Course Information
Course title
Topics in Economics and Econometrics 
Designated for
Curriculum Number
Curriculum Identity Number
Wednesday 2,3,4(9:10~12:10) 
Restriction: juniors and beyond OR Restriction: MA students and beyond OR Restriction: Ph. D students
The upper limit of the number of students: 50. 
Ceiba Web Server 
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 applying economics and econometrics to study the real world. We will study important topics in the research frontiers, for example, economic and social interactions in networks, behaviors and information diffusions in economic and social networks, poverty, inequality, and intergenerational mobility.

First, the course focuses more on econometrics because it is about studying the real world. Second, this course emphasizes the integration of economics and econometrics. We will consider the economic foundations of the econometric models and methods.

This course will help students integrating their knowledge in introductory economics and econometrics to the theories in academic research papers. Students will learn a general theoretical structure of economics and econometrics, which will help them progressing from being a student to being a researcher. 

Course Objective
This course aims at developing students’ ability of applying economics 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. 
Course Requirement
1. Prerequisites
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
(1) introductory microeconomics;
(2) basic calculus, linear algebra, probability, and statistics.

Essentially, students are expected to know what are market (competitive and non-competitive), demand, supply, differentiation, integration, optimization (unconstrained and constrained), Lagrange multiplier, matrix, probability, distribution, density, expectation (conditional and unconditional), 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 but have solid background in mathematics and statistics are welcome.

2. Expectation
Students are expected to review and study the theories developed in classes. The examinations essentially test students' understanding of the theories taught in classes.  
Student Workload (expected study time outside of class per week)
Office Hours
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.

Panel data econometrics
1. Baltagi, B.H. (Ed.), 2015. The Oxford Handbook of Panel Data. Oxford University Press, Oxford.
2. Hsiao, C., 2014. Analysis of Panel Data. 3rd ed. Cambridge University Press, New York.
3. Matyas, L., Sevestre, P. (Eds.), 2008. The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice, 3rd ed. Springer.

Social interactions and networks
1. Bramoulle, Y., Galeotti, A., Rogers, B.W. (Eds.), 2016. The Oxford Handbook of The Economics of Networks. Oxford University Press, New York.
2. Easley, D., Kleinberg, J., 2010. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press.
3. Jackson, M.O., 2008. Social and Economic Networks. Princeton University Press, Princeton.
4. Newman, M.E.J., 2010. Networks: An Introduction. Oxford University Press, Oxford.

1. Efron, B., Hastie, T., 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press, Cambridge.
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.

Model selection and model averaging
1. Claeskens, G., Hjort, N.L., 2008. Model Selection and Model Averaging. Cambridge University Press, Cambridge.
2. Konishi, S., Kitagawa, G., 2008. Information Criteria and Statistical Modeling. Springer, New York.  
Designated reading
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.

1. Konishi, S., 2014. Introduction to Multivariate Analysis: Linear and Nonlinear Modeling. CRC Press, Boca Raton.  
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