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Fix various issues with stack_solve_algo=7 and lags on exogenous > 1 

Complete the fix started in 8913791ff0.

Change the test case to prevent regressions.
time-shift
Sébastien Villemot 2018-06-07 16:14:02 +02:00
parent 33c45ee8b9
commit 79484607a7
7 changed files with 16 additions and 16 deletions
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4
matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
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@ -55,9 +55,9 @@ for it = maximum_lag+(1:T)
z(jexog) = transpose(exo_simul(it,:));
residuals(i_rows) = dynamicjacobian*z;
if nargout == 2
if it == 2
if it == maximum_lag+1
JJacobian(i_rows,i_cols_J1) = dynamicjacobian(:,i_cols_1);
elseif it == T + 1
elseif it == maximum_lag+T
JJacobian(i_rows,i_cols_J(i_cols_T)) = dynamicjacobian(:,i_cols_T);
else
JJacobian(i_rows,i_cols_J) = dynamicjacobian(:,i_cols_j);

8
matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
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@ -74,7 +74,7 @@ i_rows = 1:ny;
offset = 0;
i_cols_J = i_cols;
for it = 2:(T+1)
for it = maximum_lag+(1:T)
if nargout == 1
res = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
@ -83,15 +83,15 @@ for it = 2:(T+1)
[res,jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
residuals(i_rows) = res(eq_index);
if it == 2
if it == maximum_lag+1
[rows,cols,vals] = find(jacobian(eq_index,i_cols_1));
iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
elseif it == T + 1
elseif it == maximum_lag+T
[rows,cols,vals] = find(jacobian(eq_index,i_cols_T));
iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
else
[rows,cols,vals] = find(jacobian(eq_index,i_cols_j));
iJacobian{it-1} = [offset+rows, i_cols_J(cols), vals];
iJacobian{it-maximum_lag} = [offset+rows, i_cols_J(cols), vals];
i_cols_J = i_cols_J + ny;
end
offset = offset + ny;

8
matlab/perfect-foresight-models/perfect_foresight_problem.m
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@ -71,22 +71,22 @@ i_rows = 1:ny;
i_cols_J = i_cols;
offset = 0;
for it = (maximum_lag+1):(T+1)
for it = maximum_lag+(1:T)
if nargout == 1
residuals(i_rows) = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
elseif nargout == 2
[residuals(i_rows),jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
if it == 2
if it == maximum_lag+1
[rows,cols,vals] = find(jacobian(:,i_cols_1));
iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
elseif it == T + 1
elseif it == maximum_lag+T
[rows,cols,vals] = find(jacobian(:,i_cols_T));
iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
else
[rows,cols,vals] = find(jacobian(:,i_cols_j));
iJacobian{it-1} = [offset+rows, i_cols_J(cols), vals];
iJacobian{it-maximum_lag} = [offset+rows, i_cols_J(cols), vals];
i_cols_J = i_cols_J + ny;
end
offset = offset + ny;

6
matlab/perfect-foresight-models/private/initialize_stacked_problem.m
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@ -67,9 +67,9 @@ elseif (options.solve_algo == 11)
end
end
y0 = endogenousvariables(:,1);
yT = endogenousvariables(:,periods+2);
z = endogenousvariables(:,2:periods+1);
y0 = endogenousvariables(:,M.maximum_lag);
yT = endogenousvariables(:,M.maximum_lag+periods+1);
z = endogenousvariables(:,M.maximum_lag+(1:periods));
illi = M.lead_lag_incidence';
[i_cols, junk,i_cols_j] = find(illi(:));
illi = illi(:,2:3);

2
matlab/perfect-foresight-models/solve_stacked_problem.m
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@ -69,7 +69,7 @@ else
check = 1;
end
endogenousvariables = [y0 reshape(y, M.endo_nbr, options.periods) yT];
endogenousvariables(:, M.maximum_lag+(1:options.periods)) = reshape(y, M.endo_nbr, options.periods);
if check
info.status = false;

2
tests/deterministic_simulations/rbc_det.mod
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@ -17,7 +17,7 @@ sigma2 = 0;
model;
// Eq. n°1:
efficiency = rho*efficiency(-1) + EfficiencyInnovation;
efficiency = rho*efficiency(-1) + EfficiencyInnovation(-2); // Use a lag of two to test the maximum_lag logic
// Eq. n°2:
Efficiency = effstar*exp(efficiency);

2
tests/deterministic_simulations/rbc_det_stack_solve_algo_7.mod
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@ -17,7 +17,7 @@ sigma2 = 0;
model;
// Eq. n°1:
efficiency = rho*efficiency(-1) + EfficiencyInnovation;
efficiency = rho*efficiency(-1) + EfficiencyInnovation(-2); // Use a lag of two to test the maximum_lag logic
// Eq. n°2:
Efficiency = effstar*exp(efficiency);