Nicht- und Semiparametrik
- Typ: Vorlesung (V)
- Lehrstuhl: Lehrstuhl für Ökonometrie und Statistik
- Semester: WS 16/17
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Ort:
20.30 SR -1.025 (UG) (V)
20.30 SR 0.016 (Ü)
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Zeit:
Di, 11:30 - 13:00 (V)
Mi, 15:45 - 17:15 (Ü)
- Beginn: 18.10.2016, 26.10.2016
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Dozent:
Schienle, Siebenschuh
- SWS: 2
- LVNr.: 2521312 (V), 2521313 (Ü)
Course Outline
I. Nonparametrics
- Introduction
- Kernel Density Estimation
- Univariate Density Estimator
- Statistical Properties of the Univariate Kernel Density Estimator: consistency, asymptotic normality, choices of bandwidth
- Multivariate Kernel Density Estimator
- Applications
- Challenges: Boundaries and Discrete Variables
- Practice: Estimation of conditional pdfs, cdfs and quantiles
- Conditional Mean Estimation
- Nadaraya-Watson Estimator
- Statistical Properties of the Local Constant Estimator: consistency, asymptotic normality, choice of bandwidth
- Local Polynomial Estimators
- Other Types of Conditional Mean Estimators
- Practice: Additive Conditional Mean Estimation
II. Semiparametrics
- Introduction
- Examples of Semiparametric Estimation Models
- Regression Models
- Efficient Estimation
- Two-Step Semiparametric Estimators
Literature:
- Li, Q., Racine, J. S. (2007) Nonparametric Econometrics, Princeton University Press.
- Pagan, A. und Ullah A. (1999): Nonparametric Econometrics, Cambridge University Press.
- Härdle, W. (1992) Applied Nonparametric Regression, Cambridge University Press.
Exercises:
theoretical and practical
Course Language:
Slides in English – explanations in German