You are not currently logged in.
Access JSTOR through your library or other institution:
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
You can always find the topics here!Topics: Analytical forecasting, Coefficients, Constant coefficients, Time series forecasting, Data smoothing, Kalman filters, Forecasting models, Forecasting techniques, Statistical forecasts, Simulations
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Preview not available
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