A Robust Test For Autocorrelation in the Presence of Statistical Dependence

Ignacio Lobato, John C. Nankervis and N.E. Savin ()
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John C. Nankervis: University of Surrey
N.E. Savin: University of Iowa

Working Papers from University of Iowa, Department of Economics

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.

Pages: 29 pages
Date: 1999-07
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Persistent link: https://EconPapers.repec.org/RePEc:uia:iowaec:99-07

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