Comparer des méthodes

	Modèle à effets aléatoires pour données de panel	Méthode des variables instrumentales (VI) pour l'inférence causale
Domaine	Économétrie	Économie de la santé
Famille	Regression model	Process / pipeline
Année d'origine	2021	1990s (modern applications)
Auteur d'origine	Baltagi (textbook treatment); classical random-effects panel estimator	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Panel data regression	Method
Source fondatrice	Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli	IV, two-stage least squares, TSLS, causal estimation
Apparentées	5	3
Résumé	The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Analysis of Panel Data (2021).	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.