function [residuals,JJacobian] = perfect_foresight_problem(y, dynamicjacobian, Y0, YT, ... exo_simul, params, steady_state, ... maximum_lag, T, ny, i_cols, ... i_cols_J1, i_cols_1, i_cols_T, ... i_cols_j,nnzJ,jendo,jexog) % function [residuals,JJacobian] = perfect_foresight_problem(x, model_dynamic, Y0, YT,exo_simul, % params, steady_state, maximum_lag, periods, ny, i_cols,i_cols_J1, i_cols_1, % i_cols_T, i_cols_j, nnzA) % computes the residuals and th Jacobian matrix % for a perfect foresight problem over T periods. % % INPUTS % ... % OUTPUTS % ... % ALGORITHM % ... % % SPECIAL REQUIREMENTS % None. % Copyright (C) 2015 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 . YY = [Y0; y; YT]; residuals = zeros(T*ny,1); z = zeros(columns(dynamicjacobian), 1); if nargout == 2 JJacobian = sparse([],[],[],T*ny,T*ny,T*nnzJ); end i_rows = 1:ny; i_cols_J = i_cols; for it = maximum_lag+(1:T) z(jendo) = YY(i_cols); z(jexog) = transpose(exo_simul(it,:)); residuals(i_rows) = dynamicjacobian*z; if nargout == 2 if it == 2 JJacobian(i_rows,i_cols_J1) = dynamicjacobian(:,i_cols_1); elseif it == T + 1 JJacobian(i_rows,i_cols_J(i_cols_T)) = dynamicjacobian(:,i_cols_T); else JJacobian(i_rows,i_cols_J) = dynamicjacobian(:,i_cols_j); i_cols_J = i_cols_J + ny; end end i_rows = i_rows + ny; i_cols = i_cols + ny; end