On the asymptotic non-equivalence of efficient-GMM and MEL estimators in models with missing data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Detail(s)

Original languageEnglish
Pages (from-to)361-388
Journal / PublicationScandinavian Journal of Statistics
Volume46
Issue number2
Online published17 Sep 2018
Publication statusPublished - Jun 2019

Abstract

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 empirical likelihood (MEL) estimators have the same asymptotic distribution to order n−1/2 and that both estimators are asymptotically semiparametric efficient. In this paper, we demonstrate that when data are missing at random from the sample, the utilization of some well-known missing-data handling approaches proposed in the literature can yield GMME and MEL estimators with nonidentical properties; in particular, it is shown that the GMME estimator is semiparametric efficient under all the missing-data handling approaches considered but that the MEL estimator is not always efficient. A thorough examination of the reason for the nonequivalence of the two estimators is presented. A particularly strong feature of our analysis is that we do not assume smoothness in the underlying moment conditions. Our results are thus relevant to situations involving nonsmooth estimating functions, including quantile and rank regressions, robust estimation, the estimation of receiver operating characteristic (ROC) curves, and so on.

Research Area(s)

  • empirical likelihood, generalized method of moments, kernel, missing at random, non-smooth, semiparametric efficiency bound