 Permutation testing for dependence in time seriesJoseph P. Romano and Marius A. Tirlea Journal of Time Series Analysis, 2022, vol. 43, issue 5, 781-807 Abstract: 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. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:43:y:2022:i:5:p:781-807 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Time Series Analysis is currently edited by M.B. Priestley More articles in Journal of Time Series Analysis from Wiley Blackwell |
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