diff --git a/matlab/non_linear_dsge_likelihood.m b/matlab/non_linear_dsge_likelihood.m index 2af54fac8eaf9b558b37cd55fa72eb5e0a756770..6206ca11e5ff38489dc72a15b8b21846624ce33a 100644 --- a/matlab/non_linear_dsge_likelihood.m +++ b/matlab/non_linear_dsge_likelihood.m @@ -135,14 +135,16 @@ end switch DynareOptions.particle.initialization case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model. StateVectorMean = ReducedForm.constant(mf0); - StateVectorVariance = lyapunov_symm(dr.ghx(mf0,:), dr.ghu(mf0,:)*Q*dr.ghu(mf0,:)', DynareOptions.lyapunov_fixed_point_tol, ... + [A,B] = kalman_transition_matrix(dr,dr.restrict_var_list,dr.restrict_columns,Model.exo_nbr); + StateVectorVariance = lyapunov_symm(A, B*Q*B', DynareOptions.lyapunov_fixed_point_tol, ... DynareOptions.qz_criterium, DynareOptions.lyapunov_complex_threshold, [], DynareOptions.debug); + StateVectorVariance = StateVectorVariance(mf0,mf0); case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model). StateVectorMean = ReducedForm.constant(mf0); old_DynareOptionsperiods = DynareOptions.periods; DynareOptions.periods = 5000; y_ = simult(DynareResults.steady_state, dr,Model,DynareOptions,DynareResults); - y_ = y_(state_variables_idx,2001:5000); + y_ = y_(dr.order_var(state_variables_idx),2001:5000); %state_variables_idx is in dr-order while simult_ is in declaration order StateVectorVariance = cov(y_'); DynareOptions.periods = old_DynareOptionsperiods; clear('old_DynareOptionsperiods','y_');