Bayesian methods are increasingly used in econometrics, particularly in the field of macroeconomics. This is a course in Bayesian econometrics with a focus on the models used in empirical macroeconomics. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and showing how Bayesian methods work in the familiar context of the regression model. Computational methods are of great importance in modern Bayesian econometrics and these are discussed in in detail. In light of the Big Data revolution, applied economists often face the situation where the number of variables under consideration is large relative to the number of observations and conventional econometric methods do not work well. We describe various methods that can be used with Big Data in the context of the regression model and emphasize the wider applicability of these methods in other modelling contexts. Subsequently, the course shows how Bayesian methods are used with models which are currently popular in macroeconomics such as vector autoregressions, state space models (including factor models and stochastic volatility models) and nonparametric methods such as regression trees.
We are very thankful to Gary Koop, who previously taught this course. Gary has not only developed the core teaching materials for this class but also shaped the whole Bayesian econometrics field over the last three decades.
Lecturer & Course Organiser: Niko Hauzenberger (niko.hauzenberger@strath.ac.uk; nhauzenb.github.io)
Lecturer: Ping Wu (ping.wu@strath.ac.uk; pingwu.org)
Tutor: TBC
Office Hours: Immediately after the lecture or online via Zoom (in this case email in advance)
This course takes place in block 4 (semester 2) over six weeks and involves lectures and computer sessions. The lectures are given by Niko Hauzenberger and Ping Wu, while the computer sessions are given by […]. You can find out more about the teaching team from their websites.
The topics covered in the lectures include:
Assessment will be through an empirical project and a journal article summary. The empirical project will be worth 60% of the final grade and the journal article summary worth 40%. There will be no exam. Detailed instructions for each are available on the course website. The deadline for the journal article summary will be 12 noon, 10 April 2026 and the deadline for the empirical project will be 12 noon, 29 May 2026. Detailed written feedback and marks will be returned within two weeks of assessment submission.
The primary readings for the course are two textbooks (one of which is a book of solved exercises) which provide much more detail (and computer code in the case of the book of solved exercises) for most of the topics covered in the course:
Koop, G. (2003) Bayesian Econometrics.
Chan, J., Koop, G., Poirier, D. and Tobias, J. (2019). Bayesian Econometric Methods (second edition).
Additional handbooks, book chapters and monographs on relevant topics include:
Colleagues and co-authors who provide excellent code repositories and replication packages for published papers, books, and teaching: Joshua Chan, Florian Huber, Karin Klieber, Gary Koop, Dimitris Korobilis, Michael Pfarrhofer, Aubrey Poon, Tomasz Woźniak.