 Reflections on the Teaching of Bayesian EconometricsGary Koop ()
A chapter in Teaching Econometrics, 2026, pp 39-47 from Springer Abstract: Abstract This chapter discusses my experience of teaching Bayesian econometrics over the last 30 years. It offers an overview of how the teaching of Bayesian econometrics has developed since the 1980s due to the huge improvement in computing power that has occurred. Prior to the 1980s, Bayesian research was mostly theoretical with empirical work being limited to a narrow range of models and priors. Advances in computing power, along with algorithms, which could exploit this extra computer power, revolutionized Bayesian econometrics since it could now handle a vast range of empirically-interesting models, including complicated high-dimensional models capable of handling the large data sets economists now have available. These developments in research were matched by a change in the way Bayesian econometrics is taught. This chapter offers my personal reflections on this change. My Bayesian course has evolved to place much more emphasis on Bayesian computation. Strategies are discussed for simple ways of teaching Bayesian econometrics and the associated computational methods in a relatively non-technical manner. Date: 2026 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:adschp:978-3-031-97942-2_3 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-97942-2_3 Access Statistics for this chapter More chapters in Advanced Studies in Theoretical and Applied Econometrics from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.