Outlying observations in time series influence parameter estimation and testing procedures, leading to biased estimates and spurious test decisions. Further inference based on these results will be misleading. In this paper the effects of outliers on the performance of ratio-based tests for a change in persistence are investigated. We consider two types of outliers, additive outliers and innovative outliers. Our simulation results show that the effect of outliers crucially depends on the outlier type and on the degree of persistence of the underlying process. Additive outliers deteriorate the performance of the tests for high degrees of persistence. In contrast, innovative outliers do not negatively influence the performance of the tests. Since additive outliers lead to severe size distortions when the null hypothesis under consideration is described by a nonstationary process, we apply an outlier detection method designed for unit-root testing. The adjustment of the series results in size improvements and power gains. In an empirical example we apply the tests and the outlier detection method to the G7 inflation rates.
Additive Outliers Innovative Outliers Change in Persistence Outlier Detection Monte Carlo