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Finite Sample Evidence Suggesting a Heavy Tail Problem of the Generalized Empirical Likelihood Estimator

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  • Patrik Guggenberger
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    Abstract

    Comprehensive Monte Carlo evidence is provided that compares the finite sample properties of generalized empirical likelihood (GEL) estimators to the ones of k-class estimators in the linear instrumental variables (IV) model. We focus on sample median, mean, mean squared error, and on the coverage probability and length of confidence intervals obtained from inverting a t-statistic based on the various estimators. The results indicate that in terms of the above criteria, all the GEL estimators and the limited information maximum likelihood (LIML) estimator behave very similarly. This suggests that GEL estimators might also share the “no-moment” problem of LIML. At sample sizes as in our Monte Carlo study, there is no systematic bias advantage of GEL estimators over k-class estimators. On the other hand, the standard deviation of GEL estimators is pronouncedly higher than for some of the k-class estimators. Therefore, if mean squared error is used as the underlying loss function, our study suggests the use of computationally simple estimators, such as two-stage least squares, in the linear IV model rather than GEL. Based on the properties of confidence intervals, we cannot recommend the use of GEL estimators either in the linear IV model.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/07474930801960410
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

    Volume (Year): 27 (2008)
    Issue (Month): 4-6 ()
    Pages: 526-541

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    Handle: RePEc:taf:emetrv:v:27:y:2008:i:4-6:p:526-541

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    Related research

    Keywords: Generalized empirical likelihood estimator; Generalized method of moments; Monte Carlo simulation; No-moment problem;

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    Cited by:
    1. Rockey, James, 2012. "Reconsidering the fiscal effects of constitutions," European Journal of Political Economy, Elsevier, vol. 28(3), pages 313-323.
    2. Dovonon, Prosper, 2008. "Large sample properties of the three-step euclidean likelihood estimators under model misspecification," MPRA Paper 40025, University Library of Munich, Germany, revised 16 May 2010.
    3. Fan, Yanqin & Gentry, Matthew & Li, Tong, 2011. "A new class of asymptotically efficient estimators for moment condition models," Journal of Econometrics, Elsevier, vol. 162(2), pages 268-277, June.
    4. Lee, Seojeong, 2014. "Asymptotic refinements of a misspecification-robust bootstrap for generalized method of moments estimators," Journal of Econometrics, Elsevier, vol. 178(P3), pages 398-413.
    5. Crudu, Federico & Sándor, Zsolt, 2011. "On the finite-sample properties of conditional empirical likelihood estimators," MPRA Paper 34116, University Library of Munich, Germany.

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