課程大綱

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

110-1

授課對象

社會科學院經濟學研究所

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/1101ECON5179_

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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.

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

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.

預期每週課後學習時數

Office Hours

指定閱讀

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.

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

參考書目

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.

Statistics
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.

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.

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.	Final examination	45%
2.	Midterm examination	45%
3.	Homework	10%

課程進度

週次	日期	單元主題
第1週	9/28	Meanings of identification in econometrics
第2週	10/05	Meanings of identification in econometrics
第3週	10/12	Moment-based identification and estimation
第4週	10/19	Moment-based identification and estimation
第5週	10/26	Extremum-based identification and estimation
第6週	11/02	Extremum-based identification and estimation
第7週	11/09	Endogeneity problems in econometrics
第8週	11/16	Endogeneity problems in econometrics
第9週	11/23	Midterm examination
第10週	11/30	Model selection and prediction
第11週	12/07	Model selection and prediction
第12週	12/14	Causality in econometrics
第13週	12/21	Causality in econometrics
第14週	12/28	Frequentist, Bayesian, and Fisherian inferences
第15週	01/04	Frequentist, Bayesian, and Fisherian inferences
第16週	01/11	Frequentist, Bayesian, and Fisherian inferences
第17週	01/18	Frequentist, Bayesian, and Fisherian inferences
第18週	01/25	Final examination