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

Yongmiao Hong

Annals of Economics and Finance, 2001, vol. 2, issue 1, 123-164

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.

Keywords: Asymptotic normality; Cramer-von Mises statistic; Empirical characteristic function; Independence; Kernel function; Multivariate time series (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (2)

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