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DC Field | Value | Language |
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dc.creator | Baker, Andrew C. | - |
dc.creator | Larcker, David F. | - |
dc.creator | Wang, Charles | - |
dc.date | 2023-03-17T11:24:00Z | - |
dc.date | 2022-05 | - |
dc.date | 2023-03-17T11:24:00Z | - |
dc.date.accessioned | 2023-04-10T07:26:56Z | - |
dc.date.available | 2023-04-10T07:26:56Z | - |
dc.identifier | Baker, 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.identifier | 0304-405X | - |
dc.identifier | https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37374628 | - |
dc.identifier | 10.1016/j.jfineco.2022.01.004 | - |
dc.identifier.uri | http://lib.yhn.edu.vn/handle/YHN/238 | - |
dc.description | 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. | - |
dc.description | Accepted Manuscript | - |
dc.format | application/pdf | - |
dc.language | en_US | - |
dc.publisher | Elsevier BV | - |
dc.relation | https://doi.org/10.1016/j.jfineco.2022.01.004 | - |
dc.relation | Journal of Financial Economics | - |
dc.subject | Strategy and Management | - |
dc.subject | Economics and Econometrics | - |
dc.subject | Finance | - |
dc.subject | Accounting | - |
dc.title | How Much Should We Trust Staggered Difference-in-Differences Estimates? | - |
dc.type | Journal Article | - |
Appears in Collections | Tài liệu ngoại văn |
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