Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Панельное исследование событий с гетерогенными эффектами воздействия× | Панельное событийно-ориентированное исследование× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2021 | 1990s–2020s (modern panel formulation) |
| Автор метода≠ | Sun & Abraham; Callaway & Sant'Anna | Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation |
| Тип≠ | Causal inference / quasi-experimental | Quasi-experimental / causal panel design |
| Основополагающий источник≠ | Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI ↗ | Freyaldenhoven, S., Hansen, C., Perez-Orive, J., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper 29170. National Bureau of Economic Research. link ↗ |
| Другие названия | HTE panel event study, heterogeneous effects event study, staggered panel event study, CATT event study | event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model |
| Связанные | 4 | 4 |
| Сводка≠ | A heterogeneous treatment effect panel event study estimates how treatment impacts vary across units and over time in a panel setting, allowing each cohort of treated units to have its own dynamic response. Seminal contributions by Sun and Abraham (2021) and Callaway and Sant'Anna (2021) showed that standard two-way fixed-effects event studies mask sign-reversing treatment heterogeneity across cohorts, motivating cohort-specific estimation followed by flexible aggregation. | A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged from untreated units at each point in calendar time relative to their treatment date, making both pre-treatment trend violations and post-treatment effect trajectories immediately visible. |
| ScholarGateНабор данных ↗ |
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