
Course Description
The purpose of this lecture sequence is to introduce students and researchers
to the key ideas of nonlinear econometric models, which can be applied to
many areas of modern economic analysis.
A solid background of basic mathematical statistics and econometrics is
helpful to understand the materials covered in this course.
If you still need to be tooled up in basic econometrics training, even
though you may still learn a great deal from this lecture sequence,
you are unlikely to be able to garner the full benefit of attending
this lecture.
Reading Materials
There is no required textbook for the lectures. It is based on
reading a collection of articles and lecture notes.
Requirements
It will be very useful if you can read the papers before attending class,
to internalize the concepts and to form the basis of classroom discussions.
Participation in classroom discussion will always be beneficial to you.
Schedule
Contact Information:
Class meeting time:
Instructor office hours:
The teaching assistant is Li Sheng
TA Contact Information:
TA office hours:
Course Outline
 Review of Probability Theory and Asymptotic Distribution
 Distribution Theory for Nonlinear Extreme Estimators
 Kernel based nonparametric regression method
 Quantile Regression Methods
 Treatment Effect Models
 Propensity Score Matching
 Endogenous Treatments
 Heckman 1990,
Varieties of Selection Bias
 Imbens and Angrist,
Identification and Estimation of Local Average Treatment Effects
 Abadie, Angrist and Imbens,
Instrumental Variables Estimates of Quantile Treatment Effects

Notes about Abadie, Angrist and Imbens
 Vytlacil,
Independence, Monotonicity, and Latent Index Models: An Equivalence Result
 Chernozhukov and Hansen,
An IV Model of Quantile Treatment Effects
 Heckman, Urzua and Vytlacil,
Understanding Instrumental Variables in Models with Essential Heterogeneity.
 Heckman and Vytlacil,
Structural Equations, Treatment Effects, and Econometric Policy Evaluation
 Basic Numerical Skills
 Random Number Generation
 Notes, Written by Jason Blevins of Duke University
 Basic Numerical Optimization
 Numerical Equation Solver
 Numerical Integration
 Kenneth Judd's "Numerical Methods in Economics"
 Numerical Optimization
 Simulation Estimation
 Bayesian Methods
 Resampling Methods
Problem Sets
This is a tentative and incomplete outline, and is subject to change
and addition. Check this web page frequently for updates.

