Added routine for computing unconditional forecasts of a backward looking model.

matlab/backward/backward_model_forecast.m

@@ -0,0 +1,106 @@
+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 (C) 2017 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 M_ options_ oo_
+
+% Check that the model is actually backward
+if M_.maximum_lead
+    error(['backward_model_irf:: The specified model is not backward looking!'])
+end
+
+% Initialize returned argument.
+forecasts = struct();
+
+% Set defaults.
+if nargin<2
+    listofvariables = cellstr(M_.endo_names);
+    periods = 8;
+    withuncertainty = false;
+end
+
+if nargin<3
+    periods = 8;
+    withuncertainty = false;
+end
+
+if nargin<4
+    withuncertainty = false;
+end
+
+% Get full list of endogenous variables
+endo_names = cellstr(M_.endo_names);
+
+% Get vector of indices for the selected endogenous variables.
+n = length(listofvariables);
+idy = zeros(n,1);
+for i=1:n
+    j = strmatch(listofvariables{i}, endo_names, 'exact');
+    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
+
+% Put initial conditions in a vector of doubles
+initialconditions = transpose(initialcondition{endo_names{:}}.data);
+
+% Compute forecast without shock 
+innovations = zeros(periods+1, M_.exo_nbr);
+oo__0 = simul_backward_model(initialconditions, periods, options_, M_, oo_, innovations);
+forecasts.pointforecast = dseries(transpose(oo__0.endo_simul(idy,:)), initialcondition.init, listofvariables);
+
+if withuncertainty
+    % Preallocate an array gathering the simulations.
+    ArrayOfForectasts = zeros(n, periods+1, B);
+    for i=1:B
+        innovations(2:end,:) = transpose(sigma*randn(M_.exo_nbr, periods));
+        oo__ = simul_backward_model(initialconditions, periods, options_, M_, oo_, innovations);     
+        ArrayOfForecasts(:,:,i) = oo__.endo_simul(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