Course title 
Causal Inference and Prediction in Econometrics 
Semester 
1111 
Designated for 
COLLEGE OF SOCIAL SCIENCES DEPARTMENT OF ECONOMICS 
Instructor 
HON HO KWOK 
Curriculum Number 
ECON5179 
Curriculum Identity Number 
323EU4300 
Class 

Credits 
2.0 
Full/Half Yr. 
Half 
Required/ Elective 
Elective 
Time 
Tuesday 6,7(13:20~15:10) 
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. 


Course introduction video 

Table of Core Capabilities and Curriculum Planning 
Table of Core Capabilities and Curriculum Planning 
Course Syllabus

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Course Description 
This course is about some fundamental and important ideas in econometrics.
First, the course starts with a review of the basic ideas and history of econometrics.
Second, we discuss the meanings of identification in econometrics. We start with the classical example of identifying simultaneous equations (demand and supply curves). The various identification meanings are closely related to the estimation strategies. We discuss two important types of estimation methods: momentbased and extremumbased methods.
Third, we discuss the endogeneity problems in econometrics, which are common reasons for the failure of identification.
Fourth, we discuss the ideas and differences in the meanings of causality and prediction.
Finally, we discuss frequentist, Bayesian, and Fisherian inferences. This part emphasizes the connection between econometrics and statistics. 
Course Objective 
This course is about advanced undergraduate to introductory postgraduate 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. 
Course Requirement 
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. 
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, 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 

Designated reading 
In the classes, it will be clear that the materials are based on which books' chapters and papers. 
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. 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.
4. Wasserman, L., 2004. All of Statistics: A Concise Course in Statistical Inference. Springer, New York.
5. Wasserman, L., 2010. All of Nonparametric Statistics. Springer, New York.
6. Efron, B., Hastie, T., 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press, Cambridge.
Model Selection and Model Averaging
1. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical InformationTheoretic Approach, 2nd. 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.
Treatment Effects
1. Lee, M.J., 2005. MicroEconometrics 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. 
Grading 
No. 
Item 
% 
Explanations for the conditions 
1. 
Homeworks 
20% 

2. 
Examinations 
80% 


