Applied Advanced Econometrics

Course Syllabus

Fall 2016, TTH 6:40-8:30pm (CH-307)
Prof. K.-P. Lin (CH-241G, 725-3931)
Office Hours: TTH 3:30-4:30pm or by Appointment
(Last updated: 11/10/2016)

This course covers advanced topics related to methodological issues in econometrics, with emphases on computation intensive methods and applications. We assumes background knowledge of linear econometric models, and study the nonlinear models and optimization techniques. The purpose of this course is to prepare students with a broad coverage of econometric methods and applications capable of doing independent research project. In addition to economic theory, knowledge of mathematics, statistics, and basic econometrics is required. Experience of computer programming is helpful but not necessary. Software R is used throughout the course.

Texts

W. H. Greene, Econometric Analysis, 7th ed., Pearson Education, 2012.
J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, 2nd ed., The MIP Press, 2010 (Recommended).
A. Colin Cameron and Pravin K. Trivedi, Microecometrics: Methods and Applications, Cambridge University Press, 2005 (Recommended).

Software

Download and install R and RStudio
CRAN Task View: Econometrics, Optimization
Roger D. Peng, R Programming for Data Sciences, Learnpub.com, 2015.

Course Topics

Lecture notes will be updated and available online for download during class in progress.

Econometric Computing with R

Matrix Algebra in R
R Programming

Nonlinear Optimization

Unconstrained Optimization
Constrained Optimization

Classical Nonlinear Regression Models

Nonlinear Least Squares (NLSQ)
Maximum Log-Likelihood (ML)
M-Estimation
Statistical Inferences in Nonlinear Models
Applications

Generalized Linear Models

Binary Choice Models: Probit, Logit
Count Data and Posisson Model

Generalized Method of Moments

Nonlinear Generalized Method of Moments (GMM)
GMM Estimation for Econometric Models
Application: A Nonlinear Rational Expectation Model

Course Expectation

This course consists of lectures, readings, homework, presentation, and final exam. Each student is expected to present the assigned readings on a specific topic. In addition, there are 4-5 homeworks (once every two weeks in average). Doing homework is very important not only to understand the theoretical concepts but also to learn structural and efficient computing techniques for econometric estimation and inference. The time schedule and grade distribution are as follows:

Homework (30%): Due every 2 weeks
Presentation(30%): Topics are based on the chapters of the following readings
- J. Angrist and J.-S. Pischke, Mastering 'Metrics: The Path from Cause to Effect, Princeton: Princeton University Press, 2014.
- J. Angrist and J.-S. Pischke, Mostly Harmless Econometrics: An Empiricist's Companion, Princeton: Princeton University Press, 2009.
- H. R. Varian, Causal inference in economics and marketing, PNAS (July 5, 2016), vol. 113 no. 27, 7310-7315.
1. Regression: A Review
2. Instrumental Variables
3. Regression Discontinuity
4. Difference in Difference
5. Quantile Regression
6. Nonstandarded Standard Error Issues
Final Exam (40%)