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Detection of additive outliers in seasonal time series

Niels Haldrup (), Antonio Montañés () and Andreu Sansó ()

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: 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 et al. (1996) and Perron and Rodriguez (2003) to deal with data sampled at a seasonal frequency and the size and power properties are discussed. 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.

Keywords: Additive outliers; outlier detection; integrated processes; periodic heteroscedasticity; seasonality (search for similar items in EconPapers)
JEL-codes: C12 C2 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2009-09-14
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Journal Article: Detection of Additive Outliers in Seasonal Time Series (2011) Downloads
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