เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Robust Difference-in-Differences× | Multi-period Difference-in-Differences (Staggered DiD)× | |
|---|---|---|
| สาขาวิชา | การอนุมานเชิงสาเหตุ | การอนุมานเชิงสาเหตุ |
| ตระกูล | Regression model | Regression model |
| ปีกำเนิด≠ | 2021-2023 | 2021 |
| ผู้ริเริ่ม≠ | Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023) | Callaway & Sant'Anna; Goodman-Bacon |
| ประเภท | Causal inference / panel regression | Causal inference / panel regression |
| แหล่งต้นตำรับ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| ชื่อเรียกอื่น | robust DiD, heterogeneity-robust DiD, staggered DiD, disaggregated ATT DiD | staggered DiD, multi-period DiD, staggered difference-in-differences, heterogeneous timing DiD |
| ที่เกี่ยวข้อง | 5 | 5 |
| สรุป≠ | Robust Difference-in-Differences is a family of modern DiD estimators designed to remain valid when treatment timing is staggered across units and treatment effects are heterogeneous over time or across groups. Classical two-way fixed-effects (TWFE) DiD can be severely biased in such settings; robust variants estimate group-time average treatment effects (ATTs) separately and then aggregate them in a theoretically sound way. | Multi-period Difference-in-Differences extends the classic two-period DiD framework to settings where units adopt treatment at different points in time. Formalised by Callaway and Sant'Anna (2021) and Goodman-Bacon (2021), it decomposes the overall treatment effect into group-time average treatment effects and addresses the bias that arises when conventional two-way fixed-effects regressions are applied to staggered adoption designs. |
| ScholarGateชุดข้อมูล ↗ |
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