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A Robust Test For Autocorrelation in the Presence of Statistical Dependence

Listed author(s):
  • Lobato, I.N.

    (Centro de Investigacion Economica)

  • Nankervis, John C.

    (University of Surrey)

  • Savin, N.E.


    (University of Iowa)

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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Paper provided by University of Iowa, Department of Economics in its series Working Papers with number 99-07.

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Length: 29 pages
Date of creation: Jul 1999
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:

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