Thông tin tài liệu
| Nhan đề : | How Much Should We Trust Staggered Difference-in-Differences Estimates? |
| Nhà xuất bản : | Elsevier BV |
| Mô tả: | 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. Accepted Manuscript |
| URI: | http://lib.yhn.edu.vn/handle/YHN/238 |
| Định danh khác : | 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. 0304-405X https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37374628 10.1016/j.jfineco.2022.01.004 |
| Bộ sưu tập | Tài liệu ngoại văn |
XEM MÔ TẢ
27
XEM & TẢI
14
Danh sách tệp tin đính kèm: