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Robust Estimation with Exponentially Tilted Hellinger Distance

Author

Listed:
  • Bertille Antoine

    (Simon Fraser University)

  • Prosper Dovonon

    (Concordia University)

Abstract

This paper is concerned with estimation of parameters defined by moment equalities. We introduce the exponentially tilted Hellinger distance (ETHD) estimator which is efficient under correct specification, and robust to both global and local misspecification. In the spirit of Schennach (2007), ETHD combines the Hellinger distance and the Kullback-Leibler information criterion. We show that it achieves optimal minimax robust properties under local deviations from the model and remains well-behaved under global misspecification.

Suggested Citation

  • Bertille Antoine & Prosper Dovonon, 2020. "Robust Estimation with Exponentially Tilted Hellinger Distance," Discussion Papers dp20-02, Department of Economics, Simon Fraser University.
    • Handle: RePEc:sfu:sfudps:dp20-02
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    References listed on IDEAS

    as
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    3. Gospodinov, Nikolay & Maasoumi, Esfandiar, 2021. "Generalized aggregation of misspecified models: With an application to asset pricing," Journal of Econometrics, Elsevier, vol. 222(1), pages 451-467.

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    More about this item

    Keywords

    moment condition models; global misspecification; local misspecification; Hellinger distance; minimax robust estimation; semiparametric efficiency;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • 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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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