96 lines
3.2 KiB
Matlab
96 lines
3.2 KiB
Matlab
function [y, info_convergence, endogenousvariablespaths] = extended_path_core(periods,endo_nbr,exo_nbr,positive_var_indx, ...
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exo_simul,init,initial_conditions,...
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steady_state, ...
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debug,bytecode_flag,order,M,pfm,algo,solve_algo,stack_solve_algo,...
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olmmcp,options,oo,initialguess)
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% Copyright (C) 2016-2020 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 <http://www.gnu.org/licenses/>.
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ep = options.ep;
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if init% Compute first order solution (Perturbation)...
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endo_simul = simult_(M,options,initial_conditions,oo.dr,exo_simul(2:end,:),1);
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else
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if nargin==20 && ~isempty(initialguess)
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% Note that the first column of initialguess should be equal to initial_conditions.
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endo_simul = initialguess;
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else
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endo_simul = [initial_conditions repmat(steady_state,1,periods+1)];
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end
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end
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oo.endo_simul = endo_simul;
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if debug
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save ep_test_1.mat endo_simul exo_simul
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end
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if bytecode_flag && ~ep.stochastic.order
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try
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tmp = bytecode('dynamic', endo_simul, exo_simul, M_.params, endo_simul, periods);
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flag = false;
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catch ME
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disp(ME.message);
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flag = true;
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end
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else
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flag = true;
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end
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if flag
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if order == 0
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options.periods = periods;
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options.block = pfm.block;
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oo.endo_simul = endo_simul;
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oo.exo_simul = exo_simul;
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oo.steady_state = steady_state;
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options.bytecode = bytecode_flag;
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options.lmmcp = olmmcp;
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options.solve_algo = solve_algo;
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options.stack_solve_algo = stack_solve_algo;
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tmp = perfect_foresight_solver_core(M, options, oo);
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if ~tmp.deterministic_simulation.status
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info_convergence = false;
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else
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info_convergence = true;
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end
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else
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switch(algo)
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case 0
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[flag, tmp.endo_simul] = ...
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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, tmp.endo_simul] = ...
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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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info_convergence = ~flag;
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end
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end
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if ~info_convergence && ~options.no_homotopy
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[info_convergence, tmp.endo_simul] = extended_path_homotopy(endo_simul, exo_simul, M, options, oo, pfm, ep, order, algo, 2, debug);
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end
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if info_convergence
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y = tmp.endo_simul(:,2);
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else
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y = NaN(size(endo_nbr,1));
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end
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endogenousvariablespaths = tmp.endo_simul;
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