Detection of additive outliers in seasonal time series

Niels Haldrup, Antonio Montañés, Andreu Sansó

    Research output: Working paper/Preprint Working paperResearch

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    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
    Original languageEnglish
    Place of publicationAarhus
    PublisherInstitut for Økonomi, Aarhus Universitet
    Number of pages18
    Publication statusPublished - 2009

    Keywords

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

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