Course Syllabus

The syllabuses on both this page and the NTU online course information are synchronized.

Course Information

Item Content
Course title Econometric Theory (Ⅰ) B
Semester 113-1
Designated for GRADUATE INSTITUTE OF INTERNATIONAL BUSINESS
GRADUATE INSTITUTE OF ECONOMICS
Instructor HON HO KWOK
Curriculum No. ECON 8819
Curriculum Id No. 323 M0920
Class
Credit 2
Full/Half Yr. Half
Required/Elective Required
Time Monday 9,10(16:30~18:20)Thursday 2,3,4(9:10~12:10)
Place 社科303
Remarks The course is conducted in English。Intensive courses。

 

Course Syllabus

Item Content
Course Description This is a self-contained 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.
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.
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 economics and econometrics but have solid background in mathematics and statistics are welcome.
Student Workload (expected study time outside of class per week) 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, 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.
References Probability 1. Durrett, R., 2019. Probability: Theory and Examples, 5th ed. Cambridge University Press, Cambridge. 2. DasGupta, A., 2010. Fundamentals of Probability: A First Course. Springer, New York. 3. DasGupta, A., 2008. Asymptotic Theory of Statistics and Probability. Springer, New York. 4. DasGupta, A., 2011. Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics. Springer, New York. 5. Stoyanov, J.M., 2013. Counterexamples in Probability, 3rd ed. Dover Publications, Mineola. Statistics 1. Wasserman, L., 2004. All of Statistics: A Concise Course in Statistical Inference. Springer, New York. 2. Wasserman, L., 2010. All of Nonparametric Statistics. Springer, New York. 3. Konishi, S., 2014. Introduction to Multivariate Analysis: Linear and Nonlinear Modeling. CRC Press, Boca Raton. 4. Bickel, P.J., Doksum, K.A., 2015. Mathematical Statistics: Basic Ideas and Selected Topics, Volume 1. CRC Press, Boca Raton. 5. Bickel, P.J., Doksum, K.A., 2016. Mathematical Statistics: Basic Ideas and Selected Topics, Volume 2. CRC Press, Boca Raton. 6. Efron, B., Hastie, T., 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press, Cambridge. Statistics: Model Selection and Model Averaging 1. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. Springer, New York. 2. Claeskens, G., Hjort, N.L., 2008. Model Selection and Model Averaging. Cambridge University Press, Cambridge. 3. Konishi, S., Kitagawa, G., 2008. Information Criteria and Statistical Modeling. Springer, New York. 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. Probability and Statistics for Economists. Princeton University Press, Princeton. 6. Hansen, B.E., 2022. Econometrics. Princeton University Press, Princeton. Econometrics: Advanced Topics 1. Eatwell, J., Milgate, M., Newman, P. (Eds.), 1990. The New Palgrave: Econometrics. The Macmillan Press Limited, London. 2. Hassani, H., Mills, T.C., Patterson, K. (Eds.), 2006. Palgrave Handbook of Econometrics, Volume 1: Econometric Theory. Palgrave Macmillan, New York. 3. Mills, T.C., Patterson, K. (Eds.), 2009. Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics. Palgrave Macmillan, New York. 4. Durlauf, S.N., Blume, L.E. (Eds.), 2010. Microeconometrics. Palgrave Macmillan, Basingstoke. 5. Durlauf, S.N., Blume, L.E. (Eds.), 2010. Macroeconometrics and Time Series Analysis. Palgrave Macmillan, Basingstoke. Econometrics: Theory Bierens, H.J., 1981. Robust Methods and Asymptotic Theory in Nonlinear Econometrics. Springer, Berlin. Bierens, H.J., 1996. Topics in Advanced Econometrics: Estimation, Testing, and Specification of Cross-Section and Time Series Models. Cambridge University Press, Cambridge. Bierens, H.J., 2005. Introduction to the Mathematical and Statistical Foundations of Econometrics. Cambridge University Press, Cambridge.
Designated Reading In the classes, it will be clear that the teaching materials are from which book chapters or papers.

 

Progress

Week Date Topic
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

 

Grading

NO Item Pc Explanations for the conditions
1 Homework 20%
2 Examination 80%

 

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Office Hour

Remarks None