Permutation testing for dependence in time series
Permutation testing for dependence in time series
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DOI：
10.1111/jtsa.12638
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发表时间：
2020-09
影响因子：
0.9
通讯作者：
Joseph P. Romano;Marius A. Tirlea
中科院分区：
文献类型：
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作者：
Joseph P. Romano;Marius A. Tirlea
给定来自平稳时间序列的观测值，置换检验允许在独立同分布的零假设下构造精确水平的α检验。(or更一般地，可交换的）分布。另一方面，当零假设是潜在过程是不相关序列时，排列检验不一定是α水平，在大样本中也不接近α水平。此外，排列检验可能具有较大的第3类或方向性错误，其中双侧检验拒绝零假设，并得出一阶自相关大于0的结论，而实际上它小于0。本文在对序列的混合系数和矩的弱假设下，给出了一个检验方法，当观测值独立同分布时，在有限样本中，置换检验的渐近有效性成立，同时保留了精确的拒绝概率α。蒙特卡洛模拟研究，比较排列测试的自相关性的其他测试，也进行了，沿着与金融数据的应用经验的例子。
Given observations from a stationary time series, permutation tests allow one to construct exactly level α tests under the null hypothesis of an i.i.d. (or, more generally, exchangeable) distribution. On the other hand, when the null hypothesis of interest is that the underlying process is an uncorrelated sequence, permutation tests are not necessarily level α , nor are they approximately level α in large samples. In addition, permutation tests may have large Type 3, or directional, errors, in which a two‐sided test rejects the null hypothesis and concludes that the first‐order autocorrelation is larger than 0, when in fact it is less than 0. In this article, under weak assumptions on the mixing coefficients and moments of the sequence, we provide a test procedure for which the asymptotic validity of the permutation test holds, while retaining the exact rejection probability α in finite samples when the observations are independent and identically distributed. A Monte Carlo simulation study, comparing the permutation test to other tests of autocorrelation, is also performed, along with an empirical example of application to financial data.