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.

Participants

• BWL-EaF-Ma-2015 > Wahlpflichtbereich > (1st-3rd Semester, Elective) Modul "Bayesian Econometrics"
• ECMX-Ma-2019 > Wahlpflichtbereich > ME7 Econometric Methods > (1st-3rd Semester, Elective) Modul "Bayesian Econometrics"
• GOEMIK-Ma-2016 > Wahlpflichtbereich > Bereich Volkswirtschaftslehre > (1st-3rd Semester, Elective) Modul "Bayesian Econometrics"
• MuU-Ma-2013 > Wahlpflichtbereich I > Wahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen > (1st-2nd Semester, Elective) Modul "Bayesian Econometrics"
• VWL-Ma-2009-V2013 > Wahlpflichtbereich I > (1st-3rd Semester, Elective) Modul "Bayesian Econometrics"