Non- and semi- parametric econometrics

lstat2420  2020-2021  Louvain-la-Neuve

Non- and semi- parametric econometrics
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h

This biannual learning unit is being organized in 2020-2021
The objective of this lecture is to provide an introduction to non- and semiparametric estimation methods that are often used in econometrics. For the classical kernel density and regression estimator, the asymptotic theory will be developed in some detail. For time series regression and semiparametric models, an emphasis will be given on applications through various examples. Beyond understanding the properties, students are expected to learn how to implement the methods.
1. Nonparametric estimation
a. Kernel density estimator (properties, asymptotics, higher order kernels, density derivatives,
multivariate densities, bandwidth selection)
b. Nonparametric regression (local polynomial estimator, properties, asymptotics; time series)
2. Semiparametric estimation
a. Semiparametric eciency bounds
b. Linear regression with unknown error density
c. Partially linear model
d. Single index model
e. Semiparametric models for time series
f. Semiparametric models for panel data
  • Li, Q. and S. Racine (2007), Nonparametric Econometrics, Princeton University Press.
  • Pagan, A. and A. Ullah (1999), Nonparametric Econometrics, Cambridge University Press.
  • Ruppert, D., M.P. Wand and R.J. Carroll (2003), Semiparametric Regression, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press.
  • Yatchew, A. (2003), Semiparametric regression for the applied econometrician, Cambridge University Press.
Teaching materials
  • matériel sur moodle
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Master [120] en statistique, orientation générale