Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap

Emmanuel Flachaire

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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.

Keywords: wild bootstrap; pairs bootstrap; heteroskedasticity-robust test; Monte Carlo simulations (search for similar items in EconPapers)
Date: 2005
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00175910
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Citations: View citations in EconPapers (70)

Published in Computational Statistics and Data Analysis, 2005, 49 (2), pp.361-376. ⟨10.1016/j.csda.2004.05.018⟩

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Journal Article: Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap (2005)
Working Paper: Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:halshs-00175910

DOI: 10.1016/j.csda.2004.05.018

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