Revised empirical likelihood

Marian Grendar and George Judge ()

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: Empirical Likelihood (EL) and other methods that operate within the Empirical Estimating Equations (E3) approach to estimation and inference are challenged by the Empty Set Problem (ESP). ESP concerns the possibility that a model set, which is data-dependent, may be empty for some data sets. To avoid ESP we return from E3 back to the Estimating Equations, and explore the Bayesian infinite-dimensional Maximum A-posteriori Probability (MAP) method. The Bayesian MAP with Dirichlet prior motivates a Revised EL (ReEL) method. ReEL i) avoids ESP as well as the convex hull restriction, ii) attains the same basic asymptotic properties as EL, and iii) its computation complexity is comparable to that of EL.

Keywords: empirical estimating equations; generalized minimum contrast; empirical likelihood; generalized empirical likelihood; empty set problem; convex hull restriction; estimating equations; maximum aposteriori probability; Social and Behavioral Sciences (search for similar items in EconPapers)
Date: 2010-07-01
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Citations: View citations in EconPapers (1)

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Working Paper: Revised empirical likelihood (2010)
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