alle Infos im ILIAS-Kurs
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