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課程名稱 |
計量經濟理論一B
Econometric Theory (Ⅰ) B |
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開課學期 |
111-1 |
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授課對象 |
社會科學院 經濟學研究所 |
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授課教師 |
郭漢豪 |
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課號 |
ECON8819 |
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課程識別碼 |
323EM0920 |
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班次 |
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學分 |
2.0 |
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全/半年 |
半年 |
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必/選修 |
必修 |
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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) |
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上課地點 |
社科303社科303 |
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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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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
This is a self-contained and serious course in econometric theory at the master and doctoral levels. This course is about necessary fundamental knowledge in econometrics: asymptotics, unbiased and consistent estimations, constrained (restricted) estimations, and hypothesis testing.
The first topic is asymptotics, which is about the probabilistic properties of random (stochastic) sequences when the sample sizes are very large (or diverge to infinity). This part covers random processes, laws of large numbers, and central limit theorems.
The second topic is unbiased and consistent estimations. This part covers least squares, maximum likelihood, generalized method of moments, minimum distance estimation. The third and fourth topics, constrained estimations and hypothesis testing, are based on the knowledge in this part.
Lastly, we may discuss some interesting important topics, such as shrinkage estimations, model selection, and Bayesian estimations. |
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課程目標 |
This course aims at developing students’ knowledge in theoretical and applied econometrics. After the training in this course, hard-working students will be well-prepared for master or doctoral programs at top universities in Asian and western countries, and will have the ability to conduct basic research. |
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課程要求 |
No econometrics knowledge is assumed. Each topic will be developed at the beginner level so that the course is self-contained. 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 non-competitive) 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. |
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預期每週課後學習時數 |
Students are expected to review and study the theories developed in 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, make-up 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. |
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Office Hours |
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指定閱讀 |
The course is mainly based on Hansen's "Econometrics". |
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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. Micro-econometrics: 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 Information-Theoretic 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. |
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評量方式
(僅供參考) |
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