LMMCP / linear perfect foresight: fix bug for models with a single equation

The routines use the find() function applied to a subset of columns of the
Jacobian, which in this case is a row vector. When passed a row vector, find()
returns row vectors (while it returns column vectors when passed a column
vector or a matrix). This case was not correctly handled.
parent df58037f
......@@ -16,7 +16,7 @@ function [residuals,JJacobian] = linear_perfect_foresight_problem(y, dynamicjaco
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2015-2019 Dynare Team
% Copyright (C) 2015-2020 Dynare Team
%
% This file is part of Dynare.
%
......@@ -55,15 +55,35 @@ for it = maximum_lag+(1:T)
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
......
......@@ -44,7 +44,7 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 1996-2019 Dynare Team
% Copyright (C) 1996-2020 Dynare Team
%
% This file is part of Dynare.
%
......@@ -83,15 +83,35 @@ for it = maximum_lag+(1:T)
residuals(i_rows) = res(eq_index);
if T==1 && it==maximum_lag+1
[rows, cols, vals] = find(jacobian(:,i_cols_0));
if size(jacobian, 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(jacobian(eq_index,i_cols_1));
if numel(eq_index) == 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(jacobian(eq_index,i_cols_T));
if numel(eq_index) == 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(jacobian(eq_index,i_cols_j));
if numel(eq_index) == 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
......
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