Syllabus

Titel
5567 Bayesian Econometrics I
LV-Leiter/innen
Univ.Prof. Dr. Sylvia Frühwirth-Schnatter
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
18.02.2019 bis 01.03.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Doktorat/PhD
Termine
Wochentag Datum Uhrzeit Raum
Mittwoch 06.03.2019 17:00 - 20:00 D4.4.008
Mittwoch 13.03.2019 17:00 - 20:00 D4.4.008
Mittwoch 20.03.2019 17:00 - 20:00 D4.4.008
Mittwoch 27.03.2019 17:00 - 20:00 D4.4.008
Mittwoch 03.04.2019 17:00 - 20:00 D4.4.008
Mittwoch 10.04.2019 17:00 - 20:00 D4.0.039
Mittwoch 05.06.2019 17:00 - 19:30 D4.4.008
Mittwoch 12.06.2019 17:00 - 19:30 D4.4.008
Mittwoch 26.06.2019 17:00 - 19:30 D4.4.008

Inhalte der LV

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

Lernergebnisse (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

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 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.

Leistung(en) für eine Beurteilung

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.

Literatur

1 Autor/in: Edward Greenberg
Titel: Introduction to Bayesian Econometrics

Verlag: Cambridge University Press
Auflage: 1. Auflage
Jahr: 2008
Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Stark empfohlen (aber nicht absolute Kaufnotwendigkeit)
Art: Buch

Empfohlene inhaltliche Vorkenntnisse

Basic knowledge in probability theory and statistics

Erreichbarkeit des/der Vortragenden

sylvia.fruehwirth-schnatter@wu.ac.at
Zuletzt bearbeitet: 07.11.2018



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