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Bootstrapping Kernel-Based Semiparametric Estimators

Author

Listed:
  • Matias D. Cattaneo

    (University of Michigan)

  • Michael Jansson

    (UC Berkeley and CREATES)

Abstract

This paper develops alternative asymptotic results for a large class of two-step semiparametric estimators. The first main result is an asymptotic distribution result for such estimators and differs from those obtained in earlier work on classes of semiparametric two-step estimators by accommodating a non-negligible bias. A noteworthy feature of the assumptions under which the result is obtained is that reliance on a commonly employed stochastic equicontinuity condition is avoided. The second main result shows that the bootstrap provides an automatic method of correcting for the bias even when it is non-negligible.

Suggested Citation

  • Matias D. Cattaneo & Michael Jansson, 2014. "Bootstrapping Kernel-Based Semiparametric Estimators," CREATES Research Papers 2014-25, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2014-25
    as

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    File URL: https://repec.econ.au.dk/repec/creates/rp/14/rp14_25.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    2. Dante Amengual & Enrique Sentana, 2016. "Comments on: Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(2), pages 248-252.

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    More about this item

    Keywords

    Semiparametric estimation; bootstrapping; asymptotic separability.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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