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.
Item Type: | MPRA Paper |
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Original Title: | Testing for independence between two covariance stationary time series |
Language: | English |
Keywords: | Coherency; Cross-correlation; Independence; Kernel function; Multivariate time series. |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models |
Item ID: | 108731 |
Depositing User: | Yongmiao Hong |
Date Deposited: | 12 Jul 2021 06:49 |
Last Modified: | 06 Dec 2024 15:23 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/108731 |