課程資訊
課程名稱
 經濟學與計量經濟學專題
Topics in Economics and Econometrics 
開課學期
 112-2 
授課對象
 社會科學院  經濟學系  
授課教師
 郭漢豪 
課號
 ECON5169 
課程識別碼
 323EU1080 
班次
  
學分
 3.0 
全/半年
 半年 
必/選修
 選修 
上課時間
 星期二6,7,8(13:20~16:20) 
上課地點
 社科401 
備註
 本課程以英語授課。
限學士班三年級以上 或 限碩士班以上 或 限博士班
總人數上限：50人 
  
課程簡介影片
 
核心能力關聯
 核心能力與課程規劃關聯圖
課程大綱
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課程概述

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. 

課程目標
 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. 
課程要求
 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. 
預期每週課後學習時數
 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. 
Office Hours
  
指定閱讀
 The sources of teaching materials will be clear in the classes. 
參考書目
 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. 
評量方式
(僅供參考)
  
No.
項目
百分比
說明
1. 
 Homework 
20% 
  
2. 
 Midterm Examination 
40% 
  
3. 
 Final examination 
40% 
  
 
課程進度
週次
日期
單元主題
第1週
   Introduction to economics and econometrics, basic probability and statistics 
第2週
   Consistent estimation, hypothesis testing 
第3週
   Consistent estimation, hypothesis testing 
第4週
   Consistent estimation, hypothesis testing 
第5週
   Model selection, model averaging, prediction 
第6週
   Model selection, model averaging, prediction 
第7週
   Model selection, model averaging, prediction 
第8週
   Spatial econometrics, network econometrics 
第9週
   Midterm examination 
第10週
   Spatial econometrics, network econometrics 
第11週
   Spatial econometrics, network econometrics 
第12週
   Bayesian econometrics 
第13週
   Bayesian econometrics 
第14週
   Advanced topics: economic and social networks, poverty, inequality, intergenerational mobility. 
第15週
   Advanced topics: economic and social networks, poverty, inequality, intergenerational mobility. 
第16週
   Final examination