Bayesian Econometrics

The widespread use of prior information in formulating, estimating, and using
econometric models is reviewed. Attempts to avoid the use of prior information by
formulating multivariate statistical VAR and ARMA time series models for economic time
series data have resulted in heavily over-parametrized models. A simple demand, supply,
and entry model is presented to contrast models utilizing prior information provided by
economic theory and other sources with multivariate statistical time series models. Formal
Bayesian methods for incorporating prior information in econometric estimation, testing,
and prediction are presented. A number of published applied Bayesian studies are cited
in which Bayesian methods have proved to be effective. It is concluded that wise use of
the Bayesian approach will produce improved econometric results.
1. INTRODUCTION
I AM GRATEFUL to have this opportunity to share some of my thoughts on Bayesian
econometrics with you. Before doing this, I would like to say that I have great
admiration and respect for the work of Irving Fisher and Henry Schultz. Henry
Schultz, who spent many years at the University of Chicago, made many significant
research contributions. Similarly, Irving Fisher's research has had a profound
effect on economics and econometrics. While I could spend the entire lecture
attempting to summarize their research, I shall just emphasize that both of them
produced key results relating to relatively simple models that have endured over
the years. For example, Fisher put forward the famous Fisher equation that
relates the nominal interest rate to the anticipated real rate and the anticipated
rate of inflation. Often, when I am asked, "Are there any laws in economics?"
I point to the Fisher equation as an example. Schultz worked on the laws of
supply and demand, relatively simple relationships that are additional examples
of sophisticatedly simple laws in economics. I shall discuss the role of "simplicity"
in model-building later in my lecture.