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