Testing for a change in persistence in the presence of non-stationary volatility☆
Testing for a change in persistence in the presence of non-stationary volatility☆
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DOI：
10.1016/j.jeconom.2008.09.004
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发表时间：
2008-11
影响因子：
6.3
通讯作者：
Giuseppe Cavaliere;A. Taylor
中科院分区：
文献类型：
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作者：
Giuseppe Cavaliere;A. Taylor
在本文中，我们考虑对（趋势-）平稳性的零值的检验，而不是观察样本中某些（已知或未知）点的持久性变化的替代，从I(0)到I(1)行为或反之亦然，特别是，[Kim， J., 2000]。检测线性时间序列的持续变化。经济研究[j]。我们表明，在创新过程显示非常一般形式的非平稳无条件波动的情况下，其中包括单个和多个波动中断作为特殊情况，用于测试持久性变化的基于比率的统计数据不具有关键限制零分布。数值证据表明，这可能在测试中导致严重的超大尺寸。因此，在实践中可能很难区分持久变化过程和具有恒定持久性但显示随时间变化的无条件波动的过程。我们通过提出基于野引导的测试实现来解决已识别的推理问题。蒙特卡罗证据表明，自举测试在有限样本中表现良好。本文提供了一个使用美国物价通胀数据的实证说明。
In this paper we consider tests for the null of (trend-) stationarity against the alternative of a change in persistence at some (known or unknown) point in the observed sample, either from I(0) to I(1) behaviour or vice versa, of, inter alia, [Kim, J., 2000. Detection of change in persistence of a linear time series. Journal of Econometrics 95, 97–116]. We show that in circumstances where the innovation process displays non-stationary unconditional volatility of a very general form, which includes single and multiple volatility breaks as special cases, the ratio-based statistics used to test for persistence change do not have pivotal limiting null distributions. Numerical evidence suggests that this can cause severe over-sizing in the tests. In practice it may therefore be hard to discriminate between persistence change processes and processes with constant persistence but which display time-varying unconditional volatility. We solve the identified inference problem by proposing wild bootstrap-based implementations of the tests. Monte Carlo evidence suggests that the bootstrap tests perform well in finite samples. An empirical illustration using US price inflation data is provided.