133 lines
6.4 KiB
Matlab
133 lines
6.4 KiB
Matlab
function Simulations = extended_path_mc(initialconditions, samplesize, replic, exogenousvariables, options_, M_, oo_)
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% Simulations = extended_path_mc(initialconditions, samplesize, replic, exogenousvariables, options_, M_, oo_)
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% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
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% series of size T is obtained by solving T perfect foresight models.
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%
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% INPUTS
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% o initialconditions [double] m*1 array, where m is the number of endogenous variables in the model.
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% o samplesize [integer] scalar, size of the sample to be simulated.
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% o exogenousvariables [double] T*n array, values for the structural innovations.
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% o options_ [struct] Dynare's options structure
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% o M_ [struct] Dynare's model structure
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% o oo_ [struct] Dynare's results structure
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%
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% OUTPUTS
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% o ts [dseries] m*samplesize array, the simulations.
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% o results [cell]
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%
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% ALGORITHM
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%
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% SPECIAL REQUIREMENTS
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% Copyright © 2016-2023 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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[initialconditions, innovations, pfm, ep, ~, options_, oo_] = ...
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extended_path_initialization(initialconditions, samplesize, exogenousvariables, options_, M_, oo_);
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% Check the dimension of the first input argument
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if isequal(size(initialconditions, 2), 1)
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initialconditions = repmat(initialconditions, 1, replic);
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else
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if ~isequal(size(initialconditions, 2), replic)
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error('Wrong size. Number of columns in first argument should match the value of the third argument!')
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end
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end
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% Check the dimension of the fourth input argument
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if isempty(exogenousvariables)
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exogenousvariables = repmat(exogenousvariables, [1, 1, replic]);
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else
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if ~isequal(size(exogenousvariables, 3), replic)
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error('Wrong size. !')
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end
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end
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if ~isequal(size(exogenousvariables, 3), replic)
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error('Wrong dimensions. Fourth argument must be a 3D array with as many pages as the value of the third argument!')
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end
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data = NaN(size(initialconditions, 1), samplesize+1, replic);
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vexo = NaN(innovations.effective_number_of_shocks, samplesize+1, replic);
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info = NaN(replic, 1);
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if ep.parallel
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% Use the Parallel toolbox.
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parfor i=1:replic
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innovations_ = innovations;
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oo__ = oo_;
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[shocks, spfm_exo_simul, innovations_, oo__] = extended_path_shocks(innovations_, ep, exogenousvariables(:,:,i), samplesize, M_, options_, oo__);
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endogenous_variables_paths = NaN(M_.endo_nbr,samplesize+1);
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endogenous_variables_paths(:,1) = initialconditions(:,1);
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exogenous_variables_paths = NaN(innovations_.effective_number_of_shocks,samplesize+1);
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exogenous_variables_paths(:,1) = 0;
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info_convergence = true;
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t = 1;
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while t<=samplesize
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t = t+1;
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spfm_exo_simul(2,:) = shocks(t-1,:);
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exogenous_variables_paths(:,t) = shocks(t-1,:);
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[endogenous_variables_paths(:,t), info_convergence] = extended_path_core(ep.periods, M_.endo_nbr, M_.exo_nbr, innovations_.positive_var_indx, ...
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spfm_exo_simul, ep.init, endogenous_variables_paths(:,t-1), ...
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oo__.steady_state, ...
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ep.verbosity, ep.stochastic.order, ...
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M_, pfm,ep.stochastic.algo, ep.solve_algo, ep.stack_solve_algo, ...
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options_.lmmcp, options_, oo__);
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if ~info_convergence
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msg = sprintf('No convergence of the (stochastic) perfect foresight solver (in period %s, iteration %s)!', int2str(t), int2str(i));
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warning(msg)
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break
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end
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end % Loop over t
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info(i) = info_convergence;
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vexo(:,:,i) = exogenous_variables_paths;
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data(:,:,i) = endogenous_variables_paths;
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end
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else
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% Sequential approach.
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for i=1:replic
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[shocks, spfm_exo_simul, innovations, oo_] = extended_path_shocks(innovations, ep, exogenousvariables(:,:,i), samplesize, M_, options_, oo_);
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endogenous_variables_paths = NaN(M_.endo_nbr,samplesize+1);
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endogenous_variables_paths(:,1) = initialconditions(:,1);
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exogenous_variables_paths = NaN(innovations.effective_number_of_shocks,samplesize+1);
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exogenous_variables_paths(:,1) = 0;
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t = 1;
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while t<=samplesize
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t = t+1;
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spfm_exo_simul(2,:) = shocks(t-1,:);
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exogenous_variables_paths(:,t) = shocks(t-1,:);
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[endogenous_variables_paths(:,t), info_convergence] = extended_path_core(ep.periods, M_.endo_nbr, M_.exo_nbr, innovations.positive_var_indx, ...
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spfm_exo_simul, ep.init, endogenous_variables_paths(:,t-1), ...
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oo_.steady_state, ...
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ep.verbosity, ep.stochastic.order, ...
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M_, pfm,ep.stochastic.algo, ep.solve_algo, ep.stack_solve_algo, ...
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options_.lmmcp, options_, oo_);
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if ~info_convergence
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msg = sprintf('No convergence of the (stochastic) perfect foresight solver (in period %s, iteration %s)!', int2str(t), int2str(i));
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warning(msg)
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break
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end
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end % Loop over t
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info(i) = info_convergence;
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vexo(:,:,i) = exogenous_variables_paths;
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data(:,:,i) = endogenous_variables_paths;
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end % Loop over i
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end
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Simulations.innovations = vexo;
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Simulations.data = data;
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Simulations.info = info;
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