107 lines
4.9 KiB
Matlab
107 lines
4.9 KiB
Matlab
function DynareOutput = simul_backward_nonlinear_model(initial_conditions, sample_size, DynareOptions, DynareModel, DynareOutput, innovations)
|
|
|
|
% 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) 2012-2017 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 <http://www.gnu.org/licenses/>.
|
|
|
|
if DynareModel.maximum_lead
|
|
error('simul_backward_nonlinear_model:: The specified model is not backward looking!')
|
|
end
|
|
|
|
if nargin<6
|
|
% Set the covariance matrix of the structural innovations.
|
|
variances = diag(DynareModel.Sigma_e);
|
|
number_of_shocks = length(DynareModel.Sigma_e);
|
|
positive_var_indx = find(variances>0);
|
|
effective_number_of_shocks = length(positive_var_indx);
|
|
covariance_matrix = DynareModel.Sigma_e(positive_var_indx,positive_var_indx);
|
|
covariance_matrix_upper_cholesky = chol(covariance_matrix);
|
|
% Set seed to its default state.
|
|
if DynareOptions.bnlms.set_dynare_seed_to_default
|
|
set_dynare_seed('default');
|
|
end
|
|
% Simulate structural innovations.
|
|
switch DynareOptions.bnlms.innovation_distribution
|
|
case 'gaussian'
|
|
DynareOutput.bnlms.shocks = randn(sample_size,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
|
|
otherwise
|
|
error(['simul_backward_nonlinear_model:: ' DynareOption.bnlms.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
|
|
end
|
|
% Put the simulated innovations in DynareOutput.exo_simul.
|
|
DynareOutput.exo_simul = zeros(sample_size,number_of_shocks);
|
|
DynareOutput.exo_simul(:,positive_var_indx) = DynareOutput.bnlms.shocks;
|
|
DynareOutput.exo_simul = [zeros(1,number_of_shocks); DynareOutput.exo_simul];
|
|
else
|
|
DynareOutput.exo_simul = innovations;
|
|
end
|
|
|
|
% Get usefull vector of indices.
|
|
ny1 = nnz(DynareModel.lead_lag_incidence(1,:));
|
|
iy1 = find(DynareModel.lead_lag_incidence(1,:)>0);
|
|
idx = 1:DynareModel.endo_nbr;
|
|
jdx = idx+ny1;
|
|
hdx = 1:ny1;
|
|
|
|
% Get the name of the dynamic model routine.
|
|
model_dynamic = str2func([DynareModel.fname,'_dynamic']);
|
|
model_dynamic_s = str2func('dynamic_backward_model_for_simulation');
|
|
|
|
% initialization of vector y.
|
|
y = NaN(length(idx)+ny1,1);
|
|
|
|
% initialization of the returned simulations.
|
|
DynareOutput.endo_simul = NaN(DynareModel.endo_nbr,sample_size+1);
|
|
if isempty(initial_conditions)
|
|
if isfield(DynareModel,'endo_histval')
|
|
DynareOutput.endo_simul(:,1:DynareModel.maximum_lag) = DynareModel.endo_histval;
|
|
else
|
|
warning('simul_backward_nonlinear_model:: Initial condition is zero for all variables! If the model is nonlinear, the model simulation may fail with the default initialization')
|
|
DynareOutput.endo_simul(:,1) = 0;
|
|
end
|
|
else
|
|
DynareOutput.endo_simul(:,1) = initial_conditions;
|
|
end
|
|
Y = DynareOutput.endo_simul;
|
|
|
|
% Simulations (call a Newton-like algorithm for each period).
|
|
for it = 2:sample_size+1
|
|
ylag = Y(iy1,it-1); % Set lagged variables.
|
|
y = Y(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables.
|
|
Y(:,it) = dynare_solve(model_dynamic_s, y, DynareOptions, model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
|
|
end
|
|
|
|
DynareOutput.endo_simul = Y; |