140 lines
4.8 KiB
Matlab
140 lines
4.8 KiB
Matlab
function [info_convergence, endo_simul] = extended_path_homotopy(endo_simul, exo_simul, M_, options_, oo_, pfm, ep, order, algo, method, debug)
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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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endo_simul0 = endo_simul;
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if ismember(method, [1, 2])
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noconvergence = true;
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iteration = 0;
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weight = .1;
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maxiter = 100;
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increase_flag = false;
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increase_factor = 1.2;
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decrease_factor = 1.1;
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state = false(5,1);
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oldweight = weight;
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while noconvergence
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iteration = iteration + 1;
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oo_.endo_simul = endo_simul;
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oo_.endo_simul(:,1) = oo_.steady_state + weight*(endo_simul0(:,1) - oo_.steady_state);
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oo_.exo_simul = bsxfun(@plus, weight*exo_simul, (1-weight)*transpose(oo_.exo_steady_state));
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if order==0
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[endo_simul_new, success] = perfect_foresight_solver_core(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
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else
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switch(algo)
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case 0
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[flag, endo_simul_new] = ...
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solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
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case 1
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[flag, endo_simul_new] = ...
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solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
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end
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end
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if isequal(order, 0)
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% Logical variable flag is false iff the solver fails.
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flag = success;
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else
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% Fix convention issue on the value of flag.
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flag = ~flag;
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end
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if debug
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dprintf('%u\t %1.8f\t %u', iteration, weight, flag)
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end
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state(2:end) = state(1:end-1);
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state(1) = flag;
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if flag
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if isequal(weight, 1)
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noconvergence = false;
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break
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end
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if all(state)
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increase_factor = 1+(increase_factor-1)*1.1;
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state = false(size(state));
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end
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oldweight = weight;
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weight = min(weight*increase_factor, 1);
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increase_flag = true;
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endo_simul = endo_simul_new;
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else
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if increase_flag
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weight = oldweight + (weight-oldweight)/100;
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else
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weight = min(weight/decrease_factor, 1);
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end
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end
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if iteration>maxiter
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break
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end
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if weight<1e-9
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break
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end
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end
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info_convergence = ~noconvergence;
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end
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if isequal(method, 3) || (isequal(method, 2) && noconvergence)
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if isequal(method, 2)
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endo_simul = endo_simul0;
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end
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weights = 0:(1/1000):1;
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noconvergence = true;
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index = 1;
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jndex = 0;
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nweights = length(weights);
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while noconvergence
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weight = weights(index);
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oo_.endo_simul = endo_simul;
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oo_.exo_simul = bsxfun(@plus, weight*exo_simul, (1-weight)*transpose(oo_.exo_steady_state));
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if order==0
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[endo_simul_new, success] = perfect_foresight_solver_core(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
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else
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switch(algo)
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case 0
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[flag, endo_simul_new] = ...
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solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
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case 1
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[flag, endo_simul_new] = ...
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solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
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end
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end
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if isequal(order, 0)
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% Logical variable flag is false iff the solver fails.
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flag = success;
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else
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% Fix convention issue on the value of flag.
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flag = ~flag;
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end
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if debug
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dprintf('%u\t %1.8f\t %u', index, weight, flag)
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end
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if flag
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jndex = index;
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if isequal(weight, 1)
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noconvergence = false;
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continue
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end
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index = index+1;
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endo_simul = endo_simul_new;
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else
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break
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end
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end
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info_convergence = ~noconvergence;
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end
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