Applied Advanced Econometrics

Course Syllabus

Fall 2008, TTH 6:40-8:30pm (NH-366)
Prof. K.-P. Lin (CH-241G, 725-3931)
Office Hours: TTH 4:00-4:30pm or by Appointment

This course covers advanced topics related to methodological issues in econometrics, with emphases on computation intensive methods and applications. This course assumes background knowledge of linear econometric models. The purpose of this course is to prepare students with 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. GAUSS and GPE2 econometric package will be used throughout the course.

Texts and References

• W. H. Greene, Econometric Analysis, 6th ed., Prentice Hall, 2008.
• K.-P. Lin, Computational Econometrics: GAUSS Programming for Econometricians and Financial Analysts, ETEXT Publishing, 2001.

Software and Manual

• GAUSS 8.0 for Windows Student Version, Aptech Systems, 2006. Academic and professional versions of GAUSS for Windows are also available from Aptech.
• GPE2 (GAUSS Programming for Econometricians and Financial Analysts) for GAUSS, included in Computational Econometrics. Free download of GPE2 for GAUSS 8.0 and GAUSS Light are available here for registered students only.

Course Topics

• Econometric Computing with GAUSS
  • Introduction to GAUSS
  • Using GPE2 for GAUSS
• Nonlinear Optimization
  • Unconstrained Optimization
  • Constrained Optimization
• Nonlinear Regression Models
  • Nonlinear Least Squares (NLSQ)
  • Maximum Log-Likelihood (ML)
  • Statistical Inferences in Nonlinear Models
• Nonlinear Econometric Models
  • Box-Cox Variable Transformation
  • Stochastic Frontier Production Function
  • Hetroscedastic Regression Models
  • Autocorrelated Regression Models
  • Regime Switching Regression Models
• Generalized Method of Moments
  • Nonlinear Generalized Method of Moments (GMM)
  • GMM Estimation for Econometric Models
  • Application: A Nonlinear Rational Expectation Model
• Advanced Topics for Presentation: TBA
  • Nonparametric Model Estimation
  • Spatial Econometric Analysis
  • Monte Carlo Simulation and Bootstrapping Methods

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 homeworks using GAUSS 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, TBA
  1. Homework 1
  2. Homework 2
  3. Homework 3
  4. Homework 4
  5. Homework 5
• Presentation or Project (30%): TBA
• Final Exam (40%): Take Home, December 5-8 (Test, Data)