A GENERALIZED PORTMANTEAU TEST FOR INDEPENDENCE BETWEEN TWO STATIONARY TIME SERIES

By Xiaofeng Shao. 2008

11 citations recorded by the source corpus.

We propose generalized portmanteau-type test statistics in the frequency domain to test independence between two stationary time series. The test statistics are formed analogous to the one in the paper by Chen and Deo (2004, Econometric Theory 20, 382–416), who extended the applicability of the portmanteau goodness-of-fit test to the long memory case. Under the null hypothesis of independence, the asymptotic standard normal distributions of the proposed statistics are derived under fairly mild conditions. In particular, each time series is allowed to possess short memory, long memory, or antipersistence. A simulation study shows that the tests have reasonable size and power properties.

Subfields: Economics and Econometrics, Finance

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In Economics and Econometrics

1. Arthur Gretton
2. Kacper P. Chwialkowski

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1. Arthur Gretton
2. Kacper P. Chwialkowski
3. Bernhard Schölkopf
4. Dino Sejdinovic
5. Leila Wehbe
6. Aaditya Ramdas

Citations in the corpus

• A Kernel Independence Test for Random Processes (2014, methodology): “Prior work in the context of dependency tests of two time series includes cross covariance based tests e.g. (Haugh, 1976; Hong, 1996; Shao, 2009); and a Generalized Association Measure based criterion (Fadlallah et al., 2012).”