Estimation in the multiprocess dynamic generlized linear model

Abstract

The dynamic generalized linear model and the dynamic discount Bayesian model have been used to describe processes involving time-varying parameters. This paper develops an estimation algorithm for the multiprocess extension of these model. These algorithms have the same characteristics as Harrison-Steven forecasting, namely insensitivity to outliers and quick reaction to real change in the parameters.

Keywords:

• Bayesain forecasting
• dynamic discount bayesian model
• fault detection
• Harrison-stevens forecasting
• Kalman filter
• multiprocess models
• state space models