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How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?

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  • Benedikt M. Potscher
  • David Preinerstorfer

Abstract

We develop theoretical finite-sample results concerning the size of wild bootstrap-based heteroskedasticity robust tests in linear regression models. In particular, these results provide an efficient diagnostic check, which can be used to weed out tests that are unreliable for a given testing problem in the sense that they overreject substantially. This allows us to assess the reliability of a large variety of wild bootstrap-based tests in an extensive numerical study.

Suggested Citation

  • Benedikt M. Potscher & David Preinerstorfer, 2020. "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?," Papers 2005.04089, arXiv.org, revised Nov 2021.
  • Handle: RePEc:arx:papers:2005.04089
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    References listed on IDEAS

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    Cited by:

    1. Benedikt M. Potscher & David Preinerstorfer, 2021. "Valid Heteroskedasticity Robust Testing," Papers 2104.12597, arXiv.org, revised Nov 2022.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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