中文課名：高等計量方法專題
授課教師：陳釗而
研究室：台大次震宇宙館 223室
Course Description
This course is organized around three major themes in modern econometrics:
- Quantile regression and quantile treatment effects
- Factor models
- Causal machine learning and targeting
Each theme spans five weeks. In the first three weeks of each module, the instructor will introduce the core theory, intuition, and econometric techniques required to understand the topic. In the remaining two weeks, students will present assigned research papers and lead discussions. This structure is designed to balance methodological training with exposure to frontier empirical and methodological research.
Course Objective
By the end of the course, students will be able to:
- Understand and apply advanced econometric tools related to distributional analysis, factor structures, and causal machine learning.
- Critically read, interpret, and evaluate contemporary econometric research articles.
- Implement the covered methods using programming or statistical software and apply them to real empirical problems.
- Produce a term paper (written individually or in groups of up to three students) that demonstrates mastery of at least one of the methodological frameworks taught in the course.
Course Outline
Theme I: Quantile Regression and Quantile Treatment Effects
Week 1: Instructor-led lecture
Week 2: Instructor-led lecture
Week 3: Instructor-led lecture
Week 4: Student presentations
Week 5: Student presentations
Theme II: Factor Models
Week 6: Instructor-led lecture
Week 7: Instructor-led lecture
Week 8: Instructor-led lecture
Week 9: Student presentations
Week 10: Student presentations
Theme III: Causal Machine Learning and Targeting
Week 11: Instructor-led lecture
Week 12: Instructor-led lecture
Week 13: Instructor-led lecture
Week 14: Student presentations
Week 15: Student presentations
Week 16: Discussion
Readings
Readings assigned for presentations:
Theme I
Rios-Avila, F., & Maroto, M. L. (2024). Moving beyond linear regression: Implementing and interpreting quantile regression models with fixed effects. Sociological Methods & Research, 53(2), 639-682.
Carriero, A., Clark, T. E., & Marcellino, M. (2025). Specification choices in quantile regression for empirical macroeconomics. Journal of Applied Econometrics, 40(1), 57-73.
Chernozhukov, V., Fernández-Val, I., & Luo, S. (2025). Distribution regression with sample selection and uk wage decomposition. Journal of Political Economy, 133(12), 3952-3992.
Theme II
Bai, J., & Wang, P. (2016). Econometric analysis of large factor models. Annual Review of Economics, 8(1), 53-80.
Bai, J., & Wang, P. (2024). Causal inference using factor models.
Giglio, S., Kelly, B., & Xiu, D. (2022). Factor models, machine learning, and asset pricing. Annual Review of Financial Economics, 14(1), 337-368.
Theme III
Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M. & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI. CausalML-book.org Chapters 14 and 15
Athey, S., Keleher, N., & Spiess, J. (2025). Machine learning who to nudge: causal vs predictive targeting in a field experiment on student financial aid renewal. Journal of Econometrics, 249, 105945.
Grading
Assessment consists of:
- Two in-class presentations of assigned research papers (50%)
- A term paper (individual or group work of up to three students), due in Week 18, applying at least one econometric method covered in this course, accompanied by a 15–20 minute recorded presentation explaining the term paper (50%)
Jauer 