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• W3197062812 abstract "A broad empirical literature uses study, or difference-in-differences with staggered rollout, research designs for treatment effect estimation: settings in which units in the panel receive treatment at different times. We show a series of problems with conventional regression-based two-way fixed effects estimators, both static and dynamic. These problems arise when researchers conflate the identifying assumptions of parallel trends and no anticipatory effects, implicit assumptions that restrict treatment effect heterogeneity, and the specification of the estimand as a weighted average of treatment effects. We then derive the efficient estimator robust to treatment effect heterogeneity for this setting, show that it has a particularly intuitive imputation form when treatment-effect heterogeneity is unrestricted, characterize its asymptotic behavior, provide tools for inference, and illustrate its attractive properties in simulations. We further discuss appropriate tests for parallel trends, and show how our estimation approach extends to many settings beyond standard event studies." @default.
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• W3197062812 date "2021-08-27" @default.
• W3197062812 modified "2023-09-23" @default.
• W3197062812 title "Revisiting Event Study Designs: Robust and Efficient Estimation" @default.
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