Difference-in-Differences and Event Studies for Panel Data and Repeated Cross Sections with Jeffrey M. Wooldridge (MSU)

Course Outline and Software

The course will discuss how flexible regression methods apply to repeated cross sections. We will begin with flexible regression-based methods, including two-way fixed effects estimation of a flexible equation allowing for staggered interventions and heterogeneous treatment effects. Imputation methods and doubly robust methods based on rolling estimation (including long differencing) also will be covered.

Professor Wooldridge will make connections between standard difference-in-differences estimators and event-study estimators, including how to make event-study methods more flexibly by controlling for covariates in order to relax the parallel trends assumption. We will discuss how to test for pre-trends and how to adjust for heterogeneous trends.

Other topics include modifications required if there is no never treated group, how to allow for exit from treatment, how to handle unbalanced panels, and issues that arise with time-varying control variables. We will also learn how one can obtain inference in situations with a small number of cross-sectional units, as well as provide an overview of synthetic control methods.

We will briefly cover extension of regression-based methods to non-binary treatments. Professor Wooldridge will show how linear regression methods extend to nonlinear difference-in-differences methods for binary, fractional, and nonnegative (including count and corner solution) outcomes. The final topic shows how methods for panel data can be modified for repeated cross sections.

Instruction:

Jeffrey M. Wooldridge is University Distinguished Professor of Economics and Walter Adams Distinguished Faculty Fellow in Economics at Michigan State University, where he has taught since 1991. He previously taught at MIT. He received his bachelor of arts, with majors in computer science and economics, from the University of California, Berkeley, and his doctorate in economics from the University of California, San Diego. Dr. Wooldridge is a fellow of the Econometric Society and of the Journal of Econometrics, and is a founding fellow of the International Association for Applied Econometrics. His other awards include the Distinguished Author award from the Journal of Applied Econometrics, the Plura Scripset award from Econometric Theory, and the Sir Richard Stone prize from the Journal of Applied Econometrics. He has also served on several editorial boards, including as editor of the Journal of Business and Economic Statistics. Dr. Wooldridge has written chapters for the Handbook of Econometrics and the Handbook of Applied Econometrics. He is the author of of the textbooks Introductory Econometrics: A Modern Approach (South-Western, 7e, 2019) and Econometric Analysis of Cross Section and Panel Data (MIT Press, 2e, 2010).

Program

- DAY 1

Session 1: 9:15-10:30

Introduction and Overview; Two-Period Panel Data Case; No Anticipation and Parallel Trends; Regression Adjustment and Propensity Score Methods

Coffee Break: 10:30-10:45

Session 2: 10:45-12:00

General Common Intervention Timing; Pooled OLS and Extended Controlling for Covariates via Regression Adjustment; Event Study Estimation

Lunch Break: 12:00-13:30

Session 3: 13:30-14:45

Staggered Interventions, I; Heterogeneous Effects; Imputation; Pooled OLS and Extended TWFE; All Units Eventually Treated

Coffee Break: 14:45-15:00

Practical Session: 15:00-16:30

- DAY 2

Session 4: 9:15-10:30

Staggered Interventions, II. Strategies with Exit. Event Study Methods. Testing and Correcting for Violation of Parallel Trends. 

Coffee Break: 10:30-10:45

Session 5: 10:45-12:00

Staggered Interventions, III; Alternative Imputation Estimators; Rolling Methods and Long Differencing; Propensity Score Methods. 

Lunch Break: 12:00-13:30

Session 6: 13:30-14:45

Small Number of Cross-Sectional Units; Synthetic Control Methods

Coffee Break: 14:45-15:00

Practical Session: 15:00-16:30

- DAY 3

Session 7: 9:15-10:30

Non-Binary Treatments; Time-Varying Covariates; Unbalanced Panels; Standard Errors

Coffee Break: 10:30-10:45

Session 8: 10:45-12:00

Nonlinear DiD. Binary, Fractional, and Nonnegative Reponses; Quasi-MLE Estimation

Lunch Break: 12:00-13:30

Session 9: 13:30-14:45

Difference-in-Differences with Repeated Cross Sections

- DAY 4

Research Seminar: “Estimating Distributional Treatment Effects with Staggered

Interventions for Panel Data”

Laboratory/practical sessions will be taught using Stata, and participants are expected to bring their laptops.

Temporary licenses will be freely provided to participants during the summer school.

Entry Requirements

A graduate degree (MSc or PhD) in Economics, Management, Finance, Mathematics, Statistics, or closely related fields. We also welcome applications from qualified MSc students in these fields.

Please note that ECTS credits are not issued for this summer school.

Fees and Application deadline

The application deadline is June 30th, 2024; The early-bird discount applies to payments made on or before June 3rd, 2024.

Early registration: 650€                                                               Late registration: 800€

Students* (PhD, Master) early registration: 500€                     Students* (PhD, Master) late registration: 650€

A cancellation fee of 100 EUR is charged for cancellations until July 5th, 2024. No reimbursements will be made for cancellations after this date.

Fees cover attendance, lunches and two refreshments from Monday to Wednesday (July 15–17), and a bus pass (provided upon registration) which allows you to use Braga’s public transport (TUB) at no cost during the summer school.

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