Syllabus

Titel
1835 Bayesian Econometrics II
LV-Leiter/innen
Univ.Prof. Dr. Sylvia Frühwirth-Schnatter
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
14.09.2020 bis 04.10.2020
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Doktorat/PhD
Termine
Wochentag Datum Uhrzeit Raum
Mittwoch 07.10.2020 16:00 - 19:00 TC.4.01
Mittwoch 14.10.2020 16:00 - 19:00 TC.4.01
Mittwoch 21.10.2020 16:00 - 19:00 TC.4.01
Mittwoch 28.10.2020 16:00 - 19:00 TC.4.01
Mittwoch 11.11.2020 16:00 - 19:00 Online-Einheit
Mittwoch 09.12.2020 16:00 - 18:30 Online-Einheit
Mittwoch 13.01.2021 16:00 - 18:30 Online-Einheit
Mittwoch 27.01.2021 16:00 - 19:30 Online-Einheit

Ablauf der LV bei eingeschränktem Campusbetrieb

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

 

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.

Lehr-/Lerndesign

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%). 

Literatur

1

Empfohlene inhaltliche Vorkenntnisse

Basic knowledge of Bayesian inference is required.

Erreichbarkeit des/der Vortragenden

sylvia.fruehwirth-schnatter@wu.ac.at
Zuletzt bearbeitet: 25.06.2020



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