Syllabus

Title
1835 Bayesian Econometrics II
Instructors
Univ.Prof. Dr. Sylvia Frühwirth-Schnatter
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/14/20 to 10/04/20
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Wednesday 10/07/20 04:00 PM - 07:00 PM TC.4.01
Wednesday 10/14/20 04:00 PM - 07:00 PM TC.4.01
Wednesday 10/21/20 04:00 PM - 07:00 PM TC.4.01
Wednesday 10/28/20 04:00 PM - 07:00 PM TC.4.01
Wednesday 11/11/20 04:00 PM - 07:00 PM Online-Einheit
Wednesday 12/09/20 04:00 PM - 06:30 PM Online-Einheit
Wednesday 01/13/21 04:00 PM - 06:30 PM Online-Einheit
Wednesday 01/27/21 04:00 PM - 07:30 PM Online-Einheit
Procedure for the course when limited activity on campus

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

 

Contents

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)

Learning outcomes

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.   

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 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.

Assessment

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

Readings
1
Recommended previous knowledge and skills

Basic knowledge of Bayesian inference is required.

Availability of lecturer(s)
sylvia.fruehwirth-schnatter@wu.ac.at
Last edited: 2020-06-25



Back