Bayesian Econometrics

Module: Bayesian Econometrics (6 Credits)
Name in diploma supplement	Bayesian Econometrics
Responsible	Prof. Dr. Christoph Hanck
Admission criteria	See exam regulations.
Workload	180 hours of student workload, in detail: Attendance: 60 hours Preparation, follow up: 60 hours Exam preparation: 60 hours
Duration	The module takes 1 semester(s).
Qualification Targets	Students acquire comprehensive knowledge of the Bayesian statistical paradigm and associated tools know how to apply these to address applied questions in economics and related disciplines identify and clean relevant data to do so are proficient in taking an analysis from an empirical question to a suitable econometric model assess the strengths and limitations of their empirical results can assess the mathematical and statistical properties of core methods and are able to formally establish these independently program and apply statistical software and code to practically use the methods in practice independently tackle a range of theoretical problem sets
Relevance	The practical relevance of the module is high in view of the key and increasing importance of empirical work in economics and elsewhere.
Module Exam	Examination for this module takes place through a written exam (typically 60-90 minutes), or an oral exam (typically 20-40 minutes), or an empirical project (70% of the final grade) combined with a presentation (typically 20 minutes, 30% of the final grade). The type of examination will be communicated at the start of the semester.
Usage in different degree programs	BWL EaF Master > Wahlpflichtbereich > 1.-3. Sem, Elective ECMX Master > Wahlpflichtbereich > ME7 Econometric Methods > 1.-3. Sem, Elective GOEMIK Master > Wahlpflichtbereich > Bereich Volkswirtschaftslehre > 1.-3. Sem, Elective MuU Master > Wahlpflichtbereich I > Wahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen > 1.-2. Sem, Elective VWL Master > Wahlpflichtbereich I > 1.-3. Sem, Elective
Elements	Lecture Bayesian Econometrics (3 Credits) Exercise Bayesian Econometrics (3 Credits)
Module: Bayesian Econometrics (WIWI‑M0939)

Lecture: Bayesian Econometrics (3 Credits)
Name in diploma supplement	Bayesian Econometrics
Organisational Unit	Lehrstuhl für Ökonometrie
Lecturers	Prof. Dr. Christoph Hanck
Hours per week	2	Language	English
Cycle	irregular	Participants at most	###LABEL_NOLIMIT###
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.
Lecture: Bayesian Econometrics (WIWI‑C1205)

Exercise: Bayesian Econometrics (3 Credits)
Name in diploma supplement	Bayesian Econometrics
Organisational Unit	Lehrstuhl für Ökonometrie
Lecturers	Prof. Dr. Christoph Hanck, wissenschaftliche Mitarbeiter(innen)
Hours per week	2	Language	English
Cycle	irregular	Participants at most	###LABEL_NOLIMIT###
Preliminary knowledge see lecture
Contents see lecture
Literature see lecture
Exercise: Bayesian Econometrics (WIWI‑C1208)