This half-course covers some commonly used designs and empirical research in applied microeconomics and related fields. For Princeton students, homework and solutions to it will be posted on Canvas. Official course description is at the Registrar's website.

Good empirical research has three pillars:

1. Measurement. One can't really answer a policy question (what's the effect of intervention A) or a science question (what's the elasticity of demand) without being able to measure the key quantities. Indeed, much of the progress in the last 20 or so years in applied micro has been due to better measurement---access to better and more reliable data.
2. Design. This another catch-all that just means the empirical specification you run is grounded in something. You don't just throw a kitchen sink of controls at the problem and argue unconfoundedness. Instead, identification exploits some economic or institutional feature of the data.
3. Inference. You have a way of assessing the reliability of your estimates.

This course has nothing to say about the most important pillar, measurement. It will have something to say about the second pillar, though for much of the course we'll take the empirical specification as given, and instead think through the assumptions needed to make sense of the estimand. We'll spend much time on getting the inference right, which is the least important pillar: a good paper requires good measurement and good design, but not necessarily great inference.

It is not that common that some naive way of estimating the standard errors is off by an order of magnitude (one major exception being the question of whether to cluster the standard errors), but there are lots of settings where the estimates may be off by an order of magnitude if the design is off. Yet there are many settings where naive inference may mislead, and it's important to recognize those settings.

I provide detailed lecture notes with references. Sometimes I will provide slides. The goal of the slides is just to streamline classroom discussion; they are incomplete. In contrast, the goal of the lecture notes is to serve as a reference for your own later research, and as such, they aim to be self-contained and detailed. In lectures (and slides), we won't have time to dive into all the details, so it's a good idea to read the notes ahead of time, or at least after class.

I'll give weekly homework, which will be a mix of empirical exercises and critical summaries of assigned readings. They will be posted on Canvas.

1. The bootstrap: when does it work, when does it fail? Notes
2. Linear regression. What does regression estimate when regresison function is not linear / treatment effects are heterogeneous? When is the estimand causal? Model-based versus design-based identification and inference. What variable to cluster on? Notes
3. Small-sample issues and large data issues in linear regression: when do usual robust standard errors mislead and what to do instead. Notes
4. Instrumental variables. What does IV estimate when treatment effects are heterogeneous? How does inference change under heterogeneity? How to detect weak instruments and what, if anything, to do about them. Notes
5. Many instruments and judge designs. Notes Slides
6. Shift-share instruments Slides
7. Regression discontinuity. Standard vs bias-aware approaches to inference. Notes Slides
8. Differences in differences and event studies. Changes-in-Changes, conditional common trends, interpretation under treatment effect heterogeneity. Notes
9. Simulated method of moments. Indirect inference, simulated maximum likelihood, importance sampling, and implementation issues. Notes
10. Synthetic controls Notes

Please report typos and errors in the lecture notes by opening an issue.