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Maximum Empirical Likelihood: Empty Set Problem

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  • Grendar, Marian
  • Judge, George G.

Abstract

In the Empirical Estimating Equations (E^3) approach to estimation and inference estimating equations are replaced by their data-dependent empirical counterparts. It is odd but with E^3 there are models where the E^3-based estimator does not exist for some data set, and does exist for others. This depends on whether or not a set of data-supported probability mass functions that satisfy the empirical estimating equations is empty for the data set. In a finite sample context, this unnoted feature invalidates methods of estimation and inference, such as the Maximum Empirical Likelihood, that operate within E^3. The empty set problem of E^3 is illustrated by several examples and possible remedies are discussed.

Suggested Citation

  • Grendar, Marian & Judge, George G., "undated". "Maximum Empirical Likelihood: Empty Set Problem," CUDARE Working Papers 53402, University of California, Berkeley, Department of Agricultural and Resource Economics.
    • Handle: RePEc:ags:ucbecw:53402
    DOI: 10.22004/ag.econ.53402
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    References listed on IDEAS

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    1. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
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    1. Grendar, Marian & Judge, George G., "undated". "Maximum likelihood with estimating equations," CUDARE Working Papers 56691, University of California, Berkeley, Department of Agricultural and Resource Economics.
    2. Yijie Xue & Nicole Lazar, 2012. "Empirical likelihood-based hot deck imputation methods," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 629-646.
    3. Varron, Davit, 2016. "Empirical likelihood confidence tubes for functional parameters in plug-in estimation," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 100-118.
    4. L. Andries Ark & Wicher P. Bergsma & Letty Koopman, 2023. "Maximum Augmented Empirical Likelihood Estimation of Categorical Marginal Models for Large Sparse Contingency Tables," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1228-1248, December.
    5. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2017. "Empirical likelihood ratio in penalty form and the convex hull problem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 507-529, November.
    6. Grendar, Marian & Judge, George G., 2010. "Revised empirical likelihood," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt6gs579r0, Department of Agricultural & Resource Economics, UC Berkeley.

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