Informationen zu den Modulen
Modul (6 Credits)
Nonparametric 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 broad knowledge of modern nonparametric methods from both statistics and econometrics
- are proficient to use these to empirically investigate topics in economics and related fields
- gather and process data to do so
- critically comment on published empirical findings as well as on limitations of own analyses
- can assess and formally demonstrate the theoretical properties of the most central methods
- independently apply and extend statistical software to practically conduct empirical work
- solve suitable methodological 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
- Infos für das aktuelle Semester
- Bestandteile
Vorlesung (3 Credits)
Nonparametric Econometrics
- Anbieter
- Lehrstuhl für Ökonometrie
- Lehrende
- Prof. Dr. Christoph Hanck,
Prof. Dr. Yannick Hoga - Turnus
- unregelmäßig
- SWS
- 2
- Sprache
- englisch
- Hörerschaft
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
- Univariate density estimation
- Multivariate density estimation
- Inference about the density
- Nonparametric regression
- Smoothing discrete variables
- Regression with discrete covariates
- Semiparametric methods
- Instrumental variables
Literaturangaben
- Hayashi, F. (2000). Econometrics. Princeton: Princeton Univ. Press.
- Henderson, D. J.; Parmeter, C. F. (2015). Applied Nonparametric Econometrics. New York: Cambridge University Press
- Li, Q.; Racine, J. S. (2006). Nonparametric Econometrics: Theory and Parctice. Princeton University 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)
Nonparametric Econometrics
- Anbieter
- Lehrstuhl für Ökonometrie
- Lehrende
- Prof. Dr. Christoph Hanck,
wissenschaftliche Mitarbeiter(innen) - Turnus
- unregelmäßig
- SWS
- 2
- Sprache
- englisch
- Hörerschaft
empfohlenes Vorwissen
see lecture
Lehrinhalte
see lecture
Literaturangaben
see lecture