Informationen zu den Modulen
Modul (6 Credits)
Bayesian Econometrics
- Name im Diploma Supplement
- Bayesian Econometrics
- Verantwortlich
- Prof. Dr. Christoph Hanck
- Voraussetzungen
- Siehe Prüfungsordnung.
- Workload
- 180 Stunden studentischer Workload gesamt, davon:
- Präsenzzeit: 60 Stunden
- Vorbereitung, Nachbereitung: 60 Stunden
- Prüfungsvorbereitung: 60 Stunden
- Dauer
- Das Modul erstreckt sich über 1 Semester.
- Qualifikationsziele
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
- Praxisrelevanz
The practical relevance of the module is high in view of the key and increasing importance of empirical work in economics and elsewhere.
- Prüfungsmodalitäten
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.
- Verwendung in Studiengängen
- Bestandteile
Vorlesung (3 Credits)
Bayesian Econometrics
- Name im Diploma Supplement
- Bayesian Econometrics
- Anbieter
- Lehrstuhl für Ökonometrie
- Lehrperson
- Prof. Dr. Christoph Hanck
- Turnus
- unregelmäßig
- SWS
- 2
- Sprache
- englisch
- maximale Hörerschaft
- unbeschränkt
- Hörerschaft
- vgl. Modul
empfohlenes Vorwissen
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.
Lehrinhalte
- 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
Literaturangaben
- Greenberg, E. (2013). Introduction to Bayesian econometrics (2. Aufl.). Cambridge: Cambridge University Press.
- Hayashi, F. (2000). Econometrics. Princeton: Princeton Univ. Press.
didaktisches Konzept
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.
Übung (3 Credits)
Bayesian Econometrics
- Name im Diploma Supplement
- Bayesian Econometrics
- Anbieter
- Lehrstuhl für Ökonometrie
- Lehrperson
- Prof. Dr. Christoph Hanck, wissenschaftliche Mitarbeiter(innen)
- Turnus
- unregelmäßig
- SWS
- 2
- Sprache
- englisch
- maximale Hörerschaft
- unbeschränkt
- Hörerschaft
- vgl. Modul
empfohlenes Vorwissen
see lecture
Lehrinhalte
see lecture
Literaturangaben
see lecture