Wild bootstrap tests for unit root in ESTAR models
Wild bootstrap tests for unit root in ESTAR models
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ESTAR 模型中单位根的狂野引导测试
DOI：
10.1007/s10260-014-0289-0
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发表时间：
2015
影响因子：
1
通讯作者：
Daiki Maki
中科院分区：
文献类型：
--
作者：
Maki;D.;Kitasaka;S.;Daiki Maki
本文介绍指数平滑过渡自回归（ESTAR）模型中单位根的野自举检验。ESTAR模型中的渐近单位根检验在存在异方差（如广义自回归条件异方差和随机波动）的情况下具有严重的大小扭曲，因此，为了改善这些扭曲，我们使用了野生自举。蒙特卡罗模拟表明，在渐近检验中，在异方差下会发生严重的零假设过度拒绝，而所提出的野生自举检验具有合理的大小和功率特性。
This paper introduces wild bootstrap tests for unit root in exponential smooth transition autoregressive (ESTAR) models. Asymptotic unit root tests in ESTAR models have severe size distortions in the presence of heteroskedastic variances such as generalized autoregressive conditional heteroskedasticity and stochastic volatility, and hence, to improve these distortions, we use a wild bootstrap. Monte Carlo simulations show that in asymptotic tests, severe over-rejection of the null hypothesis occurs under heteroskedastic variances, whereas the proposed wild bootstrap tests have reasonable size and power properties.