A Recursive Kalman Filter Forecasting Approach

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: When appropriate, the time-varying coefficient approach leads to better forecasts than the constant coefficient procedures. A simple decision rule, which indicates whether time-varying coefficient models are in fact needed, increases the computational efficiency.

• 저널Management Science · 29(11) · 1325–1333
• 토픽Forecasting Techniques and Applications · Management Science and Operations Research
• DOI10.1287/mnsc.29.11.1325
• 저자Douglas R. Kahl, Johannes Ledolter