Applied Advanced Econometrics

Course Syllabus

Fall 2013, 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

This course covers advanced topics related to methodological issues in econometrics, with emphases on computation intensive methods and applications. Because most of econometric computation is build upon the matrix programming, MATA in Stata will be introduced and used throughout the course.

This course 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.

Texts

• W. H. Greene, Econometric Analysis, 7th ed., Prentice Hall, 2011.
• J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, 2nd ed., The MIP Press, 2010.
• A. Colin Cameron and Pravin K. Trivedi, Microecometrics: Methods and Applications, Cambridge University Press, 2005 (Recommended).

Software

• Stata 12 or 13, StataCorp, 2011.
  Stata is available in the Economics Lab (CH-230). A version of Small Stata may be used for the class. You can order it through Stata Course GradPlan here (Contact your instructor for the required GradPlan ID).
• C. F. Braum, An Introduction to Modern Econometrics Using Stata, Stata Press, 2006.
• C. F. Braum, An Introduction to Stata Programming, Stata Press, 2009.
• A. Colin Cameron and Pravin K. Trivedi, Microecometrics Using Stata, Revised Edition, Stata Press, 2010.

Course Topics

Nonlinear Econometrics

Spatial Econometrics

Econometric Computing with MATA in Stata Introduction to MATA MATA 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 Nonlinear Econometric Models Generalized Linear Models Box-Cox Variable Transformation Hetroscedastic Regression Models Autocorrelated Regression Models Generalized Method of Moments Nonlinear Generalized Method of Moments (GMM) GMM Estimation for Econometric Models Application: A Nonlinear Rational Expectation Model Qualitative Choice Models Binary and Multinomial Choice Models: Probit, Logit Limited Dependent Variable Model: Tobit Count Data and Posisson Model Duration Data and Hazard Function

Introduction to Spatial Econometric Analysis Nonparametric Treatment of Spatial Dependence Parametric Representation of Spatial Dependence Spatial Linear Econometric Models Spatial Heterogenous Models Spatial Autoregressive Models Spatial Error Component Models Hypothesis Testing of Spatial Dependence Test for Spatial Heterogeneity Test for Spatial Autocorrelation Spatial Econometric Model Estimation Least Squares: OLS, IV/2SLS Generalized Method of Moments: GMM Quasi-Maximum Likelihood: QML Using SPPACK: Stata module for cross-section spatial-autoregressive models (D. Drukker, H. Peng, I. Prucha, R. Raciborski) Lecture Notes: 1, 2, 3, 4, 5

Course Expectation

This course consists of lectures, readings, homework, presentation (or project), and final exam. Each student is expected to present the assigned readings on a specific topic. The presentation may be subsituted by an approved course project. 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
  1. Homework 1
  2. Homework 2
  3. Homework 3
  4. Homework 4
• Presentation or Project (30%)
• Final Exam (40%): December 10