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Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap

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  • Emmanuel Flachaire

    (EUREQUA - Equipe Universitaire de Recherche en Economie Quantitative - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

In regression models, appropriate bootstrap methods for inference robust to heteroskedasticity of unknown form are the wild bootstrap and the pairs bootstrap. The finite sample performance of a heteroskedastic-robust test is investigated with Monte Carlo experiments. The simulation results suggest that one specific version of the wild bootstrap outperforms the other versions of the wild bootstrap and of the pairs bootstrap. It is the only one for which the bootstrap test gives always better results than the asymptotic test.

Suggested Citation

  • Emmanuel Flachaire, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00175910, HAL.
    • Handle: RePEc:hal:cesptp:halshs-00175910
    DOI: 10.1016/j.csda.2004.05.018
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00175910
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    References listed on IDEAS

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    1. DAVIDSON, Russel & MACKINNON, James G., 1985. "Heteroskedastcity-robust tests in regressions directions," LIDAM Reprints CORE 678, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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