Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap
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.
|Date of creation:||2005|
|Date of revision:|
|Publication status:||Published in Computational Statistics & Data Analysis / Computational Statistics and Data Analysis, 2005, 49 (2), pp.361-376. <10.1016/j.csda.2004.05.018>|
|Note:||View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00175910|
|Contact details of provider:|| Web page: https://hal.archives-ouvertes.fr/|
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