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Consistency of the OLS Bootstrap for Independently but Not-Identically Distributed Data: A Permutation Perspective

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  • Alwyn Young

    (Department of Economics, London School of Economics, Houghton St., London WC2A 2AE, UK)

Abstract

This paper introduces a new approach to proving bootstrap consistency based upon the distribution of permutation statistics, using it to derive results covering fundamentally not-identically distributed groups of data, in which average moments do not converge to anything, with moment conditions that are less demanding than earlier results for either identically distributed or not-identically distributed data.

Suggested Citation

  • Alwyn Young, 2025. "Consistency of the OLS Bootstrap for Independently but Not-Identically Distributed Data: A Permutation Perspective," Econometrics, MDPI, vol. 13(4), pages 1-27, October.
    • Handle: RePEc:gam:jecnmx:v:13:y:2025:i:4:p:41-:d:1777658
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    References listed on IDEAS

    as
    1. Young, Alwyn, 2019. "Channeling Fisher: randomization tests and the statistical insignificance of seemingly significant experimental results," LSE Research Online Documents on Economics 101401, London School of Economics and Political Science, LSE Library.
    2. Igor Fedotenkov, 2013. "A bootstrap method to test for the existence of finite moments," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 315-322, June.
    3. Alwyn Young, 2019. "Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(2), pages 557-598.
    4. Hongbin Cai & Yuyu Chen & Hanming Fang, 2009. "Observational Learning: Evidence from a Randomized Natural Field Experiment," American Economic Review, American Economic Association, vol. 99(3), pages 864-882, June.
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