Comparar métodos

	Estimador de Mínimos Cuadrados Generalizados (GMM) de Arellano-Bond	Método de Variables Instrumentales (VI) para Inferencia Causal
Campo	Econometría	Economía de la salud
Familia	Regression model	Process / pipeline
Año de origen	1991	1990s (modern applications)
Autor original	Manuel Arellano and Stephen Bond	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	GMM estimator for dynamic panel data	Method
Fuente seminal	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.
Alias	AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator	IV, two-stage least squares, TSLS, causal estimation
Relacionados	5	3
Resumen	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.