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Reallocation Outliers in Time Series

Author

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  • Lilian Shiao‐Yen Wu
  • J. R. M. Hosking
  • Nalini Ravishanker

Abstract

Time series data often contain outliers which have an effect on parameter estimates and forecasts. Outliers in isolation have been well studied. However, in business and economic data, it is common to see unusually low observations followed by unusually high observations or vice versa. We model this behaviour by using a new type of multiple time period outlier which we call a reallocation, defined to be a block of unusually high and low values occurring in such a way that the sum of the observations within the block is the same as might have been expected for an undisturbed series. We derive tests for detecting reallocation outliers and distinguishing them from additive outliers. We show the effect on forecasts and forecast intervals of ignoring reallocation outliers. Finally, we apply our methods to two example data sets.

Suggested Citation

  • Lilian Shiao‐Yen Wu & J. R. M. Hosking & Nalini Ravishanker, 1993. "Reallocation Outliers in Time Series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(2), pages 301-313, June.
    • Handle: RePEc:bla:jorssc:v:42:y:1993:i:2:p:301-313
    DOI: 10.2307/2986234
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    Cited by:

    1. Webel, Karsten & Smyk, Anna, 2023. "Towards seasonal adjustment of infra-monthly time series with JDemetra+," Discussion Papers 24/2023, Deutsche Bundesbank.
    2. Beatriz Catalan & F. Javier Trivez, 2007. "Forecasting volatility in GARCH models with additive outliers," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 591-596.
    3. Andy Lee & John Yick & Yer Van Hui, 2001. "Sensitivity of the portmanteau statistic in time series modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 691-702.
    4. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.

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