Võrdle meetodeid

	Arellano-Bongi GMM-hinnang	Instrumentaalmuutujate (IV) meetod kausaalse järelduse tegemiseks
Valdkond	Ökonomeetria	Terviseökonoomika
Perekond	Regression model	Process / pipeline
Tekkeaasta	1991	1990s (modern applications)
Looja	Manuel Arellano and Stephen Bond	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tüüp	GMM estimator for dynamic panel data	Method
Algallikas	Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Rööpnimetused	AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator	IV, two-stage least squares, TSLS, causal estimation
Seotud	5	3
Kokkuvõte	The Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous.	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.