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A Recursive Kalman Filter Forecasting Approach

Author

Listed:
  • Douglas R. Kahl

    (University of South Dakota, Vermillion)

  • Johannes Ledolter

    (University of Iowa)

Abstract

This paper examines the forecasting accuracy and the cost effectiveness of time series models with time-varying coefficients. A simulation study investigates the potential forecasting benefits of a proposed Kalman filter type adaptive estimation and forecasting approach. It is found that: (1) When appropriate, the time-varying coefficient approach leads to better forecasts than the constant coefficient procedures. (2) A simple decision rule, which indicates whether time-varying coefficient models are in fact needed, increases the computational efficiency.

Suggested Citation

  • Douglas R. Kahl & Johannes Ledolter, 1983. "A Recursive Kalman Filter Forecasting Approach," Management Science, INFORMS, vol. 29(11), pages 1325-1333, November.
    • Handle: RePEc:inm:ormnsc:v:29:y:1983:i:11:p:1325-1333
    DOI: 10.1287/mnsc.29.11.1325
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    Citations

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    Cited by:

    1. Clinebell, John M. & Kahl, Douglas K. & Stevens, Jerry L., 1996. "Time series estimation of the bond default risk premium," The Quarterly Review of Economics and Finance, Elsevier, vol. 36(4), pages 475-484.
    2. Thomas C. Chiang & Douglas R. Kahl, 1991. "Forecasting The Treasury Bill Rate: A Time-Varying Coefficient Approach," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 14(4), pages 327-336, December.
    3. Lakshman Alles & Ramaprasad Bhar, 1997. "The information on inflation in the Australian term structure," Applied Financial Economics, Taylor & Francis Journals, vol. 7(6), pages 721-730.

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