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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Publisher Info
Paper provided by University of Iowa, Department of Economics in its series Working Papers with number
99-07.
Length: 29 pages Date of creation: Jul 1999 Date of revision: Handle: RePEc:uia:iowaec:99-07
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/ More information through EDIRC
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