Commit 24b0b22e authored by Stéphane Adjemian's avatar Stéphane Adjemian
Browse files

Factorized code using ispd routine and fixed bugs in models with measurement errors.

parent 26809527
......@@ -158,81 +158,31 @@ if (DynareOptions.mode_compute~=1) && any(xparam1>BayesInfo.ub)
return
end
% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
Model = set_all_parameters(xparam1,EstimatedParameters,Model);
Q = Model.Sigma_e;
H = Model.H;
for i=1:EstimatedParameters.nvx
k =EstimatedParameters.var_exo(i,1);
Q(k,k) = xparam1(i)*xparam1(i);
end
offset = EstimatedParameters.nvx;
if EstimatedParameters.nvn
for i=1:EstimatedParameters.nvn
k = EstimatedParameters.var_endo(i,1);
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
end
offset = offset+EstimatedParameters.nvn;
else
H = zeros(nvobs);
end
% Get the off-diagonal elements of the covariance matrix for the structural innovations. Test if Q is positive definite.
if EstimatedParameters.ncx
for i=1:EstimatedParameters.ncx
k1 =EstimatedParameters.corrx(i,1);
k2 =EstimatedParameters.corrx(i,2);
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
Q(k2,k1) = Q(k1,k2);
end
% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
[CholQ,testQ] = chol(Q);
if testQ
% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
a = diag(eig(Q));
k = find(a < 0);
if k > 0
fval = objective_function_penalty_base+sum(-a(k));
exit_flag = 0;
info = 43;
return
end
if ~isscalar(Q) && EstimatedParameters.ncx
[Q_is_positive_definite, penalty] = ispd(Q);
if ~Q_is_positive_definite
fval = objective_function_penalty_base+penalty;
exit_flag = 0;
info = 43;
return
end
offset = offset+EstimatedParameters.ncx;
end
% Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite.
if EstimatedParameters.ncn
for i=1:EstimatedParameters.ncn
k1 = DynareOptions.lgyidx2varobs(EstimatedParameters.corrn(i,1));
k2 = DynareOptions.lgyidx2varobs(EstimatedParameters.corrn(i,2));
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
H(k2,k1) = H(k1,k2);
end
% Try to compute the cholesky decomposition of H (possible iff H is positive definite)
[CholH,testH] = chol(H);
if testH
% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
a = diag(eig(H));
k = find(a < 0);
if k > 0
fval = objective_function_penalty_base+sum(-a(k));
exit_flag = 0;
info = 44;
return
end
if ~isscalar(H) && EstimatedParameters.ncn
[H_is_positive_definite, penalty] = ispd(H);
if ~H_is_positive_definite
fval = objective_function_penalty_base+penalty;
exit_flag = 0;
info = 44;
return
end
offset = offset+EstimatedParameters.ncn;
end
% Update estimated structural parameters in Mode.params.
if EstimatedParameters.np > 0
Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
end
% Update Model.Sigma_e and Model.H.
Model.Sigma_e = Q;
Model.H = H;
%------------------------------------------------------------------------------
% 2. call model setup & reduction program
%------------------------------------------------------------------------------
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment