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A Robust Test For Autocorrelation in the Presence of Statistical Dependence

Author

Listed:
  • Lobato, I.N.

    (Centro de Investigacion Economica)

  • Nankervis, John C.

    (University of Surrey)

  • Savin, N.E.

    (University of Iowa)

Abstract

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.

Suggested Citation

  • Lobato, I.N. & Nankervis, John C. & Savin, N.E., 1999. "A Robust Test For Autocorrelation in the Presence of Statistical Dependence," Working Papers 99-07, University of Iowa, Department of Economics.
    • Handle: RePEc:uia:iowaec:99-07
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