Forecasting with incomplete data
The Bayesian method is used to forecast with incomplete data. This involves the application of the Kalman filter technique. It is shown that this is an appropriate procedure. Among many advantages, the Kalman filter algorithm can generate forecasts with paucity of data, and it is possible to calculate the exact likelihood function involved. Several models are presented in their state space representations, and their uses in forecasting are discussed.