Bayesian Econometrics

Information about Impact of Corona Epidemic

Due to the ongoing restrictions we will teach this course online in the summer term 2021. Please visit the course website on StudOn for further information.


This course is an introduction to Bayesian statistics. It focuses primarily on models­ that are used in economics. The course will give students the theoretical knowledge and practical skills to apply Bayesian techniques in a wide range of empirical applications. We will start with the foundations of Bayesian statistics and then cover estimation of linear and non-linear regression models as well as probit models and Bayesian vectorautoregressions. The course will also cover various numerical methods needed so estimate those models. Students will learn how to practically implement the covered methods using the software R.

At the end of the course, students can use the covered methods on their own and will understand academic papers that use Bayesian methods for empirical analyses. They will also be able to recognize and apply Bayesian reasoning in various contexts.


This course is regularly taught in the summer semester. The course language is English. The course can be used as a module for the following Master programs:

  • Master in Economics
  • Master in Marketing
  • Master in Finance, Auditing, Controlling, Taxation (FACT)
  • Master in Labour Market and Human Resources
  • Master in Socioeconomics

The course consists of weekly lectures (2 SWS) and exercise sessions (2 SWS). The latter focus primarily on the implementation of econometric methods in R. The lectures are taught by Dr. Alexander Glas in the summer term 2021 and the exercise sessions are organized by Daniel Perico.

The course is worth 5 ECTS which can be earned by passing an oral exam (20 minutes) at the end of the semester.

The date for the oral exam at the end of the summer semester 2021 will be announced soon.


More information about dates, grading, the software that is used, and the course in general can be found in the syllabus.