A Monte Carlo study to compare two recent modifications of the KPSS test for near integration

Abstract

The objective of this article is to compare the size and power properties of two modifications of the KPSS test of Kwiatkowski et al. (1992) proposed by Sul et al. (2005) and by Harris et al. (2007), using Monte Carlo simulations, in order to decide which version to use in applied research. The two modifications have been proposed to deal with those cases in which the null hypothesis specifies that the time series is near integrated, in the sense that it is very close to the alternative hypothesis of a unit root. It has been shown in the literature that in these cases the KPSS test tends to over-reject the null hypothesis. The modification by Sul et al. (2005) is based on an alternative long-run variance estimator. Harris et al. (2007) propose applying the KPSS test to the filtered series instead of to the original series. We conclude that the test based on the transformation proposed by Sul et al. outperforms the test based on the transformation proposed by Harris et al.