Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap
E Flachaire - Computational Statistics & Data Analysis, 2005 - Elsevier
Computational Statistics & Data Analysis, 2005•Elsevier
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 always gives better results than the asymptotic test.
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 always gives better results than the asymptotic test.
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 always gives better results than the asymptotic test.
Elsevier
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