Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap
AbstractIn 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 49 (2005)
Issue (Month): 2 (April)
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Web page: http://www.elsevier.com/locate/csda
Other versions of this item:
- Emmanuel Flachaire, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," UniversitÃ© Paris1 PanthÃ©on-Sorbonne (Post-Print and Working Papers) halshs-00175910, HAL.
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