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 InfoPaper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00175910.
Date of creation: 2005
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Publication status: Published, Computational Statistics & Data Analysis / Computational Statistics and Data Analysis, 2005, 49, 2, 361-376
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wild bootstrap ; pairs bootstrap ; heteroskedasticity-robust test ; Monte Carlo simulations;
Other versions of this item:
- Flachaire, Emmanuel, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 361-376, April.
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