EC 595 Applied Advanced Econometrics

Course Syllabus

Fall 2006, TTH 6:40-8:30pm (CH-296)
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 including non-linear regression models and financial econometrics. The purpose of this course is to prepare students in doing independent research project as part of graduate curriculum (i.e. EC 596/597). In addition to economic theory, knowledge of calculus and linear algebra is required. Experience of computer programming is helpful but not necessary. GAUSS and GPE2 econometric package will be used throughout the course.

Texts

• 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.
• Readings on Financial Econometrics
  1. R. F. Engle, "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica 50, 1982, 987-1006 (Paper).
  2. R. F. Engle, "Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models," Journal of Business and Economic Statistics 20, 2002, 339-350.
  3. T. Bollerslev, "Generalized Autoregressive Conditional Heteroskedasticity," Journal of Econometrics 31, 1986, 307-327.
  4. T. Bollerslev, R. Y. Chou and K. F. Kroner, "ARCH Modeling in Finance," Journal of Econometrics 52, 1992, 5-59.
  5. T. Bollerslev, R. F. Engle and J. M. Wooldridge, "A Capital Asset Pricing Model with Time Varying Covariances," Journal of Political Economy 96, 1988, 116-131 (Paper).
  6. L. Bauwens, S. Laurent and J. V. K. Rombouts, "Multivariate GARCH Models: A Survey," Journal of Applied Econometrics 21, 2006, 79-110 (Paper).
  7. Y. K. Tse and A. K. C. Tsui, "A Multivariate GARCH Model with Time-Varying Correlations," Journal of Business and Economic Statistics 20, 2002, 351-362.

Software and Manual

• GAUSS 7.0 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 upgrade of GPE2 for GAUSS 7.0 is 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 Hetroscedastic Regression Model Autocorrelated Regression Model Nonparametric Econometric Model
Generalized Method of Moments Nonlinear Generalized Method of Moments (GMM) GMM Estimation for Econometric Models Application: A Nonlinear Rational Expectation Model	Financial Econometrics Univariate ARMA/GARCH Models Multivariate ARMA/GARCH Models

Course Expectation

This course consists of lectures, readings, homeworks, presentations, 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 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	Due every 2 weeks	(30%)
Presentation	TBA	(30%)
Final	December 5 (Tuesday), 7:30pm	(40%)