Statistical tests for a single change in mean against long-range dependence

摘要

Statistical tests are introduced for distinguishing between short-range dependent time series with a single change in mean, and long-range dependent time series, with the former making the null hypothesis. The tests are based on estimation of the self-similarity parameter after removing the change in mean from the series. The focus is on the GPH (Geweke and Porter-Hudak, 1983) and local Whittle estimation methods in the spectral domain. Theoretical properties of the resulting estimators are established when testing for a single change in mean, and small sample properties of the tests are examined in simulations. The introduced tests improve on the BHKS (Berkes et al., 2006) test which is the only other available test for the considered problem. It is argued that the BHKS test has a low power against long-range dependence alternatives and that this happens because the BHKS test statistic involves estimation of the long-run variance. The BHKS test could be improved readily by considering its R/S-like regression version which estimates the self-similarity parameter and which does not involve the long-run variance. Yet better alternatives are to use more powerful estimation methods (such as GPH or local Whittle) and lead to the tests introduced here.