National Tsing Hua University

Instructor: Shih-Chang Lin

Contact Information: Office: HSS-A407, Email: slin@mx.nthu.edu.tw,

Phone: 574-2729

Office Hours: By appointment

Course Webpage: Blackboard System – http://elearn.nthu.edu.tw/bin/index.pl

Course Description:

This course is designed for first-year Economics graduate students. The basic methods,modern econometric methods and theory are covered. The intention is that the material will provide a foundation for applied research in economics. It will provide an initial opportunity for you to develop the skills to conduct and understand empirical work. The coursework will be theoretical in nature, but students will also be required to use the methods to estimate

certain models and test certain hypotheses.

Prerequisites:

Basic calculus, familiarity with probability, statistics, and matrix algebra will be assumed.

Syllabus

Textbook:

Basically, I will hand out weekly lecture notes which are the summary of literature and

textbooks.

• My Lecture Notes.

• [JD] Johnston and DiNardo, 1997, Econometric Methods, 4th edition, McGraw Hill.

• [G] Greene, W. H., 2003, Econometric Analysis, 5th edition, Prentice Hall.

References:

Note that you don’t need to buy the following references. If you are not comfortable with

basic concepts of econometrics, it would be helpful to consult to above textbooks and the

following references anytime.

• [W] Wooldridge, J. M., 2000, Introductory Econometrics: A modern approach, South-

Western.

• [K] Kennedy, P. A., 1998, A guide to Econometrics, 4th edition, MIT Press.

• [DS] DeGroot M. H. and M. J. Schervish, 2002, Probability and Statistics, 3rd edition,

Addison Wesley.

• [CB] Casella, G. and R. L. Berger, 2001, Statistical Inference, 2nd edition, Duxbury.

• [R] Ruud, P. A., 2000, An Introduction to Classical Econometric Theory, Oxford.

• [DM] Davidson, R. and J. D. MacKinnon, 2004, Econometric Theory and Methods,

Oxford.

Software: You are welcome to use any econometric or statistic softwares such as Matlab,TSP, Gauss, Stata, Eviews, or Limdep.

Grading: There will be weekly assignments1, two midterm exams and a final. Note that overdue assignment will NOT be accepted. They will count toward the final grade as follows.2

Assignments 15%

Midterm I 25%

Midterm II 25%

Final 35%.

1.Class assignments will be passed out approximately every week. These assignments will include both

problem solving and computer tasks.

2.Bonus will be awarded for the typo corrections.

– 2 –

Syllabus

Course Organization: 17 weeks / lectures [Tentative!!]

1. : Organization

2. : Statistical review and Large sample theory

3. : Multiple regression model [I]

4. : Multiple regression model [II]

5. : Multiple regression model [III]

6. : Midterm Exam I [In class]

7.: Generalized least square

8. : Instrumental variable

9. : Autocorrelation

10. : Heteroskedasticity

11. : Midterm Exam II [In class]

12. : Numerical Optimization

13. : Discrete choice model

14. : Out of town for seminar – No class meeting

15. : Sample selection model

16. : Panel data

17. : Final Exam