Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h
Q2
This biannual learning unit is being organized in 2019-2020
Teacher(s)
Hafner Christian;
Language
English
Content
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
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
Bibliography
- 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
LSBA
