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How much should we trust staggered difference-in-differences estimates? *
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DOI：10.1016/j.jfineco.2022.01.004.png)
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摘要
En 中文
We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that rely on staggered treatment timing, and can result in Type-I and Type-II errors. We summarize three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs. We apply these estimators to re-examine prior published results and show, in many cases, the alternative causal estimates or inferences differ substantially from prior papers. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Keyword:
Difference in differences
Staggered difference-in-differences
Generalized difference-in-differences
Dynamic treatment effects
Treatment effect heterogeneity
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