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Nicht- und Semiparametrik

Nicht- und Semiparametrik
Typ: Vorlesung mit Übung
Lehrstuhl: Lehrstuhl für Ökonometrie und Statistik
Semester: WS 2015/ 2016
Ort:

Geb. 20.14 Raum 103.1

Zeit:

Di  09:45 - 11:15 (VL),

Di 14:00 - 15:30 (Ü)

Beginn: 20.10.2015
Dozent:

Schienle, Bormann

SWS: 2+2
ECTS: 4.5
LVNr.: 2521300 (VL) und 2521301 (Ü)
Prüfung:

19.02.2016, Redtenbacher Hörsaal
 

Nicht- und Semiparametrik

alle Infos im ILIAS-Kurs

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
  1. Challenges: Boundaries and Discrete Variables
  2. Practice: Estimation of conditional pdfs, cdfs and quantiles
  1. 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