A Method for Testing the Independence of Two Time Series That Accounts for a Potential Pattern in the Cross-Correlation Function

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected].. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association. The Haugh (1976) test for independence employs the univariate residual cross-correlation function. However, it ignores information about a possible pattern in successive cross-correlation coefficients. An asymptotic test is developed that incorporates this information and includes the Haugh test as a special case. A Monte Carlo study indicates that the proposed test is more powerful than the Haugh s and regression F tests for certain models. Two empirical examples are presented showing the simplicity of applying this test and its ability to recognize relationships that the Haugh test may fail to detect.