Type of Credit: Elective
Credit(s)
Number of Students
This course will focus on the empirical aspects of conducting research in applied microeconomics. We will first go over some of the most widelly used econometric models, and use academic articles in international trade and industrial economics to illustrate how careful empirical analyses are done. The class will involve intensive academic readings, and active participation among students.
能力項目說明
The goal of the course is to equip students with the essential knowledge of conducting empirical work. By the end of the course, students should understand:
1. What is the identification strategy of the study?
2. What are the basic assumptions behind an empirical model?
3. How to assess and review an empirical paper?
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
Students are expected to spend more than 6 hours of out-of-class readings each week.
Week 1: Introduction
Week 2: Regression and Causality
Week 3: Regression and Causality
Week 4: Instrumental Variable Regression
Week 5: Instrumental Variable Regression
Week 6: Panel Data
Week 7: Panel Data
Week 8: Introduction to Program Evaluation
Week 9: Empirical Study Reading 1
Week 10: Introduction to Program Evaluation
Week 11: Introduction to Program Evaluation
Week 12: Introduction to Machine Learning Methods
Week 13: Introduction to Machine Learning Methods
Week 14: Empirical Study Reading 2
Week 15: Final Report Presentation
Class Participation 50%
Final Report 50%
The following textbooks/articles are only for reference:
1. Wooldridge (2015) Introductory Econometrics: A Modern Approach.
2. Angrist and Pischke (2009). Mostly Harmless Econometrics.
3. Athey and Imbens (2017). "The State of Applied Econometrics Causality and Policy Evaluation," Journal of Economic Perspectives.
4. Athey and Imbens (2019). "Machine Learning Methods Economists Should Know About," Annual Review of Economics.