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A Recursive Kalman Filter Forecasting Approach
Douglas R. Kahl and Johannes Ledolter
Vol. 29, No. 11 (Nov., 1983), pp. 1325-1333
Published by: INFORMS
Stable URL: http://www.jstor.org/stable/2630909
Page Count: 9
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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.
Management Science © 1983 INFORMS