課程資訊
課程名稱
計量經濟學的因果推論與預測
Causal Inference and Prediction in Econometrics 
開課學期
109-1 
授課對象
社會科學院  經濟學系  
授課教師
郭漢豪 
課號
ECON5179 
課程識別碼
323EU4300 
班次
 
學分
2.0 
全/半年
半年 
必/選修
選修 
上課時間
星期二3,4(10:20~12:10) 
上課地點
社科402 
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 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1091ECON5179_ 
課程簡介影片
 
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課程概述

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.

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, and statistics.

Essentially, students are expected to know what are market (competitive and non-competitive), demand, supply, differentiation, integration, 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.
 

課程目標
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. 
課程要求
Students are expected to review and study the theories developed in classes. 
預期每週課後學習時數
 
Office Hours
 
指定閱讀
Slides and notes for some topics will be provided. Some chapters of the books in the suggested reading list will be used. In the classes, it will be clear that which chapters of which books are related to the discussions. 
參考書目
Suggested Readings
Econometrics
1. Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge.
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. Eatwell, J., Milgate, M., Newman, P. (Eds.), 1990. The New Palgrave: Econometrics. The Macmillan Press Limited, London.
5. Hassani, H., Mills, T.C., Patterson, K. (Eds.), 2006. Palgrave Handbook of Econometrics, Volume 1: Econometric Theory. Palgrave Macmillan, New York.
6. Hayashi, F. 2000. Econometrics. Princeton University Press, Princeton.
7. Lee, M.J., 2010. Micro-econometrics: Methods of Moments and Limited Dependent Variables, 2nd ed. Springer, New York.
8. Mills, T.C., Patterson, K. (Eds.), 2009. Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics. Palgrave Macmillan, New York.
9. Wooldridge, J.M., 2010. Econometric Analysis of Cross Section and Panel Data, 2nd ed. The MIT Press, Cambridge.

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.

Statistics
1. Konishi, S., 2014. Introduction to Multivariate Analysis: Linear and Nonlinear Modeling. CRC Press, Boca Raton.
2. Efron, B., Hastie, T., 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press, Cambridge

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. 
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
9/15  Meanings of identification in econometrics 
第2週
9/22  Meanings of identification in econometrics 
第3週
9/29  Moment-based identification and estimation 
第4週
10/06  Moment-based identification and estimation 
第5週
10/13  Extremum-based identification and estimation 
第6週
10/20  Extremum-based identification and estimation 
第7週
10/27  Endogeneity problems in econometrics 
第8週
11/03  Endogeneity problems in econometrics 
第9週
11/10  Midterm examination 
第10週
11/17  Model selection and prediction 
第11週
11/24  Model selection and prediction 
第12週
12/01  Causality in econometrics 
第13週
12/08  Causality in econometrics 
第14週
12/15  Frequentist, Bayesian, and Fisherian inferences 
第15週
12/22  Frequentist, Bayesian, and Fisherian inferences 
第16週
12/29  Frequentist, Bayesian, and Fisherian inferences 
第17週
1/05  Frequentist, Bayesian, and Fisherian inferences