Detection of additive outliers in seasonal time series

Publikation: Working paperForskning

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  • Niels Haldrup
  • Antonio Montañés, University of Zaragoza, Spanien
  • Andreu Sansó, University of the Balearic Islands, Spanien
  • Institut for Økonomi
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
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider18
StatusUdgivet - 2009

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