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Course title |
Causal Inference and Prediction in Econometrics |
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Semester |
113-1 |
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Designated for |
COLLEGE OF SOCIAL SCIENCES DEPARTMENT OF ECONOMICS |
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Instructor |
HON HO KWOK |
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Curriculum Number |
ECON5179 |
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Curriculum Identity Number |
323EU4300 |
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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 |
Elective |
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Time |
Tuesday 6,7(13:20~15:10) |
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Remarks |
Restriction: juniors and beyond OR Restriction: MA students and beyond OR Restriction: Ph. D students
The upper limit of the number of students: 50. |
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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 a self-contained course on statistics and econometrics at the intermediate undergraduate to introductory postgraduate levels. In the classes, we will develop and discuss a set of statistical and econometric methods for inferential and predictive purposes.
First, we will start with a concise review of the basic ideas and history of statistics and econometrics. Then we will talk about some basic and necessary concepts in probability and statistics, such as random (stochastic) variables and processes, probability mass, density, and distribution functions, expectation and moments, and limits and convergences.
Second, we consider the concepts of identification and consistency in econometrics. We start with the classical example of identifying simultaneous equations (demand and supply curves). We talk with the meanings of identification and their corresponding estimation strategies. We discuss two important types of estimation methods: moment-based and extremum-based methods.
Third, we consider the endogeneity problems in econometrics, which is one of the important reasons for identification failure. We talk about generalized methods of moments, instrumental variables, and control functions.
Fourth, we discuss the meanings of causality and prediction.
Fifth, we discuss frequentist, Bayesian, and Fisherian inferences. We will talk about the connection between statistics, econometrics, and economics. |
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Course Objective |
This course is about intermediate undergraduate to introductory postgraduate statistics and 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 economics and econometrics but have solid background in mathematics and statistics are welcome. |
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Student Workload (Expected weekly study hours before and/or after class) |
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 |
In the classes, it will be clear that the teaching materials are from which book chapters or papers. |
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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.
Econometrics: Panel Data
1. Matyas, L., Sevestre, P. (Eds.), 2008. The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice, 3rd ed. Springer.
2. Hsiao, C., 2014. Analysis of Panel Data. 3rd ed. Cambridge University Press, New York.
3. Baltagi, B.H. (Ed.), 2015. The Oxford Handbook of Panel Data. Oxford University Press, Oxford.
Econometrics: Treatment Effects
1. Lee, M.J., 2005. Micro-Econometrics for Policy, Program, and Treatment Effects. Oxford University Press, New York.
2. Lee, M.J., 2016. Matching, Regression Discontinuity, Difference in Differences, and Beyond. Oxford University Press, New York.
Economics and Econometrics: Social Interactions and Networks
1. Jackson, M.O., 2008. Social and Economic Networks. Princeton University Press, Princeton.
2. Newman, M.E.J., 2010. Networks: An Introduction. Oxford University Press, Oxford.
3. Easley, D., Kleinberg, J., 2010. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press.
4. Bramoulle, Y., Galeotti, A., Rogers, B.W. (Eds.), 2016. The Oxford Handbook of The Economics of Networks. Oxford University Press, 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% |
According to the teaching progress, homeworks related to the topics covered in classes will be assigned irregularly. All homework will be equally weighted; for example, if there are 4 homework assignements, then each homework constitutes 5% of the final grade. |
2. |
Examination |
80% |
The midterm and final examinations are equally weighted; that is, each of them constitute 40% of the final grade. The examinations consist of questions of the topics taught in classes. They are mainly theoretical questions. |
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