2125 Bayesian Econometrics II
Univ.Prof. Dr. Sylvia Frühwirth-Schnatter
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
16.09.2019 bis 27.09.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Doktorat/PhD
Wochentag Datum Uhrzeit Raum
Mittwoch 23.10.2019 16:00 - 19:00 D4.4.008
Mittwoch 30.10.2019 16:00 - 19:00 D4.4.008
Mittwoch 06.11.2019 16:00 - 19:00 D4.4.008
Mittwoch 13.11.2019 16:00 - 19:00 D4.4.008
Mittwoch 20.11.2019 16:00 - 19:00 D4.4.008
Mittwoch 04.12.2019 16:00 - 18:30 D4.4.008
Mittwoch 11.12.2019 16:00 - 18:30 D4.4.008
Mittwoch 18.12.2019 16:00 - 18:30 D4.4.008

Inhalte der LV

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)

Lernergebnisse (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.   

Regelung zur Anwesenheit

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.

Leistung(en) für eine Beurteilung

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



Empfohlene inhaltliche Vorkenntnisse

Basic knowledge of Bayesian inference is required.

Erreichbarkeit des/der Vortragenden
Zuletzt bearbeitet: 20.03.2019