In case of limited activity on campus the course will take place via Hybrid Mode.

The course discusses advanced topics in Bayesian econometrics:

a. Time series analysis based on state space models and  time-varying parameter models

b. Shrinkage and variable selection in latent variable models

b. Mixture and Markov switching models

d. Advanced computational techniques (e.g. boosting MCMC through interweaving, parallel MCMC)

The students will be able to study and criticially evaluate research and scientific papers in the field of Bayesian econometrics.  In additon, they will be able to use Bayesian inference for their own research projects.   

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

This course is taught as lectures and tutorials combined with assignments and a project which have to be solved individually by the students. In combination with the lectures, the assignments and the project help students to consolidate and expand their understanding of the theoretical and applied methods discussed in the lectures.

Grading is based on assignments (30%), a project (30%) and a final presentation (40%). 

Basic knowledge of Bayesian inference is required.