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Testing for independence between two covariance stationary time series

Hong, Yongmiao (1996): Testing for independence between two covariance stationary time series. Published in: Biometrika , Vol. 83, No. 3 (September 1996): pp. 615-625.

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Abstract

A one-sided asymptotically normal test for independence between two stationary time series is proposed by first prewhitening the two time series and then basing the test on the residual cross-correlation function. The test statistic is a properly standardised version of the sum of weighted squares of residual cross-correlations, with weights depending on a kernel function. Haugh's (1976) test can be viewed as a special case of our approach in the sense that it corresponds to the use of the truncated kernel. Many kernels deliver better power than Haugh's test. A simulation study shows that the new test has good power against short and long cross-correlations.

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