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dc.creatorBaker, Andrew C.-
dc.creatorLarcker, David F.-
dc.creatorWang, Charles-
dc.date2023-03-17T11:24:00Z-
dc.date2022-05-
dc.date2023-03-17T11:24:00Z-
dc.date.accessioned2023-04-10T07:26:56Z-
dc.date.available2023-04-10T07:26:56Z-
dc.identifierBaker, Andrew C., David F. Larcker, and Charles C.Y. Wang. "How Much Should We Trust Staggered Difference-In-Differences Estimates?" Journal of Financial Economics 144, no. 2 (May 2022): 370–395.-
dc.identifier0304-405X-
dc.identifierhttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37374628-
dc.identifier10.1016/j.jfineco.2022.01.004-
dc.identifier.urihttp://lib.yhn.edu.vn/handle/YHN/238-
dc.descriptionWe 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.-
dc.descriptionAccepted Manuscript-
dc.formatapplication/pdf-
dc.languageen_US-
dc.publisherElsevier BV-
dc.relationhttps://doi.org/10.1016/j.jfineco.2022.01.004-
dc.relationJournal of Financial Economics-
dc.subjectStrategy and Management-
dc.subjectEconomics and Econometrics-
dc.subjectFinance-
dc.subjectAccounting-
dc.titleHow Much Should We Trust Staggered Difference-in-Differences Estimates?-
dc.typeJournal Article-
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