A Recursive Kalman Filter Forecasting Approach

Douglas R. Kahl and Johannes Ledolter
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Douglas R. Kahl: University of South Dakota, Vermillion
Johannes Ledolter: University of Iowa

Management Science, 1983, vol. 29, issue 11, 1325-1333

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.

Keywords: statistics; forecasting; time series (search for similar items in EconPapers)
Date: 1983
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:29:y:1983:i:11:p:1325-1333

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