100 lines
3.2 KiB
Matlab
100 lines
3.2 KiB
Matlab
function [residuals,JJacobian] = linear_perfect_foresight_problem(y, dynamicjacobian, Y0, YT, ...
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exo_simul, params, steady_state, maximum_lag, T, ny, i_cols, ...
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i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, jendo, jexog)
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% Computes the residuals and the Jacobian matrix for a linear perfect foresight problem over T periods.
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%
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% INPUTS
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% ...
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%
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% OUTPUTS
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% ...
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%
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% ALGORITHM
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% ...
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%
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% SPECIAL REQUIREMENTS
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% None.
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% Copyright © 2015-2020 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 <https://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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z = zeros(columns(dynamicjacobian), 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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z(jendo) = YY(i_cols);
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z(jexog) = transpose(exo_simul(it,:));
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residuals(i_rows) = dynamicjacobian*z;
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if nargout == 2
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if T==1 && it==maximum_lag+1
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[rows, cols, vals] = find(dynamicjacobian(:,i_cols_0));
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if size(dynamicjacobian, 1) == 1 % find() will return row vectors in this case
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rows = rows';
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cols = cols';
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vals = vals';
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end
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iJacobian{1} = [rows, i_cols_J0(cols), vals];
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elseif it == maximum_lag+1
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[rows,cols,vals] = find(dynamicjacobian(:,i_cols_1));
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if size(dynamicjacobian, 1) == 1 % find() will return row vectors in this case
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rows = rows';
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cols = cols';
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vals = vals';
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end
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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(dynamicjacobian(:,i_cols_T));
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if size(dynamicjacobian, 1) == 1 % find() will return row vectors in this case
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rows = rows';
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cols = cols';
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vals = vals';
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end
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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(dynamicjacobian(:,i_cols_j));
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if size(dynamicjacobian, 1) == 1 % find() will return row vectors in this case
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rows = rows';
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cols = cols';
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vals = vals';
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end
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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*ny, T*ny);
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end
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