Efficiency properties of feasible generalized least squares estimators in SURE models under non-normal disturbances

This paper considers Zellnerʹs two estimators in a seemingly unrelated regression equations (SURE) model. Efficiency properties of the two estimators are analyzed by asymptotic expansions and Monte Carlo experiments under non-normal disturbances. It is shown that the two estimators have the same efficiency up to the second-order terms of O(T−12), and that non-normality influences the rate of convergency to normality of the estimators.