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課程名稱 |
內生性計量經濟分析
Econometric Analysis of Endogeneity |
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開課學期 |
105-1 |
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授課對象 |
社會科學院 經濟學研究所 |
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授課教師 |
陳釗而 |
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課號 |
ECON5140 |
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課程識別碼 |
323 U7250 |
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班次 |
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學分 |
3 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期三6,7,8(13:20~16:20) |
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上課地點 |
社科研607 |
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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 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1051ECON5140_mhe |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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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 Regressions and Casual Regressions; 2. Instrumental Variables Methods; 3. Mostly Harmless Big Data; 4. Quantile Models with Endogeneity; 5. Panel Data Models; 6. Nonparametric Instrumental Variables Estimation; 7. Overveiw of Structural Estimation in Corporate Finance.
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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 Regressions and Casual Regression
第二週：Causal Regressions and Casual Regression
第三週：Instrumental Variables Methods
第四週：Instrumental Variables Methods
第五週：Instrumental Variables Methods
第六週：Student Presentations (3 papers)
第七週：Mostly Harmless Big Data
第八週：Mostly Harmless Big Data
第九週：Mostly Harmless Big Data
第十週：Student Presentations (3 papers)
第十一週：Quantile Models with Endogeneity
第十二週：Quantile Models with Endogeneity
第十三週：Quantile Models with Endogeneity
第十四週：Student Presentations (3 papers)
第十五週：Panel Data Models
第十六週：Nonparametric IV Estimation
第十七週：Overview of Structural Estimation in Corporate Finance
第十八週：Student Presentations (3 papers)
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課程要求 |
The course grade will be based on problem sets and your participating in class discussions (30%), a presentation (40%, peer grading), and a term paper (30%).
本課程對學生課後學習之要求:
Do reading, and do problem sets. |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
待補 |
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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.
Bascle, Guilhem (2008), “Controlling for Endogeneity with Instrumental Variables in Strategic Management Research,” Strategic Organization, 6(3): 285–327.
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
Angrist, J.D. and J. Pischke (2015), Mastering ‘Metrics: The Path from Cause to Effect, Princeton University Press.
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.
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評量方式
(僅供參考) |
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週次 |
日期 |
單元主題 |
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第1週 |
9/14 |
課程大綱 |
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第2週 |
9/21 |
regression recap |
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第4週 |
10/05 |
matchmaker |
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第5週 |
10/12 |
propensity score |
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第6週 |
10/19 |
IV - part 1 |
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第7週 |
10/26 |
報告文章 |
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第8週 |
11/02 |
IV - part 2 |
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第14週 |
12/14 |
quantile regressions |
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第16週 |
12/28 |
double selection |
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第17週 |
1/04 |
regression discontinuity design |
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