79 lines
3.7 KiB
Matlab
79 lines
3.7 KiB
Matlab
function [ysim, xsim] = simul_backward_nonlinear_model_(initialconditions, samplesize, DynareOptions, DynareModel, DynareOutput, innovations, iy1, model_dynamic)
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% Simulates a stochastic non linear backward looking model with arbitrary precision (a deterministic solver is used).
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%
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% INPUTS
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% - initial_conditions [double] n*1 vector, initial conditions for the endogenous variables.
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% - sample_size [integer] scalar, number of periods for the simulation.
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% - DynareOptions [struct] Dynare's options_ global structure.
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% - DynareModel [struct] Dynare's M_ global structure.
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% - DynareOutput [struct] Dynare's oo_ global structure.
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% - innovations [double] T*q matrix, innovations to be used for the simulation.
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%
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% OUTPUTS
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% - DynareOutput [struct] Dynare's oo_ global structure.
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%
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% REMARKS
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% [1] The innovations used for the simulation are saved in DynareOutput.exo_simul, and the resulting paths for the endogenous
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% variables are saved in DynareOutput.endo_simul.
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% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
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% through the shocks block.
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% [3] If the first input argument is empty, the endogenous variables are initialized with 0, or if available with the informations
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% provided thrtough the histval block.
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% Copyright © 2017-2020 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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debug = false;
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model_dynamic_s = str2func('dynamic_backward_model_for_simulation');
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if ~isempty(innovations)
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DynareOutput.exo_simul(initialconditions.nobs+(1:samplesize),:) = innovations;
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end
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% Simulations (call a Newton-like algorithm for each period).
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for it = initialconditions.nobs+(1:samplesize)
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if debug
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dprintf('Période t = %s.', num2str(it-initialconditions.nobs));
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end
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y_ = DynareOutput.endo_simul(:,it-1);
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ylag = y_(iy1); % Set lagged variables.
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y = y_; % A good guess for the initial conditions is the previous values for the endogenous variables.
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try
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if ismember(DynareOptions.solve_algo, [12,14])
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[DynareOutput.endo_simul(:,it), info] = ...
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dynare_solve(model_dynamic_s, y, DynareOptions, ...
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DynareModel.isloggedlhs, DynareModel.isauxdiffloggedrhs, DynareModel.endo_names, DynareModel.lhs, ...
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model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
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else
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[DynareOutput.endo_simul(:,it), info] = ...
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dynare_solve(model_dynamic_s, y, DynareOptions, ...
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model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
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end
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if info
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error('Newton failed!')
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end
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catch
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DynareOutput.endo_simul(:, 1:it-1);
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dprintf('Newton failed on iteration i = %s.', num2str(it-initialconditions.nobs));
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break
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end
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end
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ysim = DynareOutput.endo_simul(1:DynareModel.orig_endo_nbr,:);
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xsim = DynareOutput.exo_simul; |