This course will provide a practical introduction to nonparametric and semiparametric estimation techniques.
Helpful textbook coverage can be found in:
- Applied nonparametric regression, Wolfgang Hardle,Cambridge : Cambridge University Press, 1990
- Density estimation for statistics and data analysis, B.W. Silverman, London : Chapman and Hall, 1986.
- Semiparametric regression for the applied econometrician, Adonis Yatchew, Cambridge: Cambridge University Press, 2003.
- Nonparametric curve estimation : methods, theory and applications, Sam Efromovich,
New York : Springer, 1999.
Aims: Introduction to Nonparametric Regression (with simple Stata commands)
This will take place in the Computer Lab. A do-file for the class can be found here.
Slides are here.
Aims: More technical details on Kernel methods and the Curse.
Slides are here
Short exericse set is here.
Aims: Introduction to key Semiparametric methods (the partially linear model and single index model)
Slides are here
Extra Lectures on Structural Estimation
Introduction to Different Approaches
- J. Heckman & E. Vytlacil, ‘Econometric evaluation of social programs, Part I‘, in Handbook of Econometrics, Volume 6B, 2007 ( Sections 1-4)
- M. Keane, ‘Structural vs Atheoretic Approaches to Econometrics‘, Journal of Econometrics, 2010
- G. Imbens, ‘Better LATE than Nothing‘, Journal of Economic Literature, 2010
- A. Lewbel, ‘The Identification Zoo – Meanings of Identification in Econometrics‘, Journal of Economic Literature, forthcoming
Overview of Key Structural Methods
- A. Adams, D. Clarke and S. Quinn, ‘Microeconometrics and MATLAB: An Introduction’, Chapters 3 and 4
- L. Lockwood, ‘Incidental Bequests‘, 2017