Adam Smith Business School

Professor Jeffrey Wooldridge, Michigan State University

"A Simple Transformation Approach to Difference-in-Differences Estimation for Panel Data" (Joint with Soo Jeong Lee)
Friday, 08 November 2024. 16:00-17:30
Online

Abstract

In the case of panel data, we propose a simple time-series transformation that can be combined with various treatment effect estimators, including regression adjustment, matching methods, and doubly robust estimators. The approach is motivated by the fact that, in the common timing case, our transformation, when applied with linear regression adjustment, numerically reproduces the pooled OLS estimator in Wooldridge (2021). In the general staggered case, the transformation is at the unit level, and simply requires computing the average outcome prior to an intervention, subtracting it from a post-treatment outcome, and then carefully selecting the control units in each time period. We show formally that, allowing for staggered entry under no anticipation and parallel trends assumptions, the cohort treatment indicators satisfy the key unconfoundedness assumption with respect to the transformed potential outcome. Given identification, any number of treatment effect estimators can be applied for each treated cohort and calendar time pair where the average treatment effects on the treated are identified. In effect, we establish the consistency of intuitively appealing rolling methods. The doubly robust method of combining inverse probability weighting with linear regression works particularly well in terms of bias and efficiency. Long differencing methods, such as those proposed by Callaway and Sant'Anna (2021), can be considerably less efficient. We also show how to modify the transformation to account for unit-specific trends.

Bio

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).

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First published: 4 November 2024