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| Studio di evento su dati panel dinamici× | Dynamic Difference-in-Differences× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine | 2021 | 2021 |
| Ideatore≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Callaway & Sant'Anna; Sun & Abraham |
| Tipo≠ | Quasi-experimental / causal inference | Causal inference / quasi-experimental |
| Fonte seminale | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Alias | dynamic event study, panel event-study regression, leads-and-lags event study, event-time panel design | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Correlati | 4 | 4 |
| Sintesi≠ | 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. | Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time. |
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