Módszerek összehasonlítása

	Regressziós diszkontinuitási dizájn (RDD)	Instrumentális Változók (IV) Módszer Kauzális Infláció Becslésére	Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel	Paneladatok rögzített hatású modellje
Tudományterület	Ökonometria	Egészség-gazdaságtan	Ökonometria	Ökonometria
Módszercsalád	Regression model	Process / pipeline	Regression model	Regression model
Keletkezés éve	2008	1990s (modern applications)	2019	2014
Megalkotó	Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik	Angrist & Pischke (applied econometrics); rooted in econometric theory	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Típus	Quasi-experimental causal design	Method	Linear regression	Panel data regression
Alapmű	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Alternatív nevek	RDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD)	IV, two-stage least squares, TSLS, causal estimation	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Kapcsolódó	5	3	5	5
Összefoglaló	Regression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the design developed for applied practice by Imbens and Lemieux (2008) and by Lee and Lemieux (2010).	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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).