Lecture

Bayesian Econometrics

Name in diploma supplement

Bayesian Econometrics

Organisational Unit

Lehrstuhl für Ökonometrie

Lecturers

Prof. Dr. Christoph Hanck

SPW

2

Language

English

Cycle

irregular

Participants at most

no limit

Participants

• BWL EaF MasterWahlpflichtbereich 1.-3. Sem, Elective, Module Bayesian Econometrics
• ECMX MasterWahlpflichtbereichME7 Econometric Methods 1.-3. Sem, Elective, Module Bayesian Econometrics
• GOEMIK MasterWahlpflichtbereich Bereich Volkswirtschaftslehre 1.-3. Sem, Elective, Module Bayesian Econometrics
• MuU MasterWahlpflichtbereich IWahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen 1.-2. Sem, Elective, Module Bayesian Econometrics
• VWL MasterWahlpflichtbereich I 1.-3. Sem, Elective, Module Bayesian Econometrics

Preliminary knowledge

Knowledge of basic econometric concepts such as communicated in our bachelor and master courses “Einführung in die Ökonometrie" and “Methoden der Ökonometrie“ as well as good working knowledge of mathematical statistics.

Contents

• Bayesian inference
• Classical simulation methods
• Markov chains
• Markov chain Monte-Carlo methods
• Gibbs-Sampler, Metropolis-Hastings algorithm
• Applications, such as linear regression, Lasso, (multivariate) time series, latent variable models

Literature

• Greenberg, E. (2013). Introduction to Bayesian econometrics (2. Aufl.). Cambridge: Cambridge University Press.
• Hayashi, F. (2000). Econometrics. Princeton: Princeton Univ. Press.

Teaching concept

Classes are organized around traditional lectures. Students are however expected to contribute intensively through active discussion. Lectures are complemeted via, e.g., illustrations in R, joint interactive programming to better understand the statistical concepts as well as comprehensive problem sets to deepen students’ proficiency.