A Robust Version of the Empirical Likelihood Estimator

Amor Keziou and Aida Toma
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Amor Keziou: Laboratoire de Mathématiques de Reims, UMR9008 CNRS et Université de Reims Champagne-Ardenne, UFR SEN, Moulin de la Housse, B.P. 1039, 51687 Reims, France
Aida Toma: Department of Applied Mathematics, Bucharest University of Economic Studies, Piaţa Romană no. 6, 010374 Bucharest, Romania

Mathematics, 2021, vol. 9, issue 8, 1-19

Abstract: In this paper, we introduce a robust version of the empirical likelihood estimator for semiparametric moment condition models. This estimator is obtained by minimizing the modified Kullback–Leibler divergence, in its dual form, using truncated orthogonality functions. We prove the robustness and the consistency of the new estimator. The performance of the robust empirical likelihood estimator is illustrated through examples based on Monte Carlo simulations.

Keywords: moment condition models; estimation; robustness; empirical likelihood (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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