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Consistent Testing for Pairwise Dependence in Time Series

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Consistent Testing for Pairwise Dependence in Time Series. / Fokianos, K.; Pitsillou, M.
In: Technometrics, Vol. 59, No. 2, 2017, p. 262-270.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fokianos, K & Pitsillou, M 2017, 'Consistent Testing for Pairwise Dependence in Time Series', Technometrics, vol. 59, no. 2, pp. 262-270. https://doi.org/10.1080/00401706.2016.1156024

APA

Fokianos, K., & Pitsillou, M. (2017). Consistent Testing for Pairwise Dependence in Time Series. Technometrics, 59(2), 262-270. https://doi.org/10.1080/00401706.2016.1156024

Vancouver

Fokianos K, Pitsillou M. Consistent Testing for Pairwise Dependence in Time Series. Technometrics. 2017;59(2):262-270. Epub 2017 Apr 12. doi: 10.1080/00401706.2016.1156024

Author

Fokianos, K. ; Pitsillou, M. / Consistent Testing for Pairwise Dependence in Time Series. In: Technometrics. 2017 ; Vol. 59, No. 2. pp. 262-270.

Bibtex

@article{e2bb7d30fc094ac98a6a3fd2afc3bfa9,
title = "Consistent Testing for Pairwise Dependence in Time Series",
abstract = "We consider the problem of testing pairwise dependence for stationary time series. For this, we suggest the use of a Box–Ljung-type test statistic that is formed after calculating the distance covariance function among pairs of observations. The distance covariance function is a suitable measure for detecting dependencies between observations as it is based on the distance between the characteristic function of the joint distribution of the random variables and the product of the marginals. We show that, under the null hypothesis of independence and under mild regularity conditions, the test statistic converges to a normal random variable. The results are complemented by several examples. This article has supplementary material online.",
keywords = "Distance covariance, Empirical characteristic function, Generalized spectral density, Kernel, U-statistic, V-statistic",
author = "K. Fokianos and M. Pitsillou",
year = "2017",
doi = "10.1080/00401706.2016.1156024",
language = "English",
volume = "59",
pages = "262--270",
journal = "Technometrics",
issn = "0040-1706",
publisher = "American Statistical Association",
number = "2",

}

RIS

TY - JOUR

T1 - Consistent Testing for Pairwise Dependence in Time Series

AU - Fokianos, K.

AU - Pitsillou, M.

PY - 2017

Y1 - 2017

N2 - We consider the problem of testing pairwise dependence for stationary time series. For this, we suggest the use of a Box–Ljung-type test statistic that is formed after calculating the distance covariance function among pairs of observations. The distance covariance function is a suitable measure for detecting dependencies between observations as it is based on the distance between the characteristic function of the joint distribution of the random variables and the product of the marginals. We show that, under the null hypothesis of independence and under mild regularity conditions, the test statistic converges to a normal random variable. The results are complemented by several examples. This article has supplementary material online.

AB - We consider the problem of testing pairwise dependence for stationary time series. For this, we suggest the use of a Box–Ljung-type test statistic that is formed after calculating the distance covariance function among pairs of observations. The distance covariance function is a suitable measure for detecting dependencies between observations as it is based on the distance between the characteristic function of the joint distribution of the random variables and the product of the marginals. We show that, under the null hypothesis of independence and under mild regularity conditions, the test statistic converges to a normal random variable. The results are complemented by several examples. This article has supplementary material online.

KW - Distance covariance

KW - Empirical characteristic function

KW - Generalized spectral density

KW - Kernel

KW - U-statistic

KW - V-statistic

U2 - 10.1080/00401706.2016.1156024

DO - 10.1080/00401706.2016.1156024

M3 - Journal article

VL - 59

SP - 262

EP - 270

JO - Technometrics

JF - Technometrics

SN - 0040-1706

IS - 2

ER -