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| 动态事件研究设计× | 双重差分法 (Diff-in-Diff)× | |
|---|---|---|
| 领域≠ | 因果推断 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2021 (canonical treatment); practice since 1990s) | 1994 |
| 提出者≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| 类型≠ | Quasi-experimental / causal inference | Causal inference / panel regression |
| 开创性文献≠ | Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 别名≠ | dynamic DiD, lead-lag event study, relative-time event study, event-time regression | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| 相关≠ | 3 | 5 |
| 摘要≠ | The dynamic event study design extends the standard difference-in-differences framework by estimating treatment effects at each period before and after the event, rather than collapsing everything into a single post-treatment coefficient. By plotting lead and lag coefficients against relative event time, researchers can simultaneously test for pre-existing trends and trace how the causal effect evolves over multiple post-treatment periods. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
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