Course Outline

I.    Nonparametrics

1. Introduction
2. Kernel Density Estimation
   1. Univariate Density Estimator
   2. Statistical Properties of the Univariate Kernel Density Estimator: consistency, asymptotic normality, choices of bandwidth
   3. Multivariate Kernel Density Estimator
   4. Applications

• Challenges: Boundaries and Discrete Variables
• Practice: Estimation of conditional pdfs, cdfs and quantiles

3. Conditional Mean Estimation
   1. Nadaraya-Watson Estimator
   2. Statistical Properties of the Local Constant Estimator: consistency, asymptotic normality, choice of bandwidth
   3. Local Polynomial Estimators
   4. Other Types of Conditional Mean Estimators
   5. Practice: Additive Conditional Mean Estimation

II.   Semiparametrics

1. Introduction
2. Examples of Semiparametric Estimation Models
   1. Regression Models
   2. Efficient Estimation
   3. 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