A Recursive Kalman Filter Forecasting Approach
AbstractThis 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.
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Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 29 (1983)
Issue (Month): 11 (November)
statistics; forecasting; time series;
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- 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.
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