قارن الطرق

	الفرق الاصطناعي في الفروق	التأثيرات الثابتة التفاعلية
المجال	الاقتصاد القياسي	الاقتصاد القياسي
العائلة	Regression model	Regression model
سنة النشأة	2021	2009
صاحب الطريقة	Arkhangelsky, Athey, Hirshberg, Imbens, and Wager	Jushan Bai
النوع	Treatment-effect estimation	Panel with latent structure
المصدر التأسيسي	Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118.	Bai, J. (2009). Panel data models with interactive fixed effects. Econometric Reviews, 28(4), 289-312.
الأسماء البديلة	Synthetic DID, SDID	Factor models with individual heterogeneity
ذات صلة	3	3
الملخص	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.