Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap
Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap
复制标题
DOI：
10.1016/j.csda.2004.05.018
复制
发表时间：
2005-04-30
影响因子：
1.8
通讯作者：
Flachaire, E
中科院分区：
文献类型：
--
作者：
Flachaire, E
在回归模型中，适用于对未知形式异方差稳健推断的Bootstrap方法是Wild Bootstrap和Pair Bootstrap。利用Monte Carlo实验研究了异方差稳健检验的有限样本性能。仿真结果表明，一个特定版本的野生引导优于其他版本的野生引导和对引导。这是唯一一个自举检验总是比渐近检验给出更好的结果。(c)2004 Elsevier B.V.保留所有权利。
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. (c) 2004 Elsevier B.V. All rights reserved.