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課程名稱 |
勞動經濟學專題: 實證方法與應用
Topics in Labor Economics: Empirical Methods and Applications |
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開課學期 |
109-2 |
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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 |
楊子霆 |
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課號 |
ECON5163 |
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課程識別碼 |
323EU4100 |
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班次 |
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學分 |
2.0 |
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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 |
星期二6,7(13:20~15:10) |
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上課地點 |
社科研607 |
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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：30人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1092ECON5163_ |
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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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課程概述 |
課程概述:
This course will survey empirical methods in labor economics. We will focus on recent advances in these methods as well as their empirical applications. The topics will include randomized (field) experiments, matching method, instrumental variables, differences-in-differences method, synthetic controls method, regression discontinuity (kink) design, machine learning method, text mining, and GIS data. The applications in labor economics include labor market discrimination, life-cycle labor supply, social insurance, labor demand, and tax incidence. We will especially focus on the practical implementation of these methods and tips for data management by writing a term paper. After taking this course, students should be able to conduct empirical research independently.
課程簡介影片:
https://causaldatalab.wordpress.com/2019/01/07/topics-in-labor-economics-empirical-methods-and-applications-spring-2019/ |
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課程目標 |
1. Be able to understand and use recent advances in labor economics and empirical methods
2. Be able to implement a good empirical research and evaluate an empirical studies
3. Have a good start of your research |
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課程要求 |
Two empirical homeworks (20%)
Research progress presentation (10%)
Term paper presentation (20%)
Term paper (50%): milestones throughout the term
You will get extra 5 points in your final grade if you use Latex to type your paper |
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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 |
Pierre Cahuc, Stephane Carcillo and Andre Zylberberg, Labor Economics, Second Edition
Angrist and Pischke (2016), Mastering Metrics: The Path from Cause to Effect
Angrist and Pischke (2009), Mostly Harmless Econometrics
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2017), An Introduction to Statistical Learning with Applications in R |
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評量方式
(僅供參考) |
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