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A Score Test For Detection Of Time Series Outliers

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  • Bovas Abraham
  • Nihal Yatawara

Abstract

. Two characterizations, the aberrant observation and innovation models, for outliers in time series are considered. A procedure based on the well‐known score‐test is discussed for detection of outliers and distinguishing between the outlier types. Significance levels of the tests are also obtained and the method is illustrated with simulated examples.

Suggested Citation

  • Bovas Abraham & Nihal Yatawara, 1988. "A Score Test For Detection Of Time Series Outliers," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(2), pages 109-119, March.
    • Handle: RePEc:bla:jtsera:v:9:y:1988:i:2:p:109-119
    DOI: 10.1111/j.1467-9892.1988.tb00458.x
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    Cited by:

    1. Jurgen A. Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Tinbergen Institute Discussion Papers 05-092/4, Tinbergen Institute.
    2. Wai-Sum Chan, 2006. "Outliers in Nonstationary Time Series," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 4(2), pages 75-83, July.
    3. Ardelean, Vlad, 2012. "Detecting outliers in time series," FAU Discussion Papers in Economics 05/2012, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    4. Thomas Flak & Wolfgang Schmid, 1993. "An outlier test for linear processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 40(1), pages 299-318, December.

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