Difference-in-Differences with Panel Data


19-23 August 2024


9:30 to 13:00 CEST


In person

Intended for

Empirical researchers and applied econometricians with an interest in recent advances in difference-in-differences estimation.


A masters level course in probability and statistics - including a basic understand of asymptotic tools - and a course in econometrics at the level of W.H. Greene (2018), Econometric Analysis, 8th edition, F. Hayashi (2000), Econometrics, or B. Hansen (2023), Econometrics. Participants are expected to have a working knowledge of ordinary least squares, generalized least squares, two-way fixed effects, and basic nonlinear estimation methods. It is helpful to know about treatment effect estimation assuming unconfounded assignment. Derivations will be kept to a minimum except where making an essential point.


The purpose of the course is to provide applied economists with an update on some developments in intervention analysis using what are generally known as "difference-in-differences" methods. One theme is that flexible regression methods, whether based on pooled OLS or two-way fixed effects, can be very effective - in both common and staggered timing settings. Other methods that rely on commonly used treatment effects estimators, such as inverse probability weighting combined with regression adjustment, are easily motivated in a common framework. Imputation approaches to estimation, and their relationship to pooled methods, also will be discussed.

Special situations such as all units treated, exit from treatment, and time-varying controls also will be discussed. Event study approaches for detecting the presence of pre-trends, and accounting for heterogeneous trends using both regression and treatment effects estimator, also will be covered. As time permits, strategies with non-binary treatments also will be discussed. The main focus is on microeconometric applications where the number of time periods is small. Nevertheless, some coverage of "large-T" panels is also included, including cases with few treated units. Simple strategies for small-N inference will be discussed, and compared with synthetic control methods.

The course will end with a treatment of nonlinear difference-in-differences methods, with focus on binary, fractional, and nonnegative outcomes (including counts). Logit and Poisson regression are especially attractive for such applications.


  • Introduction and Overview. The T = 2 Case. General Common Intervention Timing. Parallel Trends. Controlling for Covariates via Regression Adjustment and Propensity Score Methods. Event Study Estimators and Heterogeneous Trends
  • Staggered Interventions, I. Imputation, Pooled OLS, and Extended TWFE. Aggregation. Event Study Estimators. Testing and Correcting for Violations of Parallel Trends
  • Staggered Interventions, II. Imputation Using Fixed Effects. Rolling Methods. Long Differencing. Propensity Score and Doubly Robust Methods. Strategies with Exit
  • Staggered Interventions, III. Time-Varying Covariates. Unbalanced Panels. Non-Binary Treatments. Repeated Cross Sections
  • Inference with few Treated Units. Synthetic Control. Nonlinear DiD. Binary, Fractional, and Nonnegative Responses. Repeated Cross Sections

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. He is the 2024 winner of the T.W. Schultz Memorial Award. 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 received three teacher-of-the-year awards from the graduate economics association at MIT, and he has taught dozens of short courses internationally on applied econometrics.

Dr. Wooldridge has 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).