Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Designul studiului eveniment pentru efecte de tratament eterogene× | Diferențe în Diferențe Dinamice× | |
|---|---|---|
| Domeniu | Inferență cauzală | Inferență cauzală |
| Familie | Regression model | Regression model |
| Anul apariției | 2021 | 2021 |
| Autorul original≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Callaway & Sant'Anna; Sun & Abraham |
| Tip≠ | Quasi-experimental causal inference | Causal inference / quasi-experimental |
| Sursa seminală≠ | Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Denumiri alternative | HTE event study, heterogeneous effects event study, group-time ATT event study, dynamic HTE design | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Înrudite≠ | 3 | 4 |
| Rezumat≠ | Heterogeneous Treatment Effect Event Study Design is a causal-inference framework that uses event study regression to estimate how treatment effects vary across groups, cohorts, or time relative to a treatment event. Unlike classical two-way fixed-effects event studies — which assume a homogeneous effect — this approach explicitly models and recovers group-time average treatment effects (ATTs), addressing the contamination bias that arises when effects differ across treated units. | 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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