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Seasonal outliers in time series

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  • Kaiser Remiro, Regina
  • Maravall, Agustín

Abstract

In the analysis of time series, it is frequent to classify perturbations as Additive Outliers (AO) , Innovative Outliers (10), Level Shift (LS) outliers or Transitory Change (TC) outliers. When a time series with a clear seasonal behaviour is considered, this classification may be too restrictive since none of the four outlier types is adequate to model changes in the seasonal pattern of the series. In this paper, a new outlier type, the Seasonal level Shift (SLS), is introduced in order to complete the usual classification. The iterative procedure for the detection of outliers in Chen and Liu (1993) is extended to detect SLS outliers. We use simulations and real examples to assess the properties of the new type of outlier.

Suggested Citation

  • Kaiser Remiro, Regina & Maravall, Agustín, 1999. "Seasonal outliers in time series," DES - Working Papers. Statistics and Econometrics. WS 6333, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:6333
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    References listed on IDEAS

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    1. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    2. Victor Gómez & Agustín Maravall, 1996. "Programs TRAMO and SEATS, Instruction for User (Beta Version: september 1996)," Working Papers 9628, Banco de España.
    3. Kaiser Remiro, Regina, 1998. "Detection and estimation of structural changes and ouliers in unobserved components," DES - Working Papers. Statistics and Econometrics. WS 9847, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    5. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    6. George E. P. Box & Steven C. Hillmer & George C. Tiao, 1978. "Analysis and Modeling of Seasonal Time Series," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 309-344, National Bureau of Economic Research, Inc.
    7. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 299-307, October.
    8. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study: Response," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 313-315, October.
    9. Víctor Gómez & Agustín Maravall, 1998. "Automatic Modeling Methods for Univariate Series," Working Papers 9808, Banco de España.
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    Cited by:

    1. Regina Kaiser & Agustín Maravall, 2000. "An Application of TRAMO-SEATS: Changes in Seasonality and Current Trend-Cycle Assessment: the German Retail Trade Turnover Series," Working Papers 0011, Banco de España.
    2. Massimo Albanese, 2022. "Community Enterprises: Snapshots from Italy," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 8, ejes_v8_i.
    3. Kaiser Remiro, Regina & Maravall, Agustín, 2000. "An application of tramo-seats: changes in seasonality and current trend-cycle assesment: the german retail trade turnover series," DES - Working Papers. Statistics and Econometrics. WS 10010, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Hella, Heikki, 2003. "On robust ESACF identification of mixed ARIMA models," Bank of Finland Scientific Monographs, Bank of Finland, volume 0, number sm2003_027, December.

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    ARIMA models;

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