Econometrics II: Course Syllabus

Spring 2002, Prof. K.-P. Lin (N-216, Tel: 6278-9797)
Class Hours: T/TH 2:00-5:00pm (N-401)
Office Hours: W 2:00-4:00pm or by Appointment
E-Mail: kuan-pin@em.tsinghua.edu.cn

This 48-hours course covers topics related to methodological issues in econometrics, with emphases on computation, non-linear models, and time series analysis. 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. Course information and lecture outlines are available from the School's network.

Textbooks

• W. H. Greene, Econometric Analysis, 4th Ed., Prentice Hall, 1999.
• K.-P. Lin, Computational Econometrics: GAUSS Programming for Econometricians and Financial Analysts, ETEXT Textbook Publisher, Los Angeles, 2001. (Chinese Version)

Software and Manual

• GAUSS 4.0 for Windows Student Version, Aptech Systems, 2002. The full version of GAUSS 4.0 for Windows is also available in the Lab.
• GPE2 (GAUSS Programming for Econometricians and Financial Analysts) for GAUSS.

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
  Heteroscedastic Regression Models
  Binary Qualitative Choice Models
  Switching Regression Models
• Generalized Method of Moments
  Nonlinear Generalized Method of Moments (GMM)
  GMM Estimation for Econometric Models
  Application: A Nonlinear Rational Expectation Model
• Nonstationary Time Series
  Trend in Time Series
  Unit Roots
  Cointegration
• Time Series Analysis
  Autoregressive and Moving Average (AR, MA, ARMA, ARMAX)
  Autoregressive Conditional Heteroscedasticity (ARCH, ARCH-M, GARCH)
  State-Space Models
• Panel Data
  Fixed Effects Model
  Randon Effects Model
  Randon Coefficients Model
  Seemingly Unrelated Regression Model

Case Study

• Chinese Agricultural Production Function
• Chinese Money Demand Equation
• Consumption-Income Relationship in China

Course Expectation

This course consists of a series of lectures, readings, and hand-on computer exercises. There are two (2) tests: midterm and final. In addition, there are 4 homeworks (once every two weeks in average). Also there is a course project paper due at the end of semester. The tentative time schedule and grade distribution are as follows:

Midterm	June 6	(30%)
Final	June 30	(30%)
Project	July 31	(20%)
Homework	Due every 2 weeks	(20%)

Guideline on Writing a Course Project

• Format
  1. 10 pages typed (double-space and wide margins) with a cover page consisting of the project title and abstract.
  2. The model presented has to be an original econometric model dealing with the issues of Chinese economy. The model must be nonlinear and consistent with the topics covered in the lectures.
  3. Supporting data and computer program printout have to be included, but not counted for the page number.
• Contents
  1. Introduction and brief discussion of the main results.
  2. Technical explanation of model specification, estimation, and hypothesis testings.
  3. Detailed interpretation of the estimated model and its policy implication.
  4. References (including data sources).
• Grade
  • The project is evaluated based on its originality, creativity, and consistency with the format and content requirements described above.
• Deadlines
  • Project proposal (1 page typed): June 25 or earlier.
  • Project due: July 31 (by e-mail submission).