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Sensitivity of the portmanteau statistic in time series modeling

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  • Andy Lee
  • John Yick
  • Yer Van Hui

Abstract

The portmanteau statistic is commonly used for testing goodness-of-fit of time series models. However, this lack of fit test may depend on one or several atypical observations in the series. We investigate the sensitivity of the portmanteau statistic in the presence of additive outliers. Diagnostics are developed to assess both local and global influence. Three practical examples demonstrate the usefulness of the proposed diagnostics.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:691-702
    DOI: 10.1080/02664760120059228
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    References listed on IDEAS

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    1. Johannes Ledolter, 1990. "Outlier Diagnostics In Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(4), pages 317-324, July.
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    3. Ledolter, Johannes, 1989. "The effect of additive outliers on the forecasts from ARIMA models," International Journal of Forecasting, Elsevier, vol. 5(2), pages 231-240.
    4. Wing K. Fung & C. W. Kwan, 1997. "A Note on Local Influence Based on Normal Curvature," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 839-843.
    5. Gomez, Victor & Maravall, Agustin & Pena, Daniel, 1998. "Missing observations in ARIMA models: Skipping approach versus additive outlier approach," Journal of Econometrics, Elsevier, vol. 88(2), pages 341-363, November.
    6. Sabyasachi Basu & Gregory C. Reinsel, 1996. "Relationship between Missing Data Likelihoods and Complete Data Restricted Likelihoods for Regression Time Series Models: An Application to Total Ozone Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(1), pages 63-72, March.
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