Сравнение на методи

	Синтетичен метод на разликите в разликите	Географска регресионна дисконтинуитетност
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2021	2010
Създател	Arkhangelsky, Athey, Hirshberg, Imbens, and Wager	Melissa Dell and colleagues
Тип	Treatment-effect estimation	Spatial quasi-experiment
Основополагащ източник	Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118.	Dell, M. (2018). The persistent effects of Peru's mining mita. Econometrica, 78(6), 1863-1911.
Други названия	Synthetic DID, SDID	Spatial RD, Geographic RDD
Свързани	3	3
Резюме	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.	Geographic Regression Discontinuity (GRD) is a quasi-experimental design that exploits sharp geographic boundaries—borders, policy boundaries, or natural features—to estimate causal effects. Introduced by Dell (2010) and others, it compares outcomes on either side of a boundary where treatment changes abruptly, leveraging the idea that units on opposite sides of a border are otherwise similar. This approach yields credible causal estimates for spatially localized policies, institutional changes, and natural phenomena.