fine numerical charts and graphs on paper

About the course

Gary Koop will run a short-course on the mixed frequency Vector autoregressive model (MF-VAR). 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 seeing how Bayesian methods work in the familiar context of the regression model.

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 (VARs) and state space models. An application of mixed frequency VAR modelling to regional nowcasting completes the course.


Any enquiries can be emailed to Rodney Strachan at

About the presenter

Gary Koop is Professor of Economics at the University of Strathclyde. He has published numerous articles in Bayesian econometrics and statistics in journals such as Journal of Econometrics, Journal of the American Statistical Association and the Journal of Business and Economic Statistics. He has been an associate editor for several journals, including Journal of Econometrics and Journal of Applied Econometrics. He is the author of the books Bayesian Econometrics, Analysis of Economic Data and Analysis of Financial Data.

Course fees

The course is free for full-time students and $400 for industry and academics for the two days.


Register now 


Colin Clark Building (#39)
The University of Queensland
Room 103

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