The symmetric type two-stage trimmed least squares estimator for the simultaneous equations model

Abstract

A two-stage symmetric regression quantile is considered as an alternative for estimating the population quantile for the simultaneous equations model. We introduce a two-stage symmetric trimmed least squares estimator (LSE) based on this quantile. It is shown that, under mixed multivariate normal errors, this trimmed LSE has asymptotic variance much closer to the Cramer-Rao lower bound than some usual robust estimators. It can even achieve the Cramer-Rao lower bound when the contaminated variance goes to infinity. This suggests that the symmetric-type quantile function is as efficient in other statistical applications, such as outlier detection. A Monte Carlo study under asymmetric error distribution and a real data analysis are also presented.