Fu Jen Catholic University Huei-Yu Chiu (n=Ø)
Graduate School of Economics Office hours: 10:00-12:30, Monday
Fall 2008 10:00-12:30, Friday
Class hours: 13:40-16:30, Monday Office tel: 2905-2706
Email: 070665@mail.fju.edu.tw
Econometrics I (l¾Ü I)
Econometrics is a tool which uses the statistical techniques to analyze the real data. With this knowledge, we not only develop the ability to interpret the econo- metric models but also make inference and prediction from the information. This course is a one-year graduate course. We attempt to introduce the basic knowledge of the econometric theory in depth in this semester. For beginning, it is our honor to invite Dr. Yi-Ting Chen giving 5-week lectures (Please refer to Dr. Yi-Ting Chen’s syllabus.). We will first introduce some probability theory, including the modes of convergence, law of large numbers and central limit theorem. Then multiple linear regression model will be discussed. Specification error problems and some estimation methods, MLE, GLS and GMM, are also covered. Models with heteroscedasticity and serial correlation will be explored as well. In the end, we will focus on nonlinear regression model. Besides, how to use econometric software, Gauss, to do simulation will be also introduced in this class.
Reading
1. Kuan, C.-M. (2” ²=) (2004), Introduction to Econometric Theory, Lec- ture notes, download from http://www.sinica.edu.tw/as/ssrc/ckuan.
2. Chung, C. F. (%ý ²=)(2004), l¾%Èçƒ2, download from http://www.sinica.edu.tw/econ/faculty/researcher/chingfan.htm.
3. Greene, W. H. (2002), Econometric Analysis, Prentice Hall.
4. Johnston, J. and J. Dinardo (1996), Econometric Methods, McGraw-Hill/Irwin.
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5. Hayashi, Fumio (2000), Econometrics, Princeton University.
6. Hansen, Bruce (2008), Econometrics, download from http://www.ssc.wisc.edu/ bhansen/econometrics/
Course Outline
Week 1-5: Dr. Yi-Ting Chen’s lectures
Week 6 (9/15): Modes of convergence, law of large numbers & central limit thoery Week 7 (9/22): Multiple linear regression model: estimation & hypothesis testing Week 8 (9/29): Multicollinearity & specification error problems
Week 9 (10/06): Midterm exam
Week 10 (10/13): Maximum likelihood estimation I Week 11 (10/20): Maximum likelihood estimation II Week 12 (10/27): Generalized least squares estimation
Week 13 (11/03): Generalized method of moments estimation I Week 14 (11/10): Generalized method of moments estimation II Week 15 (11/17): Heteroscedasticity
Week 16 (11/24): Autocorrelation
Week 17 (12/01): Nonlinear regression model Week 18 (12/08): Final exam
Grading
1. Homework (40%) 2. Midterm exam (30%) 3. Final exam (30%)
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