Advanced Econometrics II

Course Syllabus (Chinese Version)

Lectures at Xiamen University, China
March 24 - April 25, 2008
http://www.econ.pdx.edu/faculty/KPL/XMU/
Prof. Kuan-Pin Lin
Portland State University
Portland, Oregon 97207, USA

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. After a quick review of basic econometric computing with GAUSS, we cover topics such as non-linear regression models, time series models, and qualitative choice models. The purpose of this course is to prepare students with broad knowledge of econometric methods and applications capable of doing independent research project. In addition to economic theory, knowledge of 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, 5th ed., Prentice Hall, 2002.
• K.-P. Lin, Computational Econometrics: GAUSS Programming for Econometricians and Financial Analysts, ETEXT Publishing, 2001; Chinese Edition, Tsinghua University Press, 2003.
• F. Hayashi, Econometrics, Princeton University Press, 2000.
• J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, The MIT Press, 2002.

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
• Panel Data Analysis
  • Fixed Effects and Random Effects
  • Stochastic Frontier Analysis for Panel Data
  • Autocorrelation in Panel Data
  • Dynamic Panel Data Analysis
• Qualitative Choice Models (Time Permitting)
  • Binary and Multinomial Choice Models: Probit, Logit
  • Limited Dependent Variable Model: Tobit
  • Count Data and Posisson Model
  • Duration Data and Hazard Function

Case Studies

• Chinese Agricultural Production Function
• Chinese Money Demand Equation

Course Expectation

This course consists of lectures, readings, homework, and final exam. Doing homework using GAUSS is important for not only to understand the theoretical concepts but also to learn structural and efficient computing techniques for econometric estimation and inference. Homework will be announced in class. Final exam is scheduled at the end of the course.

Homework

• Homework 1
• Homework 2
• Homework 3
• Homework 4
• Homework 5

If you decide you don't have to get A's,
you can learn an enormous amount in college.

-- I.I. Rabi, Nobel Laureate 1944,
Physics, Columbia University