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| Heterogenerer Panel-Ereignisstudien-Effekt bei heterogenen Behandlungseffekten× | Dynamische Differenz-von-Differenzen× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr | 2021 | 2021 |
| Urheber≠ | Sun & Abraham; Callaway & Sant'Anna | Callaway & Sant'Anna; Sun & Abraham |
| Typ | Causal inference / quasi-experimental | Causal inference / quasi-experimental |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen | HTE panel event study, heterogeneous effects event study, staggered panel event study, CATT event study | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | A heterogeneous treatment effect panel event study estimates how treatment impacts vary across units and over time in a panel setting, allowing each cohort of treated units to have its own dynamic response. Seminal contributions by Sun and Abraham (2021) and Callaway and Sant'Anna (2021) showed that standard two-way fixed-effects event studies mask sign-reversing treatment heterogeneity across cohorts, motivating cohort-specific estimation followed by flexible aggregation. | 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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