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Additive Outlier Detection Via Extreme-Value Theory

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  • Peter Burridge
  • A. M. Robert Taylor

Abstract

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.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

Volume (Year): 27 (2006)
Issue (Month): 5 (09)
Pages: 685-701

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Handle: RePEc:bla:jtsera:v:27:y:2006:i:5:p:685-701

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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782

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Cited by:
  1. Amélie Charles & Olivier Darné, 2010. "A note on the uncertain trend in US real GNP: Evidence from robust unit root test," Working Papers hal-00547737, HAL.
  2. Gabriel Rodriguez & Dionisio Ramirez, 2013. "A Note on the Size of the ADF Test with Additive Outliers and Fractional Errors. A Reapraisal about the (Non) Stationarity of the Latin-American Inflation Series," Documentos de Trabajo 2013-357, Departamento de Economía - Pontificia Universidad Católica del Perú.
  3. 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.
  4. 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.
  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 2013-355, Departamento de Economía - Pontificia Universidad Católica del Perú.

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