139 lines
4.2 KiB
Matlab
139 lines
4.2 KiB
Matlab
function info = dyn_forecast(var_list,task)
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% function dyn_forecast(var_list,task)
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% computes mean forecast for a given value of the parameters
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% computes also confidence band for the forecast
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%
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% INPUTS
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% var_list: list of variables (character matrix)
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% task: indicates how to initialize the forecast
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% either 'simul' or 'smoother'
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% OUTPUTS
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% nothing is returned but the procedure saves output
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% in oo_.forecast.Mean
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% oo_.forecast.HPDinf
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% oo_.forecast.HPDsup
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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_
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info = 0;
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maximum_lag = M_.maximum_lag;
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endo_names = M_.endo_names;
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if isempty(var_list)
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var_list = endo_names(1:M_.orig_endo_nbr, :);
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end
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i_var = [];
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for i = 1:size(var_list)
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tmp = strmatch(var_list(i,:),endo_names,'exact');
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if isempty(tmp)
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error([var_list(i,:) ' isn''t and endogenous variable'])
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end
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i_var = [i_var; tmp];
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end
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n_var = length(i_var);
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trend = 0;
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switch task
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case 'simul'
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horizon = options_.periods;
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if horizon == 0
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horizon = 5;
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end
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if isempty(M_.endo_histval)
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y0 = repmat(oo_.steady_state,1,maximum_lag);
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else
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y0 = M_.endo_histval;
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end
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case 'smoother'
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horizon = options_.forecast;
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y_smoothed = oo_.SmoothedVariables;
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y0 = zeros(M_.endo_nbr,maximum_lag);
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for i = 1:M_.endo_nbr
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v_name = deblank(M_.endo_names(i,:));
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y0(i,:) = y_smoothed.(v_name)(end-maximum_lag+1:end)+oo_.dr.ys(i);
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end
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gend = options_.nobs;
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if isfield(oo_.Smoother,'TrendCoeffs')
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var_obs = options_.varobs;
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endo_names = M_.endo_names;
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order_var = oo_.dr.order_var;
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i_var_obs = [];
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trend_coeffs = [];
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for i=1:size(var_obs,1)
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tmp = strmatch(var_obs(i,:),endo_names(i_var,:),'exact');
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if ~isempty(tmp)
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i_var_obs = [ i_var_obs; tmp];
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trend_coeffs = [trend_coeffs; oo_.Smoother.TrendCoeffs(i)];
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end
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end
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if ~isempty(trend_coeffs)
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trend = trend_coeffs*(gend+(1-M_.maximum_lag:horizon));
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end
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end
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global bayestopt_
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if isfield(bayestopt_,'mean_varobs')
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trend = trend + repmat(bayestopt_.mean_varobs,1,horizon+M_.maximum_lag);
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end
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otherwise
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error('Wrong flag value')
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end
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if M_.exo_det_nbr == 0
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[yf,int_width] = forcst(oo_.dr,y0,horizon,var_list);
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else
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exo_det_length = size(oo_.exo_det_simul,1)-M_.maximum_lag;
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if horizon > exo_det_length
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ex = zeros(horizon,M_.exo_nbr);
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oo_.exo_det_simul = [ oo_.exo_det_simul;...
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repmat(oo_.exo_det_steady_state',...
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horizon- ...
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exo_det_length,1)];
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elseif horizon < exo_det_length
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ex = zeros(exo_det_length,M_.exo_nbr);
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end
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[yf,int_width] = simultxdet(y0,ex,oo_.exo_det_simul,...
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options_.order,var_list,M_,oo_,options_);
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end
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if ~isscalar(trend)
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yf(i_var_obs,:) = yf(i_var_obs,:) + trend;
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end
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for i=1:n_var
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eval(['oo_.forecast.Mean.' var_list(i,:) '= yf(' int2str(i) ',maximum_lag+1:end)'';']);
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eval(['oo_.forecast.HPDinf.' var_list(i,:) '= yf(' int2str(i) ',maximum_lag+1:end)''-' ...
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' int_width(:,' int2str(i) ');']);
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eval(['oo_.forecast.HPDsup.' var_list(i,:) '= yf(' int2str(i) ',maximum_lag+1:end)''+' ...
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' int_width(:,' int2str(i) ');']);
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end
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for i=1:M_.exo_det_nbr
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eval(['oo_.forecast.Exogenous.' M_.exo_det_names(i,:) '= oo_.exo_det_simul(:,' int2str(i) ');']);
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end
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if options_.nograph == 0
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forecast_graphs(var_list);
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end
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