A Robust Test For Autocorrelation in the Presence of Statistical Dependence

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.