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Course title |
Econometric Theory (Ⅰ) B |
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Semester |
111-1 |
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Designated for |
COLLEGE OF SOCIAL SCIENCES GRADUATE INSTITUTE OF ECONOMICS |
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Instructor |
HON HO KWOK |
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Curriculum Number |
ECON8819 |
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Curriculum Identity Number |
323EM0920 |
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Class |
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Credits |
2.0 |
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Full/Half
Yr. |
Half |
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Required/
Elective |
Required |
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Time |
第9,10,11,12,13,14,15,16 週
Monday 9,10(16:30~18:20) Thursday 2,3,4(9:10~12:10) |
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Remarks |
Restriction: MA students and beyond OR Restriction: Ph. D students
The upper limit of the number of students: 60. |
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Course introduction video |
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Table of Core Capabilities and Curriculum Planning |
Table of Core Capabilities and Curriculum Planning |
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Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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Course Description |
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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Course Objective |
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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Course Requirement |
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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Student Workload (expected study time outside of class per week) |
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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Designated reading |
The course is mainly based on Hansen's "Econometrics". |
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References |
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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Grading |
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No. |
Item |
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Explanations for the conditions |
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1. |
Homework |
20% |
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2. |
Examination |
80% |
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