132 lines
4.8 KiB
Matlab
132 lines
4.8 KiB
Matlab
function forecasts = backward_model_forecast(initialcondition, listofvariables, periods, withuncertainty)
|
|
|
|
% Returns unconditional forecasts.
|
|
%
|
|
% INPUTS
|
|
% - initialcondition [dseries] Initial conditions for the endogenous variables.
|
|
% - periods [integer] scalar, the number of (forecast) periods.
|
|
% - withuncertainty [logical] scalar, returns confidence bands if true.
|
|
%
|
|
% OUTPUTS
|
|
% - forecast [dseries]
|
|
|
|
% Copyright © 2017-2023 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 <https://www.gnu.org/licenses/>.
|
|
|
|
global M_ options_ oo_
|
|
|
|
% Check that the model is actually backward
|
|
if M_.maximum_lead
|
|
error(['backward_model_forecast:: The specified model is not backward looking!'])
|
|
end
|
|
|
|
% Initialize returned argument.
|
|
forecasts = struct();
|
|
|
|
% Set defaults.
|
|
if nargin<2
|
|
listofvariables = M_.endo_names;
|
|
periods = 8;
|
|
withuncertainty = false;
|
|
end
|
|
|
|
if nargin<3
|
|
periods = 8;
|
|
withuncertainty = false;
|
|
end
|
|
|
|
if nargin<4
|
|
withuncertainty = false;
|
|
end
|
|
|
|
start = initialcondition.dates(end)+1;
|
|
|
|
% Set default initial conditions for the innovations.
|
|
for i=1:M_.exo_nbr
|
|
if ~ismember(M_.exo_names{i}, initialcondition.name)
|
|
initialcondition{M_.exo_names{i}} = dseries(zeros(initialcondition.nobs, 1), initialcondition.dates(1), M_.exo_names{i});
|
|
end
|
|
end
|
|
|
|
% Set up initial conditions
|
|
[initialcondition, periods, innovations, DynareOptions, DynareModel, DynareOutput, endonames, exonames, nx, ny1, iy1, jdx, model_dynamic, y] = ...
|
|
simul_backward_model_init(initialcondition, periods, options_, M_, oo_, zeros(periods, M_.exo_nbr));
|
|
|
|
% Get vector of indices for the selected endogenous variables.
|
|
n = length(listofvariables);
|
|
idy = zeros(n,1);
|
|
for i=1:n
|
|
j = find(strcmp(listofvariables{i}, endonames));
|
|
if isempty(j)
|
|
error('backward_model_forecast:: Variable %s is unknown!', listofvariables{i})
|
|
else
|
|
idy(i) = j;
|
|
end
|
|
end
|
|
|
|
% Set the number of simulations (if required).
|
|
if withuncertainty
|
|
B = 1000;
|
|
end
|
|
|
|
% Get the covariance matrix of the shocks.
|
|
if withuncertainty
|
|
Sigma = M_.Sigma_e + 1e-14*eye(M_.exo_nbr);
|
|
sigma = transpose(chol(Sigma));
|
|
end
|
|
|
|
% Compute forecast without shock
|
|
if options_.linear
|
|
[ysim__0, errorflag] = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, nx, ny1, iy1, jdx, model_dynamic);
|
|
else
|
|
[ysim__0, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, iy1, model_dynamic);
|
|
end
|
|
|
|
if errorflag
|
|
error('Simulation failed.')
|
|
end
|
|
|
|
forecasts.pointforecast = dseries(transpose(ysim__0(idy,:)), initialcondition.init, listofvariables);
|
|
|
|
% Set first period of forecast
|
|
forecasts.start = start;
|
|
|
|
if withuncertainty
|
|
% Preallocate an array gathering the simulations.
|
|
ArrayOfForecasts = zeros(n, periods+initialcondition.nobs, B);
|
|
for i=1:B
|
|
innovations = transpose(sigma*randn(M_.exo_nbr, periods));
|
|
if options_.linear
|
|
[ysim__, xsim__, errorflag] = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, nx, ny1, iy1, jdx, model_dynamic);
|
|
else
|
|
[ysim__, xsim__, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, iy1, model_dynamic);
|
|
end
|
|
if errorflag
|
|
error('Simulation failed.')
|
|
end
|
|
ArrayOfForecasts(:,:,i) = ysim__(idy,:);
|
|
end
|
|
% Compute mean (over future uncertainty) forecast.
|
|
forecasts.meanforecast = dseries(transpose(mean(ArrayOfForecasts, 3)), initialcondition.init, listofvariables);
|
|
forecasts.medianforecast = dseries(transpose(median(ArrayOfForecasts, 3)), initialcondition.init, listofvariables);
|
|
forecasts.stdforecast = dseries(transpose(std(ArrayOfForecasts, 1,3)), initialcondition.init, listofvariables);
|
|
% Compute lower and upper 95% confidence bands
|
|
ArrayOfForecasts = sort(ArrayOfForecasts, 3);
|
|
forecasts.lb = dseries(transpose(ArrayOfForecasts(:,:,round(0.025*B))), initialcondition.init, listofvariables);
|
|
forecasts.ub = dseries(transpose(ArrayOfForecasts(:,:,round(0.975*B))), initialcondition.init, listofvariables);
|
|
end
|