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| Multi-period Difference-in-Differences (Staggered DiD)× | 動的差分の差分法× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年 | 2021 | 2021 |
| 提唱者≠ | Callaway & Sant'Anna; Goodman-Bacon | Callaway & Sant'Anna; Sun & Abraham |
| 種類≠ | Causal inference / panel regression | Causal inference / quasi-experimental |
| 原典 | 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 ↗ |
| 別名 | staggered DiD, multi-period DiD, staggered difference-in-differences, heterogeneous timing DiD | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| 関連≠ | 5 | 4 |
| 概要≠ | 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. | Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time. |
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