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function [residuals,JJacobian] = linear_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, i_cols_0, i_cols_J0, jendo, jexog)
% Computes the residuals and the Jacobian matrix for a linear perfect foresight problem over T periods.
%
% INPUTS
% ...
%
% OUTPUTS
% ...
%
% ALGORITHM
% ...
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2015-2020 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 <http://www.gnu.org/licenses/>.
YY = [Y0; y; YT];
residuals = zeros(T*ny,1);
z = zeros(columns(dynamicjacobian), 1);
if nargout == 2
iJacobian = cell(T,1);
end
i_rows = 1:ny;
i_cols_J = i_cols;
offset = 0;
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 T==1 && it==maximum_lag+1
[rows, cols, vals] = find(dynamicjacobian(:,i_cols_0));
if size(dynamicjacobian, 1) == 1 % find() will return row vectors in this case
rows = rows';
cols = cols';
vals = vals';
end
iJacobian{1} = [rows, i_cols_J0(cols), vals];
elseif it == maximum_lag+1
[rows,cols,vals] = find(dynamicjacobian(:,i_cols_1));
if size(dynamicjacobian, 1) == 1 % find() will return row vectors in this case
rows = rows';
cols = cols';
vals = vals';
end
iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
elseif it == maximum_lag+T
[rows,cols,vals] = find(dynamicjacobian(:,i_cols_T));
if size(dynamicjacobian, 1) == 1 % find() will return row vectors in this case
rows = rows';
cols = cols';
vals = vals';
end
iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
else
[rows,cols,vals] = find(dynamicjacobian(:,i_cols_j));
if size(dynamicjacobian, 1) == 1 % find() will return row vectors in this case
rows = rows';
cols = cols';
vals = vals';
end
iJacobian{it-maximum_lag} = [offset+rows, i_cols_J(cols), vals];
i_cols_J = i_cols_J + ny;
end
offset = offset + ny;
end
i_rows = i_rows + ny;
i_cols = i_cols + ny;
end
if nargout == 2
iJacobian = cat(1,iJacobian{:});
JJacobian = sparse(iJacobian(:,1), iJacobian(:,2), iJacobian(:,3), T*ny, T*ny);
end
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