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| Synthetische Difference-in-Differences× | Interaktive feste Effekte× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2021 | 2009 |
| Urheber≠ | Arkhangelsky, Athey, Hirshberg, Imbens, and Wager | Jushan Bai |
| Typ≠ | Treatment-effect estimation | Panel with latent structure |
| Wegweisende Quelle≠ | Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118. DOI ↗ | Bai, J. (2009). Panel data models with interactive fixed effects. Econometric Reviews, 28(4), 289-312. link ↗ |
| Aliasnamen≠ | Synthetic DID, SDID | Factor models with individual heterogeneity |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | Synthetic Difference-in-Differences (SDID) combines synthetic control and difference-in-differences approaches to estimate treatment effects when a policy or intervention affects one unit (country, firm) at a point in time. Introduced by Arkhangelsky et al. (2021), it improves upon both methods alone by using weighted combinations of controls to match treated units' pre-treatment trends and levels. This yields more precise and robust estimates than classical DiD or synthetic control. | Interactive Fixed Effects (IFE) extends standard fixed-effects panel models by allowing unit-specific intercepts to vary not just at the individual level but also with unobserved common time-varying factors. Introduced by Bai (2009), it models heterogeneity as the interaction of individual characteristics and common shocks, ideal for studying cross-sectional variation in how units respond to macro conditions. This framework dominates when common factors drive substantial heterogeneity. |
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