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| Progettazione dinamica di studi di eventi× | Panel Event Study× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2021 (canonical treatment); practice since 1990s) | 1990s–2020s (modern panel formulation) |
| Ideatore≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics | Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation |
| Tipo≠ | Quasi-experimental / causal inference | Quasi-experimental / causal panel design |
| Fonte seminale≠ | 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 ↗ |
| Alias | dynamic DiD, lead-lag event study, relative-time event study, event-time regression | event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model |
| Correlati≠ | 3 | 4 |
| Sintesi≠ | 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. | 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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