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Outliers in multivariate time series

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  • Tsay, Ruey S.
  • Peña, Daniel
  • Pankratz, Alan E.

Abstract

This paper considers outliers in multivariate time series analysis. It generalizes four types of disturbances commonly used in the univariate time series analysis to the multivariate case, and investigates dynamic effects of a multivariate outlier on individual components if marginal models are used. An innovational outlier of a vector series can introduce a patch of outliers for the marginal component models. The paper also proposes an iterative procedure to detect and handle multiple outliers. By comparing and contrasting results of univariate and multivariate outlier detections, one can gain insights into the characteristics of an outlier. An outlier in a component series mayor may not have significant impacts on the other components. We use real examples to demonstrate the proposed analysis.

Suggested Citation

  • Tsay, Ruey S. & Peña, Daniel & Pankratz, Alan E., 1998. "Outliers in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS 6285, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:6285
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    References listed on IDEAS

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    1. Robert E. McCulloch & Ruey S. Tsay, 1994. "Bayesian Analysis Of Autoregressive Time Series Via The Gibbs Sampler," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(2), pages 235-250, March.
    2. 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.
    3. Justel, A. & Peña, Daniel & Tsay, Ruey S., 1998. "Detection of outlier patches in autoregressive time series," DES - Working Papers. Statistics and Econometrics. WS 9821, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
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