103 lines
4.8 KiB
Matlab
103 lines
4.8 KiB
Matlab
function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function, Y0, YT, ...
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exo_simul, params, steady_state, ...
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maximum_lag, T, ny, i_cols, ...
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i_cols_J1, i_cols_1, i_cols_T, ...
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i_cols_j,nnzJ)
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% function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function, Y0, YT, ...
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% exo_simul, params, steady_state, ...
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% maximum_lag, T, ny, i_cols, ...
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% i_cols_J1, i_cols_1, i_cols_T, ...
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% i_cols_j,nnzJ)
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% computes the residuals and the Jacobian matrix for a perfect foresight problem over T periods.
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%
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% INPUTS
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% y [double] N*1 array, terminal conditions for the endogenous variables
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% dynamic_function [handle] function handle to _dynamic-file
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% Y0 [double] N*1 array, initial conditions for the endogenous variables
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% YT [double] N*1 array, terminal conditions for the endogenous variables
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% exo_simul [double] nperiods*M_.exo_nbr matrix of exogenous variables (in declaration order)
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% for all simulation periods
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% params [double] nparams*1 array, parameter values
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% steady_state [double] endo_nbr*1 vector of steady state values
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% maximum_lag [scalar] maximum lag present in the model
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% T [scalar] number of simulation periods
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% ny [scalar] number of endogenous variables
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% i_cols [double] indices of variables appearing in M.lead_lag_incidence
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% and that need to be passed to _dynamic-file
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% i_cols_J1 [double] indices of contemporaneous and forward looking variables
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% appearing in M.lead_lag_incidence
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% i_cols_1 [double] indices of contemporaneous and forward looking variables in
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% M.lead_lag_incidence in dynamic Jacobian (relevant in first period)
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% i_cols_T [double] columns of dynamic Jacobian related to contemporaneous and backward-looking
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% variables (relevant in last period)
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% i_cols_j [double] indices of variables in M.lead_lag_incidence
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% in dynamic Jacobian (relevant in intermediate periods)
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% nnzJ [scalar] number of non-zero elements in Jacobian
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% OUTPUTS
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% residuals [double] (N*T)*1 array, residuals of the stacked problem
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% JJacobian [double] (N*T)*(N*T) array, Jacobian of the stacked problem
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% ALGORITHM
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% None
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%
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% SPECIAL REQUIREMENTS
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% None.
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% Copyright (C) 1996-2017 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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YY = [Y0; y; YT];
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residuals = zeros(T*ny,1);
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if nargout == 2
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iJacobian = cell(T,1);
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end
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i_rows = 1:ny;
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i_cols_J = i_cols;
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offset = 0;
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for it = maximum_lag+(1:T)
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if nargout == 1
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residuals(i_rows) = dynamic_function(YY(i_cols),exo_simul, params, ...
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steady_state,it);
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elseif nargout == 2
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[residuals(i_rows),jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
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steady_state,it);
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if it == maximum_lag+1
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[rows,cols,vals] = find(jacobian(:,i_cols_1));
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iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
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elseif it == maximum_lag+T
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[rows,cols,vals] = find(jacobian(:,i_cols_T));
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iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
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else
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[rows,cols,vals] = find(jacobian(:,i_cols_j));
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iJacobian{it-maximum_lag} = [offset+rows, i_cols_J(cols), vals];
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i_cols_J = i_cols_J + ny;
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end
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offset = offset + ny;
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end
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i_rows = i_rows + ny;
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i_cols = i_cols + ny;
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end
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if nargout == 2
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iJacobian = cat(1,iJacobian{:});
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JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
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ny,T*ny);
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end |