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Consistency of the OLS bootstrap for independently but not-identically distributed data: a permutation perspective

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  • Young, Alwyn

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

  • Young, Alwyn, 2025. "Consistency of the OLS bootstrap for independently but not-identically distributed data: a permutation perspective," LSE Research Online Documents on Economics 130036, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:130036
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    File URL: https://researchonline.lse.ac.uk/id/eprint/130036/
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    References listed on IDEAS

    as
    1. Trapani, Lorenzo, 2016. "Testing for (in)finite moments," Journal of Econometrics, Elsevier, vol. 191(1), pages 57-68.
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    3. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.
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    7. 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.
    8. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    9. 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.
    10. 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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    12. James G. MacKinnon, 2020. "Wild cluster bootstrap confidence intervals," L'Actualité Economique, Société Canadienne de Science Economique, vol. 96(4), pages 721-743.
    13. Rebecca L. Thornton, 2008. "The Demand for, and Impact of, Learning HIV Status," American Economic Review, American Economic Association, vol. 98(5), pages 1829-1863, December.
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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