int2str(nobs(j)) '}.MeanForecast.Mean.' ...
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vname '(maxlag+1:end);']);
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eval(['oo_.RecursiveForecast.HPDinf.' vname '(j,:) =' ...
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'oo_recursive_{' int2str(nobs(j)) '}.MeanForecast.HPDinf.' ...
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vname '(maxlag+1:end);']);
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eval(['oo_.RecursiveForecast.HPDsup.' vname '(j,:) =' ...
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'oo_recursive_{' int2str(nobs(j)) '}.MeanForecast.HPDsup.' ...
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vname '(maxlag+1:end);']);
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eval(['oo_.RecursiveForecast.HPDTotalinf.' vname '(j,:) =' ...
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'oo_recursive_{' int2str(nobs(j)) '}.PointForecast.HPDinf.' ...
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vname '(maxlag+1:end);']);
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eval(['oo_.RecursiveForecast.HPDTotalsup.' vname '(j,:) =' ...
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'oo_recursive_{' int2str(nobs(j)) '}.PointForecast.HPDsup.' ...
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vname '(maxlag+1:end);']);
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else
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eval(['oo_.RecursiveForecast.Mean.' vname '(j,:) =' ...
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'oo_recursive_{' int2str(nobs(j)) '}.forecast.Mean.' ...
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vname ';']);
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eval(['oo_.RecursiveForecast.HPDinf.' vname '(j,:) =' ...
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'oo_recursive_{' int2str(nobs(j)) '}.forecast.HPDinf.' ...
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vname ';']);
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eval(['oo_.RecursiveForecast.HPDsup.' vname '(j,:) =' ...
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'oo_recursive_{' int2str(nobs(j)) '}.forecast.HPDsup.' ...
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vname ';']);
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end
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x = nobs(1)+nobs(j)-nobs(1)+(1:horizon);
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y = eval(['oo_.RecursiveForecast.Mean.' vname '(j,:)']);
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y1 = eval(['oo_.RecursiveForecast.HPDinf.' vname '(j,:)']);
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y2 = eval(['oo_.RecursiveForecast.HPDsup.' vname '(j,:)']);
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plot(x,y,'-b','linewidth',2)
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plot(x,y1,'--g', ...
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'linewidth',1.5)
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plot(x,y2,'--g', ...
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'linewidth',1.5)
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if mh_replic
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y3 = eval(['oo_.RecursiveForecast.HPDTotalinf.' vname '(j,:)']);
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y4 = eval(['oo_.RecursiveForecast.HPDTotalsup.' vname ...
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'(j,:)']);
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plot(x,y3,'--r', ...
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'linewidth',1.5)
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plot(x,y4,'--r','linewidth',1.5)
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end
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end
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box on
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title(vname,'Interpreter','none')
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hold off
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xlim([nobs(1)-offsetx nobs(end)+horizon])
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m = m + 1;
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if mod(i+1,nstar) == 1 || i ==size(var_list,1)
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dyn_saveas(hfig,[M_.fname,filesep,'graphs',filesep M_.fname '_RecursiveForecasts_' int2str(plot_index)],options_);
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end
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end
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end
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