This is a
course in introductory econometrics. This means that it will cover statistical
topics that are of interest in econometrics and probably other related fields.
Basically it will cover linear regression analysis of single-equation and
multiple-equation models. The course in intended as an introduction to
econometric models, and to be a useful prerequisite to further study in
advanced (theoretical) econometrics, especially that is of interest to
(empirical) study in finance.
The
prerequisites for the course are a basic knowledge of statistics and a little
bit of matrix algebra. Students should have had at least one semester course in
statistics. There is no requirement for linear algebra, but some familiarity
with matrices is necessary.
There will be
a midterm on Wednesday, November 24, and a final exam on Wednesday, January 12. There will also be some problem sets to
do during the semester,
and hopefully some that require computing with certain econometric software
(e.g., Gauss, SAS, TSP, EViews); being familiar with some
econometric
software(s) is not required, but will prove to be very helpful when you are in
the stage of writing a thesis.
- There is no required textbook
for this course. But I will recommend two:
- Greene, Econometric Analysis,
5th ed., Prentice Hall, 2003. (a newer and
comprehensive book).
- Johnston and DiNardo, 1997,
Econometric Methods, 4th Edition, New
York: McGraw-Hill.
The following
books will also be referred from time to time.
- (Chinese Reference Book) 鍾惠民、吳壽山、周賓凰、范懷文，2002，財金計量，雙葉出版社。
- Kennedy, A
Guide to Econometrics, 3rd ed., MIT Press, 1992. (simple
and yet containing good exposition of econometric concepts; good for
beginners).
- Judge, et al, The Theory and
Practice of Econometrics, 2nd ed., Wiley, 1985. (a
good reference, yet a little out-dated).