A Robust Test For Autocorrelation in the Presence of Statistical Dependence
The problem addressed in this paper is to test the null hypothesis that a time series process is uncorrelated up to lag K in the presence of statistical dependence. We propose a robust test that is asymptotically distributed as chi-square when the null is true. The test is based on a consistent estimator of the asymptotic covariance matrix of the sample autocorrelations under the null. Two consistent estimation procedures are considered. Both employ automatic data-based methods to select tuning parameters. The performance of the two variants of the robust test is compared in a Monte Carlo study.
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|Date of creation:||Jul 1999|
|Date of revision:|
|Contact details of provider:|| Postal: University of Iowa, Department of Economics, Henry B. Tippie College of Business, Iowa City, Iowa 52242|
Phone: (319) 335-0829
Fax: (319) 335-1956
Web page: http://tippie.uiowa.edu/economics/
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