A Monte Carlo study of some limited and full information simultaneous equation estimators with normal and nonnormal autocorrelated disturbances

Abstract

The purpose of this paper is to examine the small sample properties of various limited and full information estimators of the structural coefficients of a system of two equations. Specifically, we consider a first-order autoregressive error structure under normal and nonnormal disturbances — for four different covariance structures — and report on a Monte Carlo study of the small sample behavior of limited and full information estimators according to the criteria of bias and dispersion. The results show that the differences in performance of the estimators for the alternative forms of the disturbance distributions are large. Moreover, none of the examined estimators is superior relative to the others, in the sense that its bias and dispersion are the smallest for at least one form of the disturbance distribution. Finally, no combination of highly or lowly autocorrelated disturbances favors some specific limited or full information estimator.