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function [simulation, errorflag] = simul_backward_model(initialconditions, samplesize, innovations)
% function simulation = simul_backward_model(initialconditions, samplesize, innovations)
% Simulates a stochastic backward looking model (with arbitrary precision).
%
% INPUTS
% - initialconditions [dseries] initial conditions for the endogenous variables.
% - samplesize [integer] scalar, number of periods for the simulation.
% - innovations [dseries] innovations to be used for the simulation.
%
% OUTPUTS
% - simulation [dseries] Simulated endogenous and exogenous variables.
% - errorflag [logical] scalar, equal to false iff the simulation did not fail.
%
% REMARKS
% [1] The innovations used for the simulation are saved in oo_.exo_simul, and the resulting paths for the endogenous
% variables are saved in oo_.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 through the histval block.
% Copyright © 2012-2023 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 <https://www.gnu.org/licenses/>.
global options_ M_ oo_
if M_.maximum_lead
error('Model defined in %s.mod is not backward or static.', M_.fname)
end
if ~M_.maximum_lag
dprintf('Model defined in %s.mod is static. Use simul_static_model instead.', M_.fname)
simul_static_model(samplesize, innovations);
return
end
if nargin<3
Innovations = [];
else
if isdseries(innovations)
if isdseries(initialconditions)
if isequal(innovations.dates(1)-1, initialconditions.dates(end))
if innovations.nobs<samplesize
error('Time span in third argument is too short (should not be less than %s, the value of the second argument)', num2str(samplesize))
end
% Set array holding innovations values.
Innovations = zeros(samplesize, M_.exo_nbr);
exonames = M_.exo_names;
for i=1:M_.exo_nbr
if ismember(exonames{i}, innovations.name)
Innovations(:,i) = innovations{exonames{i}}.data(1:samplesize);
else
dprintf('Exogenous variable %s is not available in third argument, default value is zero.', exonames{i})
end
end
else
error('Time spans in first and third arguments should be contiguous!')
end
else
if isempty(initialconditions)
if innovations.nobs<samplesize
error('Time span in third argument is too short (should not be less than %s, the value of the second argument)', num2str(samplesize))
end
Innovations = zeros(samplesize, M_.exo_nbr);
exonames = M_.exo_names;
for i=1:M_.exo_nbr
if ismember(exonames{i}, innovations.name)
Innovations(:,i) = innovations{exonames{i}}.data(1:samplesize);
else
dprintf('Exogenous variable %s is not available in third argument, default value is zero.', exonames{i})
end
end
else
error('First input must be an empty array!')
end
end
else
error('Third argument must be a dseries object!')
end
end
if options_.linear
[simulation, errorflag] = simul_backward_linear_model(initialconditions, samplesize, options_, M_, oo_, Innovations);
else
[simulation, errorflag] = simul_backward_nonlinear_model(initialconditions, samplesize, options_, M_, oo_, Innovations);
end
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