手法を比較

	パネルデータランダム効果モデル	因果推論のための操作変数（IV）法
分野	計量経済学	医療経済学
系統	Regression model	Process / pipeline
提唱年	2021	1990s (modern applications)
提唱者	Baltagi (textbook treatment); classical random-effects panel estimator	Angrist & Pischke (applied econometrics); rooted in econometric theory
種類	Panel data regression	Method
原典	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.
別名	random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli	IV, two-stage least squares, TSLS, causal estimation
関連	5	3
概要	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.