Syllabus
Lecture 1 (Tuesday, February 25, 12:30) - Introduction, data sources, usual empirical problems
Lecture 2 (Thursday, February 27, 15:30) - Selection to treatment, identification strategies, introduction to controlled experiments
Lecture 3 (Tuesday, March 4, 12:30) - Controlled experiments, the statistical power of an experiment
Seminar 1 (Thursday, March 6, 15:30) - Discussion of experiments
Lecture 4 (Tuesday, March 11, 15:30) - Natural experiments I - difference-in-differences estimation
Lecture 5 (Tuesday, March 18, 12:30) - Difference-in-differences continued - robustness, event study analysis
Seminar 2 (Thursday, March 20, 15:30) - Applying difference-in-differences in practice
Lecture 6 (Tuesday, March 25, 12:30) - Synthetic control function
Lecture 7 (Tuesday, April 1, 12:30) - Natural experiments II - natural experiments as instruments
Seminar 3 (Thursday, April 3, 15:30) - Applying synthetic control function in practice
Lecture 8 (Tuesday, April 8, 12:30) - Further issues with instrumental variable estimation
Seminar 4 (Thursday, April 10, 15:30) - instrumental variable estimation, checking the quality of instruments
Lecture 9 (Tuesday, April 15, 12:30) - Regression discontinuity - sharp
Lecture 10 (Tuesday, April 22, 12:30) – Regression discontinuity - fuzzy
Seminar 5 (Thursday, April 23, 15:30) - applying regression discontinuity in practice - randomization checks, method choice, etc.Lecture 11 (Tuesday, April 29, 15:30) – Matching models Lecture 12 (Tuesday, May 6, 12:30) - Matching models
Seminar 6 (Wednesday, May 7, 11:00) – matching models in practice
Thursday, May 15 (15:30), Tuesday, May 20 (12:30), Thursday, May 22 (15:30) – Student presentations: final projects
Annotation
- Do you know that correlation does not imply causation but do not know how to identify causality?
- Do you like connecting Econometrics and economic theory?
This course is ideal for students who intend to do empirical research, work for international organizations such as the OECD, work in policy evaluation (think-tank, government, etc.) analyze large firm-level data (banking, credit scoring, consumer behavior monitoring, empirical HRM, etc.), or who are writing an empirical thesis, especially using microdata.
During the Applied Microeconometrics course, you will learn how to let the data talk and will get familiar with several econometric methods useful for estimating the causal effects of employees', customers', firms', or states' decisions. For example:
"Did the marketing campaign increase the firm's profits?... or was it just implemented at the time when the firm's profits were rising?"
"Did introducing interest rate caps lead to lower personal bankruptcy rates?... or were these caps introduced when bankruptcy rates were falling due to other reasons?"
"Did the limitation of cigarette advertising lead to less smoking?... or would the incidence of smoking fall even without this policy?
"Do incumbent politicians have an advantage over runner-ups?... or did voters choose them in previous elections and will choose them again simply because they are better?
"Does studying at a high-quality college lead to higher earnings?... or is it just that students from richer families can afford better colleges?"
