Change-in-mean tests in long-memory time series: a review of recent developments
Change-in-mean tests in long-memory time series: a review of recent developments
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DOI：
10.1007/s10182-018-0328-5
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Kai Wenger;C. Leschinski;P. Sibbertsen
中科院分区：
文献类型：
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作者：
Kai Wenger;C. Leschinski;P. Sibbertsen
众所周知，均值漂移的标准检验在长相关时间序列中是无效的。因此，在文献中已经提出了几个长记忆的稳健扩展的标准测试原则的变化的意思。这些可以分为两组：那些利用长期方差和自归一化检验统计量的一致估计。在这里，我们回顾了这一文献，并通过推导一个新的长记忆鲁棒版本的sup-Wald测试来补充它。除了给出一个系统的回顾，我们进行了广泛的蒙特卡罗研究，比较这些方法的相对性能。特别注意的是相互作用的测试结果与估计的长记忆参数。此外，我们表明，自归一化检验统计量的功率可以大大提高使用的估计，是强大的均值漂移。
It is well known that standard tests for a mean shift are invalid in long-range dependent time series. Therefore, several long-memory robust extensions of standard testing principles for a change-in-mean have been proposed in the literature. These can be divided into two groups: those that utilize consistent estimates of the long-run variance and self-normalized test statistics. Here, we review this literature and complement it by deriving a new long-memory robust version of the sup-Wald test. Apart from giving a systematic review, we conduct an extensive Monte Carlo study to compare the relative performance of these methods. Special attention is paid to the interaction of the test results with the estimation of the long-memory parameter. Furthermore, we show that the power of self-normalized test statistics can be improved considerably by using an estimator that is robust to mean shifts.