So sánh phương pháp

	Synthetic Difference-in-Differences	Fixed Effects Tương tác
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	2021	2009
Người khởi xướng	Arkhangelsky, Athey, Hirshberg, Imbens, and Wager	Jushan Bai
Loại	Treatment-effect estimation	Panel with latent structure
Công trình gốc	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.
Tên gọi khác	Synthetic DID, SDID	Factor models with individual heterogeneity
Liên quan	3	3
Tóm tắt	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.