課程名稱 
計量經濟理論一B Econometric Theory (Ⅰ) B 
開課學期 
1121 
授課對象 
社會科學院 經濟學研究所 
授課教師 
郭漢豪 
課號 
ECON8819 
課程識別碼 
323EM0920 
班次 

學分 
2.0 
全/半年 
半年 
必/選修 
必修 
上課時間 
第9,10,11,12,13,14,15,16 週 星期一9,10(16:30~18:20)星期四2,3,4(9:10~12:10) 
上課地點 
社科303社科303 
備註 
本課程以英語授課。密集課程。碩博一必修。一910為實習課。密集授課第9~16週。先修科目：數量方法入門。 限碩士班以上 或 限博士班 總人數上限：60人 


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課程概述 
This is a selfcontained and rigorous course in econometrics at the master and doctoral levels. This course is about fundamental knowledge in econometrics: asymptotics, unbiased and consistent estimations, constrained (restricted) estimations, and hypothesis testing.
This course does not only develop the theoies, but also provides serious disscussions on them. For example, we will discuss the interpretations of least squares (LS), maximum likelihood (ML), and generalized method of moments (GMM). We will discuss the meanings of consistency and identification. The theoretical details and the corresponding discussions are essential for applying econometrics.
The first part is asymptotics (large sample theory), which is about the probabilistic properties of random (stochastic) sequences when the sample sizes are very large (or diverge to infinity). We will start with the basic concepts of random processes and convergences, and then discuss two extremely important sets of theorems: laws of large numbers and central limit theorems.
The second part is unbiased and consistent estimation methods. The standard consistent methods in econometrics are LS, ML, GMM, and minimum distance. We will see that there is a unified theory of their consistency and asymptotic normality.
The third and fourth parts are respectively constrained estimations and hypothesis testing. These parts are based on the knowledge in the first two part.
We may discuss some interesting and important topics, such as shrinkage estimations, model selection, and Bayesian estimations. 
課程目標 
This course aims at developing students’ knowledge in theoretical and applied econometrics. After the training in this course, hardworking students will be wellprepared for master or doctoral programs at top universities in Asian and western countries, and will have the ability to conduct basic research. 
課程要求 
No econometrics knowledge is assumed. Each topic will be developed at the beginner level so that the course is selfcontained. But a certain level of mathematical maturity is expected (see Wikipedia for interesting definitions of mathematical maturity).
Precisely, the prerequisites are introductory knowledge in microeconomics, calculus, linear algebra, probability, and statistics. Essentially, students are expected to know what are (competitive and noncompetitive) market, demand, supply, differentiation, integration, optimization (unconstrained and constrained), Lagrange multiplier, matrix, vector, probability, distribution, density, expectation, mean, variance, and covariance.
This course is suitable for those who are interested in econometrics and statistics for social sciences. Students who have no training in econometrics but have solid background in mathematics and statistics are welcome. 
預期每週課後學習時數 
In almost all academic disciplines, knowledge is cumlative. In each week, students are expected to study the theories taught in class, so that they can follow the discussion in subsequent classes.
The examinations essentially test students’ understanding of the theories taught in classes. Performance evaluations are based on homeworks and examinations.
Late submission of homeworks will not be accepted. In principle, makeup examinations will not be given. However, if there are exceptional circumstances so that you cannot take the examinations at the scheduled time, you should contact us before the examinations. 
Office Hours 

指定閱讀 
In the classes, it will be clear that the teaching materials are from which book chapters or papers. 
參考書目 
Econometrics
1. Hayashi, F. 2000. Econometrics. Princeton University Press, Princeton.
2. Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge.
3. Wooldridge, J.M., 2010. Econometric Analysis of Cross Section and Panel Data, 2nd ed. The MIT Press, Cambridge.
4. Lee, M.J., 2010. Microeconometrics: Methods of Moments and Limited Dependent Variables, 2nd ed. Springer, New York.
5. Hansen, B.E., 2022. Econometrics. Princeton University Press, Princeton.
6. Hansen, B.E., 2022. Probability and Statistics for Economists. Princeton University Press, Princeton.
Advanced Econometrics
1. Eatwell, J., Milgate, M., Newman, P. (Eds.), 1990. The New Palgrave: Econometrics. The Macmillan Press Limited, London.
2. Durlauf, S.N., Blume, L.E. (Eds.), 2010. Microeconometrics. Palgrave Macmillan, Basingstoke.
3. Durlauf, S.N., Blume, L.E. (Eds.), 2010. Macroeconometrics and Time Series Analysis. Palgrave Macmillan, Basingstoke.
4. Hassani, H., Mills, T.C., Patterson, K. (Eds.), 2006. Palgrave Handbook of Econometrics, Volume 1: Econometric Theory. Palgrave Macmillan, New York.
5. Mills, T.C., Patterson, K. (Eds.), 2009. Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics. Palgrave Macmillan, New York.
Statistics
1. Konishi, S., 2014. Introduction to Multivariate Analysis: Linear and Nonlinear Modeling. CRC Press, Boca Raton.
2. Bickel, P.J., Doksum, K.A., 2015. Mathematical Statistics: Basic Ideas and Selected Topics, Volume 1. CRC Press, Boca Raton.
3. Bickel, P.J., Doksum, K.A., 2016. Mathematical Statistics: Basic Ideas and Selected Topics, Volume 2. CRC Press, Boca Raton.
Advanced Statistics
1. Efron, B., Hastie, T., 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press, Cambridge.
2. Wasserman, L., 2004. All of Statistics: A Concise Course in Statistical Inference. Springer, New York.
3. Wasserman, L., 2010. All of Nonparametric Statistics. Springer, New York.
4. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical InformationTheoretic Approach, 2nd. Springer, New York.
5. Claeskens, G., Hjort, N.L., 2008. Model Selection and Model Averaging. Cambridge University Press, Cambridge.
6. Konishi, S., Kitagawa, G., 2008. Information Criteria and Statistical Modeling. Springer, New York. 
評量方式 (僅供參考) 

週次 
日期 
單元主題 
Week 1 

Introduction to probability, statistics, and econometrics
Random (stochastic) variables and processes
Limits and convergences 
Week 2 

Limits and convergences
Laws of large numbers
Central limit theorems 
Week 3 

Unbiased and consistent estimators
Least squares 
Week 4 

Unbiased and consistent estimators
Generalized methods of moments 
Week 5 

Unbiased and consistent estimators
Maximum likelihood 
Week 6 

Constrained and restricted estimation 
Week 7 

Hypothesis testing
Model selection 
Week 8 

Special topics
Revision 
