function [ysim, xsim] = simul_backward_nonlinear_model_(initialconditions, samplesize, DynareOptions, DynareModel, DynareOutput, innovations, iy1, model_dynamic) % Simulates a stochastic non linear backward looking model with arbitrary precision (a deterministic solver is used). % % INPUTS % - initial_conditions [double] n*1 vector, initial conditions for the endogenous variables. % - sample_size [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. % % 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 (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 . model_dynamic_s = str2func('dynamic_backward_model_for_simulation'); if ~isempty(innovations) DynareOutput.exo_simul(initialconditions.nobs+(1:samplesize),:) = innovations; end % Simulations (call a Newton-like algorithm for each period). for it = initialconditions.nobs+(1:samplesize) ylag = DynareOutput.endo_simul(iy1,it-1); % Set lagged variables. y = DynareOutput.endo_simul(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables. [DynareOutput.endo_simul(:,it), info, fvec] = dynare_solve(model_dynamic_s, ... y, DynareOptions, model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it); if info error('Newton failed!') end end ysim = DynareOutput.endo_simul(1:DynareModel.orig_endo_nbr,:); xsim = DynareOutput.exo_simul;