Сравнение на методи

	Метод на синтетичен контрол за оценка на политики	Модел с фиксирани ефекти за панелни данни
Област	Причинно-следствено заключение	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2003-2010	2014
Създател	Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller	Hsiao (textbook treatment); within transformation of panel data
Тип	Causal inference / comparative case study	Panel data regression
Основополагащ източник	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Други названия	Synthetic Control Method, SCM, Synthetic Control, Abadie-Diamond-Hainmueller method	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Свързани	5	5
Резюме	The Synthetic Control Method (SCM) is a causal inference technique for evaluating the effect of a policy or intervention on a single treated unit — such as a region, country, or firm — by constructing a weighted combination of untreated comparison units that closely mirrors the treated unit before the intervention. Introduced by Abadie and Gardeazabal (2003) and formalized by Abadie, Diamond, and Hainmueller (2010), it provides a data-driven, transparent counterfactual for comparative case studies.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).