1317 Bayesian Econometrics I
Univ.Prof. Dr. Sylvia Frühwirth-Schnatter
Weekly hours
Language of instruction
10/02/23 to 11/10/23
Registration via LPIS
Notes to the course
Day Date Time Room
Wednesday 11/15/23 04:00 PM - 06:30 PM TC.3.21
Wednesday 11/22/23 04:00 PM - 06:30 PM TC.3.21
Wednesday 11/29/23 04:00 PM - 06:30 PM TC.3.21
Wednesday 12/06/23 04:00 PM - 06:30 PM TC.3.21
Wednesday 12/13/23 04:00 PM - 06:30 PM TC.3.21
Wednesday 12/20/23 04:00 PM - 06:30 PM TC.3.21
Wednesday 01/10/24 04:00 PM - 06:30 PM TC.3.21
Wednesday 01/31/24 11:45 AM - 04:30 PM D4.0.127

The course provides an introduction to Bayesian econometrics.  Topics covered by course are

I. Fundamentals of Bayesian econometrics:
  • Bayesian inference 
  • Simulation techniques

II. Applications:

  • Linear regression and extensions
  • Time series
  • Endogeneity and sample selection
  • Multivariate responses and panel data
Learning outcomes
After completing this course the student will have the ability to:
  • Recall the basic principle of Bayesian econometrics
  • Apply public domain packages for Bayesian econometrics and to analyse and evaluate the output of such packages
  • Design and implement computer programs for solving computational problems in Bayesian inference for commonly applied econometrics models
Attendance requirements

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

Teaching/learning method(s)
This course is taught as lectures and tutorials combined with course projects which have to be solved individually by the students. In combination with the lectures, the course projects will help students to consolidate and expand their understanding of the theoretical and applied methods discussed in the lectures. For the course project, students create a report or a presentation of an R-based solution to a given computational task in Bayesian econometrics.

Assessment is based on five home assignments where students solve case studies as well as a project presentation of applied research involving Bayesian methods at the end of the term. Each home assignment accounts for 10% of the grade, whereas the project presentation accounts for 50% of the grade.

The assessment of the home assigment is based on the correctness of results. Assessment of the presentation is based the clarity of the presentation, the ability to describe and apply the key concepts discussed throughout the course, and the recognizable effort made.


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Recommended previous knowledge and skills

Basic knowledge in probability theory and statistics

Availability of lecturer(s)
Last edited: 2023-03-16