Department of Economics and Business Economics

Detection of additive outliers in seasonal time series

Research output: Working paperResearch

Documents

  • Rp09 40

    Final published version, 268 KB, PDF document

  • Niels Haldrup
  • Antonio Montañés, University of Zaragoza, Spain
  • Andreu Sansó, University of the Balearic Islands, Spain
  • School of Economics and Management
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
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages18
Publication statusPublished - 2009

    Research areas

  • Additive outliers, outlier detection, integrated processes, periodic heteroscedasticity, seasonality

See relations at Aarhus University Citationformats

Download statistics

No data available

ID: 17518469