授課教師：
孫立群
電
話：
(02) 23630231 轉 2523
傳
真：
(02) 23628496
辦 公 室：
農業綜合館 212 室
電子信箱：
sunlc@ccms.ntu.edu.tw
<sunlc@ccms.ntu.edu.tw>
約談時間：
計 量 與 GAUSS 程 式 設計
學
Econometrics and GAUSS Programming
必、選修
碩士班、博士班
課
課程名稱
授課對象
授課時間 星期二 9：10 － 12：00 AM
課程目的
先修課程 無
分
號
授課地點
3 學 分
半 年 選 修 課
627 M0230
農 經 五
This course is designed specifically for graduate students, and it may be
given another titles: "Practicing Econometrics Using Computers" or in a more
fashionable way "Econometrics DIY," which means you will be struggling
through the entire course in front of a personal computer. The software we use
is the personal computer programming language GAUSS. Although no
prerequisite knowledge on GAUSS is needed, basic training on econometrics
and matrix algebra are required.
Working out the assigned exercises is the No. 1 job in this course. There will be no
midterm exam for this course. Course grade will be based on homeworks, class
participation and maybe a final exam.
Matrix algebra and econometric theory will be briefly reviewed in the class. The
level will be consistent with the graduate econometrics course. The lecture is
mainly to refresh your memory of your previous econometric classes and to place
econometric theory in the framework of various economic applications. The basic
econometrics will be studied very carefully. This is necessary because when you
work on the exercises, you will have to "translate" the algebra and derivation
underlying each econometric theory to a GAUSS program.
授 課
One main objective of this course is to force you to study the basic econometrics
carefully. Any small error caused by carelessness may ruin your entire computer
program and leave you nothing for hours of work. So, please be prepared to get
frustrated and to spend hours and hours of your precious time in front of a PC. To a
certain point, you may feel that you want to throw your PC out of the window. It's
perfectly fine with me, as long as the PC is yours.
The following topics will be covered in this course:
大 綱
1.Matrix Algebra; 2.Introduction to GAUSS Programming; 3.OLS, GLS and
Seemingly Unrelated Regressions; 4.Panel Data Models; 5.Maximum Likelihood
Method in Econometrics; 6.LR, LM and Wald Tests; 7.Limited Dependent and
Qualitative Variables Models; 8.Self-Selection Models; 9.Economic Duration
Models; 10.Basic Time Series Models.
The other objective of this course is to give you an opportunity to work with real
data. Only getting your hands dirty with real data can give you the "feeling" about
data that cannot be acquired from reading books or papers. Hopefully, you will
have chances to encounter all the possible problems in dealing with data and get
over with them once and for all.
Moreover, going through applied econometric projects (data collection, data
transformation, data analysis, econometric model building, estimation, testing,
troubleshooting, evaluation, and model revision) as you work out exercises can
provide you with some prototypes for your future research.
When you just start working with GAUSS, be prepared for the anxiety, frustration,
and even anger in learning GAUSS. You should have access to an IBM-compatible
personal computer, preferably 386 or higher, with a math-coprocessor. Some
knowledge about the DOS operating system for IBM-compatible personal
computers is required. If you have not used PC's before, you may experience
desperate feeling in the first weeks and you just have to spend more time on PC.
教科書
Greene, William H. Econometric Analysis, 2nd ed., Macmillan
Publishing Company.
1. Matrix Algebra
陳超塵 (民國 70 年). 統計學原理, 四版. 第八章: 數陣代數大意.
Graybill, Franklin A. (1983). Matrices with Applications in Statistics, Belmont,
CA: Wadsworth International Group.
Johnston, J. (1984). Econometric Methods, 3rd ed., New York: McGraw-Hill, Inc.
Chapter 4: Elements of Matrix Algebra.
2. Introduction to GAUSS Programming
3. OLS, GLS and Seemingly Unrelated Regressions
Fomby, Thomas B., Hill , R. Carter and Stanley R. Johnson (1984). Advanced
Econometric Methods, New York: Springer-Verlag. Chapter 8: Feasible
Generalized Least Squares Estimation.
4. Panel Data Model (Option)
Hsiao, Cheng (1986). Analysis of Panel Data, New York: Cambridge University
Press.
Maddala, G. S. (1971). "The Use of Variance Components Models in Pooling Cross
Section and Time Series Data," Econometrica, 39(2):341 - 358.
Mundlak, Yair (1978). "On the Pooling of Time Series and Cross Section Data,"
Econometrica, 46(1): 69 - 85.
5. Maximum Likelihood Method in Econometrics
Cramer, J. S. (1986). Econometric Applications of Maximum Likelihood Methods,
New York: Cambridge University Press.
6. LR, LM and Wald Tests
Godfrey, L. G. (1990). Misspecification Tests in Econometrics, New York:
Cambridge University Press.
7. Limited Dependent and Qualitative Variables Models
Amemiya, Takeshi (1981). "Qualitative Response Models: A Survey," Journal of
參考文獻 Economic Literature, XIX(December): 1483 - 1536.
Maddala, G. S. (1983). Limited-Dependent and Qualitative Variables in
Econometrics, New York: Cambridge University Press.
Maddala, G. S. (1992). Introduction to Econometrics, 2nd ed., New York:
Macmillan Publishing Company. Chapter 8: Dummy Variables and Truncated
Variables.
8. Self-Selection Models
Bloom, D. E., and M. R. Killingsworth (1985). "Correcting for Truncation Bias
Caused by A Latent Truncation Variable," Journal of Econometrics, 27(1): 131
-135.
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