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Additive Outlier Detection Via Extreme-Value Theory


  • Peter Burridge
  • A. M. Robert Taylor


This article is concerned with detecting additive outliers using extreme value methods. The test recently proposed for use with possibly non-stationary time series by Perron and Rodriguez [Journal of Time Series Analysis (2003) vol. 24, pp. 193-220], is, as they point out, extremely sensitive to departures from their assumption of Gaussianity, even asymptotically. As an alternative, we investigate the robustness to distributional form of a test based on weighted spacings of the sample order statistics. Difficulties arising from uncertainty about the number of potential outliers are discussed, and a simple algorithm requiring minimal distributional assumptions is proposed and its performance evaluated. The new algorithm has dramatically lower level-inflation in face of departures from Gaussianity than the Perron-Rodriguez test, yet retains good power in the presence of outliers. Copyright 2006 The Authors Journal compilation 2006 Blackwell Publishing Ltd.

Suggested Citation

  • Peter Burridge & A. M. Robert Taylor, 2006. "Additive Outlier Detection Via Extreme-Value Theory," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 685-701, September.
  • Handle: RePEc:bla:jtsera:v:27:y:2006:i:5:p:685-701

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    References listed on IDEAS

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    Cited by:

    1. Olivier Darné & Amélie Charles, 2012. "A note on the uncertain trend in US real GNP: Evidence from robust unit root tests," Economics Bulletin, AccessEcon, vol. 32(3), pages 2399-2406.
    2. Sam Astill & David I. Harvey & A. M. Robert Taylor, 2013. "A bootstrap test for additive outliers in non-stationary time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 454-465, July.
    3. Bent Nielsen & Andrew Whitby, 2015. "A Joint Chow Test for Structural Instability," Econometrics, MDPI, Open Access Journal, vol. 3(1), pages 1-31, March.
    4. Chen, Yi-Hsuan & Tu, Anthony H., 2013. "Estimating hedged portfolio value-at-risk using the conditional copula: An illustration of model risk," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 514-528.
    5. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2010. "Robust methods for detecting multiple level breaks in autocorrelated time series," Journal of Econometrics, Elsevier, vol. 157(2), pages 342-358, August.
    6. Gabriel Rodriguez & Dionisio Ramirez, 2013. "A comparison between Tau-d and the procedure TRAMO-SEATS is also included," Documentos de Trabajo / Working Papers 2013-355, Departamento de Economía - Pontificia Universidad Católica del Perú.
    7. Josep Lluís Carrion-I-Silvestre & María Dolores Gadea, 2016. "Bounds, Breaks and Unit Root Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 165-181, March.
    8. Gabriel Rodriguez & Dionisio Ramirez, 2014. "A Note on the Size of the ADF Test with Additive Outliers and Fractional Errors. A Reappraisal about the (Non)Stationarity of the Latin-American Inflation Series," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 37(73), pages 113-132.
    9. David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2009. "Robust methods for detecting multiple level breaks in autocorrelated time series [Revised to become No. 10/01 above]," Discussion Papers 09/01, University of Nottingham, Granger Centre for Time Series Econometrics.
    10. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2016. "Empirical Likelihood for Outlier Detection and Estimation in Autoregressive Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 315-336, May.

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