|
課程名稱 |
內生性計量經濟分析
Econometric Analysis of Endogeneity |
|
開課學期 |
106-1 |
|
授課對象 |
社會科學院 經濟學研究所 |
|
授課教師 |
陳釗而 |
|
課號 |
ECON5140 |
|
課程識別碼 |
323 U7250 |
|
班次 |
|
|
學分 |
3.0 |
|
全/半年 |
半年 |
|
必/選修 |
選修 |
|
上課時間 |
星期三6,7,8(13:20~16:20) |
|
上課地點 |
社科研607 |
|
備註 |
先修課程:大二統計學。
限學士班三年級以上 或 限碩士班以上
總人數上限：24人 |
|
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1061ECON5140_endo |
|
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
|
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
|
課程概述 |
Treatment of econometric ideas and methods on endogeneity. Econometric methods illustrated with economic and corporate finance applications. The course has been tailored to the advanced undergraduate teaching. Covers topics:
1. Causal Regression and Casual Regression
2. Matching Makers
3. Instrumental Variables Methods
4. Quantile Models with Endogeneity
5. Mostly Dangerous Big Data
6. Regression Discontinuity Designs
7. Nonparametric Instrumental Variables Estimation
8. Overview of Structural Estimation in Corporate Finance
|
|
課程目標 |
Introduce students to econometric methodologies essential for dealing with endogeneity in empirical research.
每週進度及教學內容簡述
第一週：Causal Regression and Casual Regression
第二週：Causal Regression and Casual Regression
第三週：Matching Makers
第四週：Matching Makers
第五週：Instrumental Variables Methods
第六週：Instrumental Variables Methods
第七週：Instrumental Variables Methods
第八週：Quantile Models with Endogeneity
第九週：Quantile Models with Endogeneity
第十週：Quantile Models with Endogeneity
第十一週：Mostly Dangerous Big Data
第十二週：Mostly Dangerous Big Data
第十三週：Mostly Dangerous Big Data
第十四週：Regression Discontinuity Design
第十五週：Nonparametric IV Estimation
第十六週：Overview of Structural Estimation in Corporate Finance
第十七週：Student Presentations
第十八週：Student Presentations
|
|
課程要求 |
The course grade will be based on your participating in class discussions (30%), a proposal of research project (30%), and a presentation of your group research project (40%, peer grading).
Prereq: Econ2012 Statistics and Econometrics with Recitation. Enrollment limited.
本課程對學生課後學習之要求:
閱讀指定文獻; 與同組成員討論並執行研究計畫，將研究計畫撰寫成英文論文。
[Optional]
學期結束後，於 2/20/2018 (台灣連假日)、學生可選擇是否參加本課程(台灣大學)與 京都大學、九州大學 三校經濟學系聯合舉辦的學生論文發表及交流會，地點在日本福岡市九州大學。課程的學期成績與是否參加三校聯合會議無關。三校經濟系學生論文發表會: https://goo.gl/CtqYFD |
|
預期每週課後學習時數 |
|
|
Office Hours |
|
|
指定閱讀 |
待補 |
|
參考書目 |
一、 指定閱讀
Angrist, J.D. and J. Pischke (2009), Mostly Harmless Econometrics: An Empiricist’s Companion, Princeton University Press.
Angrist, J.D. and J. Pischke. (2015). Mastering 'Metrics: The Path from Cause to Effect, Princeton University Press.
Bascle, Guilhem (2008), “Controlling for Endogeneity with Instrumental Variables in Strategic Management Research,” Strategic Organization, 6(3): 285–327.
Bertomeu, J., Beyer, A., and D. J. Taylor. (2016). ``From Casual to Causal Inference in Accounting Research: The Need for Theoretical Foundations,'' Foundations and Trends in Accounting, 10(2-4), 262--313.
Laurent Fresard D.E.B. and J.P. Taillard. (2017). ``What's Your Identification Strategy? Innovation in Corporate Finance Research,'' Management Science.
Roberts M.R. and T.M. Whited (2012), “Endogeneity in Empirical Corporate Finance,” Handbook of the Economics of Finance, Volume 2.
二、 延伸閱讀
Imai K. 2017. Quantitative Social Science: An Introduction. Princeton University Press.
Hernan M.A. and J.M. Robins. 2017. Causal Inference. Boca Raton: Chapman & Hall/CRC.
Shalit U. and D. Sontag. 2016. ICML Tutorial (causal machine learning): Causal Inference for Observational Studies, MIT and NYU.
Belloni, A., Chernozhukov V., and C. Hansen (2013), “Inference on Treatment Effects after Selection among High-Dimensional Controls,” The Review of Economic Studies, 81(2): 608-650.
Chernozhukov, V. and C. Hansen (2013) “Econometrics of High-Dimensional Sparse Models,” NBER Lectures and Video Materials: http://www.nber.org/econometrics_minicourse_2013
Chernozhukov, V., Hansen C., and M. Spindler (2015), “Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments,” American Economic Review: Papers & Proceedings, 105(5): 486-490.
Newey, W. (2013), “Lessons from Nonparametric Methods in Historical Perspective – Nonparametric Instrumental Variable Estimation,” American Economic Review: Papers & Proceedings, 103(3): 550 556.
Whited, T.M. (2015), Overviw of Structural Estimation in Corporate Finance, lecture slides and lecture video.
|
|
評量方式
(僅供參考) |
|
No. |
項目 |
百分比 |
說明 |
|
1. |
課堂討論 |
30% |
|
2. |
(期中)報告研究計畫大綱 |
30% |
2至3人一組 |
3. |
(期末)上台報告研究成果 |
40% |
2至3人一組 |
|
|
週次 |
日期 |
單元主題 |
|
第1週 |
9/13 |
syllabus |
|
第2週 |
9/20 |
RCT and Regression |
|
第3週 |
9/27 |
Regression |
|
第6週 |
10/18 |
Matchmaker |
|
第7週 |
10/25 |
Propensity Score. Instrumental Variable Estimation. |
|
第9週 |
11/08 |
IV, take 2. |
|
第12週 |
11/29 |
Quantile models with endogeneity |
|
第14週 |
12/13 |
data science, LASSO, machine learning |
|