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Testing For Zero Autocorrelation In The Presence Of Statistical Dependence


  • Lobato, I.N.
  • Nankervis, John C.
  • Savin, N.E.


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 an extension of the Box–Pierce Q-test that is asymptotically distributed as chi-square when the null is true for a very general class of dependent processes that includes non-martingale difference sequences. The test is based on a consistent estimator of the asymptotic covariance matrix of the sample autocorrelations under the null. The finite sample performance of this extension is investigated in a Monte Carlo study.

Suggested Citation

  • Lobato, I.N. & Nankervis, John C. & Savin, N.E., 2002. "Testing For Zero Autocorrelation In The Presence Of Statistical Dependence," Econometric Theory, Cambridge University Press, vol. 18(3), pages 730-743, June.
  • Handle: RePEc:cup:etheor:v:18:y:2002:i:03:p:730-743_18

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