Detection of Additive Outliers in Seasonal Time Series

Abstract

[eng] The detection and location of additive outliers in integrated variables has attracted much attention recently because such outliers tend to affect unit root inference among other things. Most of these procedures have been developed for non-seasonal processes. However, the presence of seasonality in the form of seasonally varying means and variances affect the properties of outlier detection procedures, and hence appropriate adjustments of existing methods are needed for seasonal data. In this paper we suggest modifications of tests proposed by Shin, Sarkar and Lee (1996) and Perron and Rodriguez (2003) to deal with data sampled at a seasonal frequency and we discuss their size and power properties. We also show that the presence of periodic heteroscedasticity will inflate the size of the tests and hence will tend to identify an excessive number of outliers. A modified Perron-Rodriguez test which allows periodically varying variances is suggested, and it is shown to have excellent properties in terms of both power and size.