function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_) %function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_) % Core function calling solvers for perfect foresight model % % INPUTS % - M_ [struct] contains a description of the model. % - options_ [struct] contains various options. % - oo_ [struct] contains results % % OUTPUTS % - oo_ [struct] contains results % - maxerror [double] contains the maximum absolute error % Copyright (C) 2015-2017 Dynare Team % % This file is part of Dynare. % % Dynare is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % Dynare is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with Dynare. If not, see . if options_.lmmcp.status options_.stack_solve_algo=7; options_.solve_algo = 10; end if options_.linear_approximation && ~(isequal(options_.stack_solve_algo,0) || isequal(options_.stack_solve_algo,7)) error('perfect_foresight_solver: Option linear_approximation is only available with option stack_solve_algo equal to 0.') end if options_.linear && isequal(options_.stack_solve_algo,0) options_.linear_approximation = 1; end if options_.block if options_.bytecode try [info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods); catch info = 1; end if info oo_.deterministic_simulation.status = false; else oo_.endo_simul = tmp; oo_.deterministic_simulation.status = true; end if options_.no_homotopy mexErrCheck('bytecode', info); end else oo_ = feval([M_.fname '_dynamic'], options_, M_, oo_); end else if options_.bytecode try [info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods); catch info = 1; end if info oo_.deterministic_simulation.status = false; else oo_.endo_simul = tmp; oo_.deterministic_simulation.status = true; end if options_.no_homotopy mexErrCheck('bytecode', info); end else if M_.maximum_endo_lead == 0 % Purely backward model [oo_.endo_simul, oo_.deterministic_simulation] = ... sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_); elseif M_.maximum_endo_lag == 0 % Purely forward model [oo_.endo_simul, oo_.deterministic_simulation] = ... sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_); else % General case switch options_.stack_solve_algo case 0 if options_.linear_approximation [oo_.endo_simul, oo_.deterministic_simulation] = ... sim1_linear(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_); else [oo_.endo_simul, oo_.deterministic_simulation] = ... sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_); end case 6 if options_.linear_approximation error('Invalid value of stack_solve_algo option!') end [oo_.endo_simul, oo_.deterministic_simulation] = ... sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_); case 7 if options_.linear_approximation if isequal(options_.solve_algo, 10) warning('It would be more efficient to set option solve_algo equal to 0!') end [oo_.endo_simul, oo_.deterministic_simulation] = ... solve_stacked_linear_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_); else [oo_.endo_simul, oo_.deterministic_simulation] = ... solve_stacked_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_); end otherwise error('Invalid value of stack_solve_algo option!') end end end end if nargout>1 y0 = oo_.endo_simul(:,1); yT = oo_.endo_simul(:,options_.periods+2); yy = oo_.endo_simul(:,2:options_.periods+1); if ~exist('illi') illi = M_.lead_lag_incidence'; [i_cols,junk,i_cols_j] = find(illi(:)); illi = illi(:,2:3); [i_cols_J1,junk,i_cols_1] = find(illi(:)); i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)'); end if options_.block && ~options_.bytecode maxerror = oo_.deterministic_simulation.error; else if options_.bytecode [chck, residuals, junk]= bytecode('dynamic','evaluate', oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1); else residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '_dynamic']), y0, yT, ... oo_.exo_simul,M_.params,oo_.steady_state, ... M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ... i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ... M_.NNZDerivatives(1)); end maxerror = max(max(abs(residuals))); end end