เปรียบเทียบวิธี

	Synthetic Difference-in-Differences	ผลกระทบแบบตรึงเชิงโต้ตอบ
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2021	2009
ผู้ริเริ่ม	Arkhangelsky, Athey, Hirshberg, Imbens, and Wager	Jushan Bai
ประเภท	Treatment-effect estimation	Panel with latent structure
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Synthetic DID, SDID	Factor models with individual heterogeneity
ที่เกี่ยวข้อง	3	3
สรุป	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.