Course Information
Course title
Causal Inference and Prediction in Econometrics 
Designated for
Curriculum Number
Curriculum Identity Number
Tuesday 6,7(13:20~15:10) 
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: moment-based and extremum-based 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, 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 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, 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. 
Office Hours
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.

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 Information-Theoretic 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. 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. 
Designated reading
In the classes, it will be clear that the materials are based on which books' chapters and papers. 
Explanations for the conditions
No data