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Testing for Independence between Two stationary Time Series via the Empirical Characteristic Function

Author

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  • Yongmiao Hong

    (Department of Economics and Department of Statistical Science, Cornell University)

Abstract

This paper proposes an asymptotic one-sided N(0, 1) test for independence between two stationary time series using the empirical characteristic function. Unlike the tests based on the cross-correlation function (e.g. Haugh, 1976; Hong, 1996; Koch & Yang 1986), the proposed test has power against all pairwise cross-dependencies, including those with zero cross-correlation. By differentiating the empirical characteristic function at the origin, the present approach yields a modified version of Hong¡¯s (1996) test, which in turn generalizes Haugh¡¯s (1976) test. Other new tests can be derived by further differentiating the empirical characteristic function properly. A simulation study compares the new test with those of Haugh (1976), Hong (1996) and Koch & Yang (1986) in finite samples; the results show that the new test has reasonable sizes and good powers against linear and nonlinear cross-dependencies.

Suggested Citation

  • Yongmiao Hong, 2001. "Testing for Independence between Two stationary Time Series via the Empirical Characteristic Function," Annals of Economics and Finance, Society for AEF, vol. 2(1), pages 123-164, May.
    • Handle: RePEc:cuf:journl:y:2001:v:2:i:1:p:123-164
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    Cited by:

    1. Guochang Wang & Wai Keung Li & Ke Zhu, 2018. "New HSIC-based tests for independence between two stationary multivariate time series," Papers 1804.09866, arXiv.org.
    2. Chu, Ba, 2023. "A distance-based test of independence between two multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 195(C).

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