Summary
In the framework of decomposing a time series into the sum of signal components plus noise as in detrending or seasonal adjustment, we analyze the situation in which the unobserved components may be subject to the influence of sudden shifts. The kind of perturbation that such shifts cause on the observed series can be classified as an outlier, when the shift affects the noise component, or as a structural change, when the shift affects one of the signal components. The consequences of ignoring these perturbations are important for model specification, parameter estimation and forecasting. We extend and modify the iterative procedure of Chen and Liu (1993) to allow the location, classification and estimation of outliers and structural changes affecting the unobserved components of a time series.
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1This work was partially supported by the Spanish grant PB of CICYT. The author is grateful to Andrew Harvey, Agustín Maravall, Ruey S. Tsay, seminar participants at XXIII Congreso Nacional de Estadística e Investigación Operativa and two anonymous referees for helpful comments.
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Kaiser, R. Detection and estimation of structural changes and outliers in unobserved components. Computational Statistics 14, 533–558 (1999). https://doi.org/10.1007/s001800050030
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DOI: https://doi.org/10.1007/s001800050030