เปรียบเทียบวิธี

	Synthetic Difference-in-Differences	การฉายภาพเฉพาะที่ (Local Projections)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2021	2005
ผู้ริเริ่ม	Arkhangelsky, Athey, Hirshberg, Imbens, and Wager	Oscar Jorda
ประเภท	Treatment-effect estimation	Multi-horizon regression
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.	Jorda, O. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182.
ชื่อเรียกอื่น	Synthetic DID, SDID	LP-IR, Multi-horizon regression
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.	Local Projections (LP) is a semi-parametric method for estimating impulse responses directly via multi-horizon regressions, bypassing VAR-model specification. Introduced by Jorda (2005), it projects outcomes h periods ahead onto current shocks and lags, producing impulse-response functions without assuming a particular lag structure or VAR order. This flexibility has made it the dominant approach in applied macroeconomics for measuring policy effects and shock transmission.