- Contents
- Introduction
- Bayes theorem and its implications
- Recursive Bayesian Estimation, one dependent and one explanatory variable
- Recursive Bayesian Estimation, one dependent and several explanatory variables
- Recursive Bayesian Estimation, one dependent and one explanatory variable; some results
- Recursive Bayesian Estimation, several dependent and several explanatory variables
- Past applications of the Kalman Filter
- The benefits of Kalman Filter models compared to conventional methods; a Monte Carlo study
- A Kalman Filter estimated model of energy demand; one dependent and several explanatory variables
- A Kalman Filter estimated model of wool demand; multiple dependent and several explanatory variables
- Part 2
- Part 3
- Ideas for further work
- Summary, discussion, main conclusions and recommendations
- Abbreviations and notation
- The Kalman Filter program
- The Kalman Filter
- The recursive Bayesian estimation programs on the Texas Instruments programmable calculator.
- References.