Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастное панельное исследование событий× | 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 (2021); Freyaldenhoven, Hansen, Shapiro & Weidner (2021) | Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation |
| Тип≠ | Quasi-experimental / causal inference | 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 | robust event-study estimator, heteroskedasticity-robust panel event study, staggered-robust event study, robust ES design | event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model |
| Связанные | 4 | 4 |
| Сводка≠ | A robust panel event study extends the standard panel event study design by applying heteroskedasticity- and autocorrelation-robust (HAC) standard errors and, where staggered treatment adoption exists, interaction-weighted estimators that remain valid even when treatment effects are heterogeneous across cohorts and time periods. It is widely used in economics, finance, and policy research to trace the dynamic causal path of an intervention. | 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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