Estimation of Treatment Effects

Time I: 
Tuesday, 08:00am
Venue I: 
SPA1, R22

Tue, 8-10, SPA 1, 22
Tue, 14-16, SPA 1, 22

This course presents nonparametric and semiparametric regression techniques and modern microeconometric methods for treatment effects estimation. The treatment focuses on the potential outcome approach, and students learn various methods to account for selection based on observables (regression, matching, inverse probability weighting) and for selection based on unobservables (Heckman selection correction, difference-in-differences, panel regression, instrumental variable regression, regression discontinuity design). These methods are used for cross-section data and longitudinal data, both repeated cross-sections and panel data. Students will familiarize themselves with applying the methods to real empirical data using Stata.

Main References:

AP: Angrist, J. D. and J.-S. Pischke (2009): Mostly Harmless Econometrics – An Empiricist’s Companion, Princeton University Press.
CT: Cameron, A. C. and P. K. Trivedi (2005): Microeconometrics – Methods and Applications, Cambridge University Press.
GR: Greene, W. (2008): Econometric Analysis, 6th ed., International Edition, Prentice Hall.
HL: Härdle, W. and O. Linton (1994): "Applied Nonparametric Methods", in: Handbook of Econometrics, Vol. 4, R. F. Engle und O. F. McFadden, (eds.), Elsevier Science.
PU: Pagan, A. and A. Ullah (1999): Nonparametric Econometrics, Cambridge University Press.
WO: Wooldridge, J. M. (2010): Econometric Analysis of Cross Section and Panel Data. 2nd edition, Cambridge, MA: MIT Press (see also: ).

Further references, particularly regarding the method of Quantile Regression and the application of the methods, will be given in the course.

Exam: written exam (90 min)

Fall 2016
Humboldt-Universität zu Berlin