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Multipass Seasonal Adjustment Filter

Author

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  • Tep Sastri

    (Industrial Engineering Department, Texas A&M University, College Station, Texas 77843-3131)

Abstract

A state-space seasonal time series model and a new seasonal decomposition algorithm, based on the Kalman filter, are introduced. The time series model is statistically equivalent to the multiplicative seasonal model, ARIMA (0, 1, 1)(0, 1, 1) s , of Box and Jenkins. It is shown that the steady-state filter's forecasts of this model are identical to the Box and Jenkins' values. The seasonal adjustment and decomposition algorithm is based on a multipass filtering technique for back forecasting and smoothing in order to correct start-up transients and replace lost filter's estimates during the initialization phase. The in-sample performances of this multipass seasonal adjustment filter (MSAF) are compared with the Census X-11 procedure, using real time series. The empirical results clearly show the superiority of the proposed method for all time series in the study. Additionally, a sample from the Makridakis-Hibon's 111 time series is used for ex-post forecasting evaluation of the proposed method in comparison to the Winters and simple ratio-to-moving-average methods. It is observed that the MSAF forecasts are better than its competitors in most cases, especially when leadtimes are at least one season length.

Suggested Citation

  • Tep Sastri, 1989. "Multipass Seasonal Adjustment Filter," Management Science, INFORMS, vol. 35(1), pages 100-123, January.
    • Handle: RePEc:inm:ormnsc:v:35:y:1989:i:1:p:100-123
    DOI: 10.1287/mnsc.35.1.100
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