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Automatic selective intervention in dynamic linear models

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  • Manuel Salvador
  • Pilar Gargallo

Abstract

In this paper we propose an algorithm to carry out the monitoring and retrospective intervention process in Dynamic Linear Models, both selectively and automatically. The algorithm is illustrated by analysing several series taken from the literature, in which the proposed procedure is shown to perform better than the scheme proposed by West & Harrison (1997, Chapter 11).

Suggested Citation

  • Manuel Salvador & Pilar Gargallo, 2003. "Automatic selective intervention in dynamic linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1161-1184.
    • Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1161-1184
    DOI: 10.1080/0266476032000107178
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    References listed on IDEAS

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    1. Atkinson, A. C. & Koopman, S. J. & Shephard, N., 1997. "Detecting shocks: Outliers and breaks in time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 387-422, October.
    2. Balke, Nathan S, 1993. "Detecting Level Shifts in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 81-92, January.
    3. P. J. Harrison, 1999. "Statistical process control and model monitoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(2), pages 273-292.
    4. Harvey, A. C., 1986. "The effects of seat belt legislation on British road casualities: A case study in structural modelling : A.C. Harvey and J. Durbing, Journal of the Royal Statistical Society, Series A 149 (1986) (in p," International Journal of Forecasting, Elsevier, vol. 2(4), pages 496-497.
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