Forecasting Models for Different Horizons

A general class of models is introduced to provide robust and practically simple forecasts for different horizons.  These are obtained in the presence of both high and low frequencies in the data.  A sub-model is constructed to estimate low frequency state parameters and the results obtained are used as conditional information in modelling the high frequency parameters.  All parameters have probability distributions and the discount principle of Ameen and Harrison (1985) is used in the posterior‑prior state distribution transitions.  A number of limiting results and special cases are also discussed