统计与管理学院2016年学术报告第8期

【主  题】On the asymptotic non-equivalence of GMM and EL estimators in models with missing data

【报告人】 陈雪蓉

西南财经大学

【时  间】 2016年4月11日（星期一）10:30－11:30

【地  点】 上海财经大学统计与管理学院大楼1208室

【摘  要】 The generalized method of moments (GMM) and empirical likelihood (EL) are popular methods for combining sample and auxiliary information. These methods are used in very diverse fields of research where competing theories often suggest variables satisfying different moment conditions. Results in the literature have shown that the efficient GMM (GMME)and maximum EL (MEL) estimators have the same asymptotic distribution to order and both estimators are asymptotically semi-parametric efficient. In this paper, we demonstrate that when data are missing at random from the sample, the utilization of some well-known missing at a handling approaches proposed in the literature can yield GMME and MEL estimators with non-identical properties; in particular, it is shown that the GMME estimator is semiparametric efficient under all the missing data handling approaches considered but the MEL estimator is not always efficient. A thorough examination of the reason for the non-equivalence of the two estimators is presented. A particularly strong feature of our analysis is that we don’t assume smoothness in the underlying moment conditions. Our results are thus relevant to situations involving non-smooth estimating functions including quantile and rank regressions, robust estimation, the estimation of ROC curves, and so on.

【邀请人】 陈艳