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| Differenza nelle Differenze per Effetti Trattamento Eterogenei (HTE-DiD)× | Dynamic Difference-in-Differences× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine | 2021 | 2021 |
| Ideatore | Callaway & Sant'Anna; Sun & Abraham | Callaway & Sant'Anna; Sun & Abraham |
| Tipo≠ | Causal inference / panel regression | 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 | HTE-DiD, heterogeneous DiD, CATT estimator, group-time ATT | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Correlati | 4 | 4 |
| Sintesi≠ | HTE-DiD extends the classic Difference-in-Differences estimator to settings where treatment effects vary across units, time periods, or treatment cohorts. Developed formally by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it avoids the biases that arise when a conventional two-way fixed-effects regression is used with staggered adoption or effect heterogeneity, by estimating cohort-and-time-specific average treatment effects that can then be aggregated flexibly. | 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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