Nonparametric Econometrics

Term: 
Spring
Credits: 
2.0
Course Description: 

This econometrics eld course is aimed at giving a brief introduction to the statistical theory of nonparametric density and regression function estimation. I discuss several statistical and econometric applications with cross-sectional (i.i.d.) data.

Learning Outcomes: 

By successfully completing Nonparametric Econometrics students
will be able to:

  • Estimate a probability density function nonparametrically using a kernel density estimator
  • Estimate a regression function nonparametrically using various methods (kernel, local linear, series)
  • Perform basic bias-variance calculations, understand the bias-variance tradeoff fundamental to nonparametric methods
  • Select the appropriate value of smoothing parameters in practice
  • Apply these methods in various settings, e.g., in

         - estimating regression discontinuity models
         - estimating conditional average treatment effects
         - estimating sample selection models

Assessment: 

(Tentative!) Two homeworks and a take-home final (30%, 30%, 40%)

Prerequisites: 

Advanced Econometrics 2