Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| L'étude d'événements sur données de panel dynamiques× | Étude d'événement sur données de panel× | |
|---|---|---|
| Domaine | Inférence causale | Inférence causale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2021 | 1990s–2020s (modern panel formulation) |
| Auteur d'origine≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation |
| Type≠ | Quasi-experimental / causal inference | Quasi-experimental / causal panel design |
| Source fondatrice≠ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. 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 ↗ |
| Alias | dynamic event study, panel event-study regression, leads-and-lags event study, event-time panel design | event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model |
| Apparentées | 4 | 4 |
| Résumé≠ | The dynamic panel event study is a quasi-experimental method that uses panel data to trace out how a treatment effect evolves over time — before and after a defining event — by estimating a flexible regression of leads and lags around the treatment date. It simultaneously tests for pre-existing parallel trends and maps the full dynamic profile of causal impact across multiple post-event periods. | 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. |
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