81 lines
3.6 KiB
Matlab
81 lines
3.6 KiB
Matlab
function DynareOutput = simul_backward_linear_model(varargin)
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% Simulates a stochastic linear backward looking model.
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%
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% INPUTS
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% - initialconditions [double] n*1 vector, initial conditions for the endogenous variables.
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% - samplesize [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 (C) 2012-2017 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 <http://www.gnu.org/licenses/>.
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[initialconditions, samplesize, innovations, DynareOptions, DynareModel, DynareOutput, nx, ny1, iy1, jdx, model_dynamic, y] = ...
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simul_backward_model_init(varargin{:});
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% initialization of the returned simulations.
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DynareOutput.endo_simul = NaN(DynareModel.endo_nbr,samplesize);
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if isempty(initialconditions)
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if isfield(DynareModel,'endo_histval') && ~isempty(DynareModel.endo_histval)
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DynareOutput.endo_simul = [DynareModel.endo_histval, DynareOutput.endo_simul];
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else
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DynareOutput.endo_simul = [zeros(DynareModel.endo_nbr, DynareModel.max_lag_orig), DynareOutput.endo_simul];
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end
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else
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if ~isequal(size(initialconditions, 2), DynareModel.max_lag_orig)
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error(['simul_backward_linear_model:: First argument should have %s columns!'], DynareModel.max_lag_orig)
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end
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DynareOutput.endo_simul = [initialconditions, DynareOutput.endo_simul];
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end
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Y = DynareOutput.endo_simul;
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if ~DynareModel.max_exo_lag_orig
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if DynareModel.max_endo_lag_orig>1
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DynareOutput.exo_simul = [ zeros(DynareModel.max_endo_lag_orig-1, DynareModel.exo_nbr); DynareOutput.exo_simul];
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end
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end
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% Get coefficients
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[cst, jacob] = model_dynamic(zeros(DynareModel.endo_nbr+ny1,1), ...
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zeros(DynareModel.max_lag_orig+1,DynareModel.exo_nbr), ...
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DynareModel.params, ...
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DynareOutput.steady_state, DynareModel.max_lag_orig+1);
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A0inv = inv(jacob(:,jdx));
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A1 = jacob(:,nonzeros(DynareModel.lead_lag_incidence(1,:)));
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B = jacob(:,end-nx+1:end);
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% Simulations
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for it = DynareModel.max_lag_orig+(1:samplesize)
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Y(:,it) = -A0inv*(cst + A1*Y(iy1,it-1) + B*DynareOutput.exo_simul(it,:)');
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end
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DynareOutput.endo_simul = Y; |