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function [simulations, errorflag] = simul_backward_linear_model(initialconditions, samplesize, options_, M_, oo_, innovations)
% [simulations, errorflag] = simul_backward_linear_model(initialconditions, samplesize, options_, M_, oo_, innovations)
% Simulates a stochastic linear backward looking model.
%
% INPUTS
% - initialconditions [dseries] initial conditions for the endogenous variables.
% - samplesize [integer] scalar, number of periods for the simulation.
% - options_ [struct] Dynare's options_ global structure.
% - M_ [struct] Dynare's M_ global structure.
% - oo_ [struct] Dynare's oo_ global structure.
% - innovations [double] T*q matrix, innovations to be used for the simulation.
%
% OUTPUTS
% - oo_ [struct] Dynare's oo_ global structure.
% - errorflag [logical] scalar, equal to false iff the simulation did not fail.
%
% REMARKS
% [1] The innovations used for the simulation are saved in oo_.exo_simul, and the resulting paths for the endogenous
% variables are saved in oo_.endo_simul.
% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
% through the shocks block.
% [3] If the first input argument is empty, the endogenous variables are initialized with 0, or if available with the informations
% provided thrtough the histval block.
% Copyright © 2012-2023 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/>.
if M_.maximum_lead
error('Model defined in %s.mod is not backward.', M_.fname)
end
if ~M_.maximum_lag
error('Model defined in %s.mod is not backward.', M_.fname)
end
if nargin<6
innovations = [];
end
[initialconditions, samplesize, innovations, options_, M_, oo_, endonames, exonames, dynamic_resid, dynamic_g1] = ...
simul_backward_model_init(initialconditions, samplesize, options_, M_, oo_, innovations);
[ysim, xsim, errorflag] = simul_backward_linear_model_(initialconditions, samplesize, options_, M_, oo_, innovations, dynamic_resid, dynamic_g1);
simulations = [dseries(ysim', initialconditions.init, endonames(1:M_.orig_endo_nbr)), dseries(xsim, initialconditions.init, exonames)];
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