109 lines
4.6 KiB
Matlab
109 lines
4.6 KiB
Matlab
function [ts,oo_] = extended_path(initialconditions, samplesize, exogenousvariables, options_, M_, oo_)
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% [ts,oo_] = extended_path(initialconditions, samplesize, 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] options_
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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 [struct] results structure
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%
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% ALGORITHM
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%
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% SPECIAL REQUIREMENTS
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% Copyright © 2009-2023 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, verbosity, options_, oo_] = ...
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extended_path_initialization(initialconditions, samplesize, exogenousvariables, options_, M_, oo_);
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[shocks, spfm_exo_simul, innovations, oo_] = extended_path_shocks(innovations, ep, exogenousvariables, samplesize,M_,options_,oo_);
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% Initialize the matrix for the paths of the endogenous variables.
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endogenous_variables_paths = NaN(M_.endo_nbr,samplesize+1);
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endogenous_variables_paths(:,1) = initialconditions;
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% Set waitbar (graphic or text mode)
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hh_fig = dyn_waitbar(0,'Please wait. Extended Path simulations...');
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set(hh_fig,'Name','EP simulations.');
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% Initialize while-loop index.
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t = 1;
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% Main loop.
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while (t <= samplesize)
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if ~mod(t,10)
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dyn_waitbar(t/samplesize,hh_fig,'Please wait. Extended Path simulations...');
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end
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% Set period index.
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t = t+1;
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spfm_exo_simul(2,:) = shocks(t-1,:);
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if t>2
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% Set initial guess for the solver (using the solution of the
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% previous period problem).
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initialguess = [endogenousvariablespaths(:, 2:end), oo_.steady_state];
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else
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initialguess = [];
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end
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[endogenous_variables_paths(:,t), info_convergence, endogenousvariablespaths] = 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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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, ...
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options_, ...
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oo_, initialguess);
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if ~info_convergence
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msg = sprintf('No convergence of the (stochastic) perfect foresight solver (in period %s)!', int2str(t));
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warning(msg)
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break
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end
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end % (while) loop over t
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% Close waitbar.
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dyn_waitbar_close(hh_fig);
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% Set the initial period.
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if isdates(options_.initial_period)
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if ischar(options_.initial_period)
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initial_period = dates(options_.initial_period);
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else
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initial_period = options_.initial_period;
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end
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elseif isnan(options_.initial_period)
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initial_period = dates(1,1);
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else
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error('Type of option initial_period is wrong.')
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end
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% Return the simulated time series.
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if any(isnan(endogenous_variables_paths(:)))
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sl = find(~isnan(endogenous_variables_paths));
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nn = size(endogenous_variables_paths, 1);
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endogenous_variables_paths = reshape(endogenous_variables_paths(sl), nn, length(sl)/nn);
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end
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ts = dseries(transpose(endogenous_variables_paths), initial_period, M_.endo_names);
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oo_.endo_simul = transpose(ts.data); |