123 lines
4.6 KiB
Matlab
123 lines
4.6 KiB
Matlab
function forecasts = backward_model_forecast(initialcondition, listofvariables, periods, withuncertainty)
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% Returns unconditional forecasts.
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%
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% INPUTS
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% - initialcondition [dseries] Initial conditions for the endogenous variables.
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% - periods [integer] scalar, the number of (forecast) periods.
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% - withuncertainty [logical] scalar, returns confidence bands if true.
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%
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% OUTPUTS
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% - forecast [dseries]
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% Copyright © 2017-2018 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 <https://www.gnu.org/licenses/>.
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global M_ options_ oo_
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% Check that the model is actually backward
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if M_.maximum_lead
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error(['backward_model_forecast:: The specified model is not backward looking!'])
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end
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% Initialize returned argument.
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forecasts = struct();
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% Set defaults.
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if nargin<2
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listofvariables = M_.endo_names;
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periods = 8;
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withuncertainty = false;
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end
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if nargin<3
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periods = 8;
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withuncertainty = false;
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end
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if nargin<4
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withuncertainty = false;
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end
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start = initialcondition.dates(end)+1;
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% Set default initial conditions for the innovations.
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for i=1:M_.exo_nbr
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if ~ismember(M_.exo_names{i}, initialcondition.name)
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initialcondition{M_.exo_names{i}} = dseries(zeros(initialcondition.nobs, 1), initialcondition.dates(1), M_.exo_names{i});
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end
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end
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% Set up initial conditions
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[initialcondition, periods, innovations, DynareOptions, DynareModel, DynareOutput, endonames, exonames, nx, ny1, iy1, jdx, model_dynamic, y] = ...
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simul_backward_model_init(initialcondition, periods, options_, M_, oo_, zeros(periods, M_.exo_nbr));
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% Get vector of indices for the selected endogenous variables.
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n = length(listofvariables);
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idy = zeros(n,1);
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for i=1:n
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j = find(strcmp(listofvariables{i}, endonames));
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if isempty(j)
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error('backward_model_forecast:: Variable %s is unknown!', listofvariables{i})
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else
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idy(i) = j;
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end
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end
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% Set the number of simulations (if required).
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if withuncertainty
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B = 1000;
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end
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% Get the covariance matrix of the shocks.
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if withuncertainty
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Sigma = M_.Sigma_e + 1e-14*eye(M_.exo_nbr);
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sigma = transpose(chol(Sigma));
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end
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% Compute forecast without shock
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if options_.linear
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ysim__0 = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, nx, ny1, iy1, jdx, model_dynamic);
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else
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ysim__0 = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, iy1, model_dynamic);
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end
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forecasts.pointforecast = dseries(transpose(ysim__0(idy,:)), initialcondition.init, listofvariables);
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% Set first period of forecast
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forecasts.start = start;
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if withuncertainty
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% Preallocate an array gathering the simulations.
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ArrayOfForecasts = zeros(n, periods+initialcondition.nobs, B);
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for i=1:B
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innovations = transpose(sigma*randn(M_.exo_nbr, periods));
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if options_.linear
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[ysim__, xsim__] = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, nx, ny1, iy1, jdx, model_dynamic);
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else
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[ysim__, xsim__] = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, iy1, model_dynamic);
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end
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ArrayOfForecasts(:,:,i) = ysim__(idy,:);
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end
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% Compute mean (over future uncertainty) forecast.
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forecasts.meanforecast = dseries(transpose(mean(ArrayOfForecasts, 3)), initialcondition.init, listofvariables);
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forecasts.medianforecast = dseries(transpose(median(ArrayOfForecasts, 3)), initialcondition.init, listofvariables);
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forecasts.stdforecast = dseries(transpose(std(ArrayOfForecasts, 1,3)), initialcondition.init, listofvariables);
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% Compute lower and upper 95% confidence bands
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ArrayOfForecasts = sort(ArrayOfForecasts, 3);
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forecasts.lb = dseries(transpose(ArrayOfForecasts(:,:,round(0.025*B))), initialcondition.init, listofvariables);
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forecasts.ub = dseries(transpose(ArrayOfForecasts(:,:,round(0.975*B))), initialcondition.init, listofvariables);
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end |