Bootstrap Inference for Stationarity
Bootstrap Inference for Stationarity
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平稳性的 Bootstrap 推理
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
郭炳伸
中科院分区：
文献类型：
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作者：
Biing;Ching‐Chuan Tsong;郭炳伸
Kwiatkowski等人（1992）提出的零平稳性检验是经验时间序列研究中不可缺少的工具。然而，测试显示相当大的尺寸失真的存在下，高度持久的，但固定的过程。利用局部统一框架，本文提供了一个渐近解释为什么尺寸问题的存在。分析表明，如果在所关注的参数空间中没有重正化，则测试无法收敛。但是，它提供了有限的实际修改，以减少大小的偏见，因为一个未知的局部到单位参数，不能一致地估计。我们设计了一个参数引导方案来解释的大小失真。我们的引导建议是能够产生独立的引导重新采样，无论在所考虑的系列的组件表示的依赖。即使在有问题的参数空间中，模拟表明，我们的自助测试表现出很好的控制经验拒绝概率，同时保持了可比的权力，渐近同行，为小和中等的样本量中发现的应用程序。JEL分类：C12、C14、C15、C22。
Tests for the stationarity null due to Kwiatkowski et al. (1992) has been an indispensable part of tool kits for empirical time series research. The tests however display considerable size distortions in the presence of highly persistent but stationary processes. Using a localto-unity framework, the paper offers an asymptotic explanation why the size problem comes into existence. The analysis shows that the tests fail to converge without a renormalization in the parameter space of concern. But it lends limited practical modifications to reducing the size bias, because of an unknown local-to-unity parameter that cannot be consistently estimated. We devise a parametric bootstrap scheme to account for the size distortions instead. Our bootstrap proposal is able to generate independent bootstrap re-samples, regardless of the dependence in the component representation of the considered series. Even in the problematic parameter space, simulations demonstrate that our bootstrap tests exhibit an excellent control over the empirical rejection probabilities, while maintaining a comparable power to the asymptotic counterparts, for both small and moderate sample sizes found in applications. JEL Classification: C12, C14, C15, C22.