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Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions
April 26, 2024, 4:44 a.m. | Gregory Faletto
stat.ML updates on arXiv.org arxiv.org
Abstract: To address the bias of the canonical two-way fixed effects estimator for difference-in-differences under staggered adoptions, Wooldridge (2021) proposed the extended two-way fixed effects estimator, which adds many parameters. However, this reduces efficiency. Restricting some of these parameters to be equal (for example, subsequent treatment effects within a cohort) helps, but ad hoc restrictions may reintroduce bias. We propose a machine learning estimator with a single tuning parameter, fused extended two-way fixed effects (FETWFE), that …
abstract arxiv bias canonical difference differences econ.em effects efficiency equal estimator example however math.st parameters stat.me stat.ml stat.th treatment type
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