function [ysim, xsim, errorflag] = simul_backward_linear_model_(initialconditions, samplesize, DynareOptions, DynareModel, DynareOutput, innovations, nx, ny1, iy1, jdx, model_dynamic) % 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. % - DynareOptions [struct] Dynare's options_ global structure. % - DynareModel [struct] Dynare's M_ global structure. % - DynareOutput [struct] Dynare's oo_ global structure. % - innovations [double] T*q matrix, innovations to be used for the simulation. % % OUTPUTS % - DynareOutput [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 DynareOutput.exo_simul, and the resulting paths for the endogenous % variables are saved in DynareOutput.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 © 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 . errorflag = false; if ~isempty(innovations) DynareOutput.exo_simul(initialconditions.nobs+(1:samplesize),:) = innovations; end % Get coefficients [cst, jacob] = model_dynamic(zeros(DynareModel.endo_nbr+ny1,1), ... zeros(DynareModel.orig_maximum_lag+1,DynareModel.exo_nbr), ... DynareModel.params, ... DynareOutput.steady_state, DynareModel.orig_maximum_lag+1); try A0inv = inv(jacob(:,jdx)); catch errorflag = true; ysim = []; xsim = []; return end A1 = jacob(:,nonzeros(DynareModel.lead_lag_incidence(1,:))); B = jacob(:,end-nx+1:end); % Simulations for it = initialconditions.nobs+(1:samplesize) DynareOutput.endo_simul(:,it) = -A0inv*(cst + A1*DynareOutput.endo_simul(iy1,it-1) + B*DynareOutput.exo_simul(it,:)'); end ysim = DynareOutput.endo_simul(1:DynareModel.orig_endo_nbr,:); xsim = DynareOutput.exo_simul;