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Combining empirical likelihood and generalized method of moments estimators: Asymptotics and higher order bias

Roni Israelov and Steven Lugauer ()

Statistics & Probability Letters, 2011, vol. 81, issue 9, 1339-1347

Abstract: This paper proposes an estimator combining empirical likelihood (EL) and the generalized method of moments (GMM) by allowing the sample average moment vector to deviate from zero and the sample weights to deviate from n-1. The new estimator may be adjusted through free parameter [delta][set membership, variant](0,1) with GMM behavior attained as [delta][long right arrow]0 and EL as [delta][long right arrow]1. When the sample size is small and the number of moment conditions is large, the parameter space under which the EL estimator is defined may be restricted at or near the population parameter value. The support of the parameter space for the new estimator may be adjusted through [delta]. The new estimator performs well in Monte Carlo simulations.

Keywords: Generalized; method; of; moments; Empirical; likelihood (search for similar items in EconPapers)
Date: 2011
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