Small Sample Properties of Alternative Estimators of Seemingly Unrelated Regressions

Abstract

Abstract A Monte Carlo experiment is carried out to examine the small sample properties of five alternative estimators of a set of linear regression equations with mutually correlated disturbances. The estimators considered are ordinary least squares, Zellner's two-stage Aitken, Zellner's iterative Aitken, Telser's iterative, and maximum likelihood. The experiment, based on 100 samples, provides approximate sampling distributions for samples of size 10, 20 and 100 for various model specifications. The results show that three of the five estimation methods lead to identical estimates for any sample size, that in many cases the two-stage Aitken estimator performs as well as or better than the other estimators, and that most of the asymptotic properties of this estimator tend to hold in small samples as well.