The course is conducted in English。

This is a self-contained course on advanced economic and econometric theories. In the classes, we will discuss topics from foundational to research levels. Precisely, we start with the foundations of mathematical modeling and their corresponding statistical methods; then we gradually proceed to research topics; for example, economic and social networks, poverty, inequality, and intergenerational mobility. We will focus more on econometrics. We will start with foundational econometrics and then talk about the economic foundations of the econometric models. This course emphasizes the integration of economics and econometrics. Students are encouraged to develop a big picture of economics and econometrics. By taking this course, students will learn how to integrate their introductory economic and econometric knowledge with the knowledge in research papers. This course aims at providing trainning for being a researcher.

This course aims at developing students’ ability of developing and 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.

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). The prerequisites are introductory knowledge in microeconomics, calculus, linear algebra, probability, and statistics. Students are expected to know what are (competitive and non-competitive) market, demand, supply, differentiation, integration, (constrained and unconstrained) optimization, Lagrange multiplier, scalar, vector, matrix, probability, distribution, density, (conditional and unconditional) expectation, moment, 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.

Students are expected to study the theories developed in classes every week. It is impossible to cover all important ideas in each topic in classes; students are encouraged to read the related books and papers, in order to develop a broader and deeper understanding of each topic.

The sources of teaching materials will be clear in the classes.

Probability 1. DasGupta, A., 2008. Asymptotic Theory of Statistics and Probability. Springer. 2. DasGupta, A., 2010. Fundamentals of Probability: A First Course. Springer. 3. DasGupta, A., 2011. Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics. Springer. 4. Stoyanov, J.M., 2013. Counterexamples in Probability, 3rd ed. Dover Publications. 5. Durrett, R., 2019. Probability: Theory and Examples, 5th ed. Cambridge University Press. Statistics 1. Maritz, J.S., Lwin, T., 1989. Empircial Bayes Methods, 2nd ed. CRC Press. 2. Wasserman, L., 2004. All of Statistics: A Concise Course in Statistical Inference. Springer. 3. Wasserman, L., 2010. All of Nonparametric Statistics. Springer. 4. Efron, B., 2010. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press. 5. Konishi, S., 2014. Introduction to Multivariate Analysis: Linear and Nonlinear Modeling. CRC Press. 6. Bickel, P.J., Doksum, K.A., 2015. Mathematical Statistics: Basic Ideas and Selected Topics, Volume 1. CRC Press. 7. Bickel, P.J., Doksum, K.A., 2016. Mathematical Statistics: Basic Ideas and Selected Topics, Volume 2. CRC Press. 8. Efron, B., Hastie, T., 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press. 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. 2. Claeskens, G., Hjort, N.L., 2008. Model Selection and Model Averaging. Cambridge University Press. 3. Konishi, S., Kitagawa, G., 2010. Information Criteria and Statistical Modeling. Springer. 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. 3. Wooldridge, J.M., 2010. Econometric Analysis of Cross Section and Panel Data, 2nd ed. The MIT Press. 4. Lee, M.J., 2010. Micro-econometrics: Methods of Moments and Limited Dependent Variables, 2nd ed. Springer. 5. Chan, J., Koop, G., Dale, J.P., Tobias, J.L., 2020. Bayesian Econometric Methods, 2nd ed. Cambridge University Press. 6. Hansen, B.E., 2022. Probability and Statistics for Economists. Princeton University Press. 7. Hansen, B.E., 2022. Econometrics. Princeton University Press. Econometrics: Handbooks 1. Eatwell, J., Milgate, M., Newman, P. (Eds.), 1990. The New Palgrave: Econometrics. The Macmillan Press Limited. 2. Hassani, H., Mills, T.C., Patterson, K. (Eds.), 2006. Palgrave Handbook of Econometrics, Volume 1: Econometric Theory. Palgrave Macmillan. 3. Mills, T.C., Patterson, K. (Eds.), 2009. Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics. Palgrave Macmillan. 4. Durlauf, S.N., Blume, L.E. (Eds.), 2010. Microeconometrics. Palgrave Macmillan. 5. Durlauf, S.N., Blume, L.E. (Eds.), 2010. Macroeconometrics and Time Series Analysis. Palgrave Macmillan. Econometrics: Theory 1. Bierens, H.J., 1981. Robust Methods and Asymptotic Theory in Nonlinear Econometrics. Springer. 2. Bierens, H.J., 1996. Topics in Advanced Econometrics: Estimation, Testing, and Specification of Cross-Section and Time Series Models. Cambridge University Press. 3. Bierens, H.J., 2005. Introduction to the Mathematical and Statistical Foundations of Econometrics. Cambridge University Press. 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. 3. Baltagi, B.H. (Ed.), 2015. The Oxford Handbook of Panel Data. Oxford University Press. Econometrics: Spatial Econometrics 1. Kelejian, H., Piras, G., 2017. Spatial Econometrics. Academic Press 2. Lee, L.F., 2024. Spatial Econometrics: Spatial Autoregressive Models. World Scientific. Econometrics: Causality and Treatment Effects 1. Lee, M.J., 2005. Micro-Econometrics for Policy, Program, and Treatment Effects. Oxford University Press. 2. Lee, M.J., 2016. Matching, Regression Discontinuity, Difference in Differences, and Beyond. Oxford University Press. Economics and Econometrics: Social Interactions and Networks 1. Jackson, M.O., 2008. Social and Economic Networks. Princeton University Press. 2. Newman, M.E.J., 2010. Networks: An Introduction. Oxford University Press. 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. 5. Graham, B., de Paula, A. (Eds.), 2020. The Econometric Analysis of Network Data. Academic Press.