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Large deviations of generalized method of moments and empirical likelihood estimators

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  • Taisuke Otsu

Abstract

This paper studies large deviation properties of the generalized method of moments and generalized empirical likelihood estimators for moment restriction models. We consider two cases for the data generating probability measure: the model assumption and local deviations from the model assumption. For both cases, we derive conditions where these estimators have exponentially small error probabilities for point estimation.
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Suggested Citation

  • Taisuke Otsu, 2011. "Large deviations of generalized method of moments and empirical likelihood estimators," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 321-329, July.
    • Handle: RePEc:ect:emjrnl:v:14:y:2011:i:2:p:321-329
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    Cited by:

    1. Hwang, Jungbin & Kang, Byunghoon & Lee, Seojeong, 2022. "A doubly corrected robust variance estimator for linear GMM," Journal of Econometrics, Elsevier, vol. 229(2), pages 276-298.
    2. Jiang, Hui & Wang, Shaochen, 2017. "Moderate deviation principles for classical likelihood ratio tests of high-dimensional normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 57-69.
    3. Byunghoon Kang, 2018. "Higher Order Approximation of IV Estimators with Invalid Instruments," Working Papers 257105320, Lancaster University Management School, Economics Department.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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