SingleView of Module
Module (6 Credits)
Nonparametric Econometrics
- English Name
- Nonparametric 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 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
- 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
- Info for current semester
- Elements
Lecture (3 Credits)
Nonparametric Econometrics
- English Name
- Nonparametric Econometrics
- Organisational Unit
- Lehrstuhl für Ökonometrie
- Lecturers
- Prof. Dr. Christoph Hanck,
Prof. Dr. Yannick Hoga - Cycle
- irregular
- SPW
- 2
- Language
- English
- Participants
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
- Univariate density estimation
- Multivariate density estimation
- Inference about the density
- Nonparametric regression
- Smoothing discrete variables
- Regression with discrete covariates
- Semiparametric methods
- Instrumental variables
Literature
- 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
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.
Exercise (3 Credits)
Nonparametric Econometrics
- English Name
- Nonparametric Econometrics
- Organisational Unit
- Lehrstuhl für Ökonometrie
- Lecturers
- Prof. Dr. Christoph Hanck,
wissenschaftliche Mitarbeiter(innen) - Cycle
- irregular
- SPW
- 2
- Language
- English
- Participants
Preliminary knowledge
see lecture
Contents
see lecture
Literature
see lecture