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Nicht- und Semiparametrik

Nicht- und Semiparametrik
Typ: Vorlesung mit Übung
Lehrstuhl: Lehrstuhl für Ökonometrie und Statistik
Ort:

20.30 SR -1.025 (UG) (V)

20.30 SR 0.016 (Ü)

Zeit:

Di, 11:30 - 13:00 (V)

Mi, 15:45 - 17:15 (Ü)

Beginn: 18.10.2016, 26.10.2016
Dozent:

Schienle, Siebenschuh

SWS: 4
LVNr.: 2521312 (V), 2521313 (Ü)

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
  • Challenges: Boundaries and Discrete Variables
  • 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