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A modified bootstrap for kernel-based specification test with heavy-tailed data

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Listed:
  • Huang, Ta-Cheng
  • Li, Hongjun
  • Li, Zheng

Abstract

This paper provides a new resampling strategy to improve the finite sample performance of a nonparametric kernel-based specification test in the presence of heavy-tailed error terms. Based on the test statistic of Li and Wang (1998), we propose to generate the bootstrapped samples using a modified wild bootstrap. This new method matches all moments of the error terms if the error has a symmetric distribution and matches the first and all even moments when error distribution is asymmetric around zero. This new resampling method has better finite sample performance than the traditional one when the distribution of the error terms is symmetric and heavy-tailed.

Suggested Citation

  • Huang, Ta-Cheng & Li, Hongjun & Li, Zheng, 2020. "A modified bootstrap for kernel-based specification test with heavy-tailed data," Economics Letters, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:ecolet:v:189:y:2020:i:c:s0165176520300276
    DOI: 10.1016/j.econlet.2020.108986
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Wild bootstrap; Kernel-based test; Specification test;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets

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