Comparar métodos

	Estimador de Variáveis Instrumentais de Anderson-Hsiao	Método de Variáveis Instrumentais (VI) para Inferência Causal	GMM em Sistema (Arellano-Bover / Blundell-Bond)
Área	Econometria	Economia da saúde	Econometria
Família	Regression model	Process / pipeline	Regression model
Ano de origem	1981	1990s (modern applications)	1998
Autor original	Theodore Anderson & Cheng Hsiao	Angrist & Pischke (applied econometrics); rooted in econometric theory	Arellano & Bover (1995); Blundell & Bond (1998)
Tipo	Instrumental variables estimator for dynamic panel data	Method	Dynamic panel data estimator
Fonte seminal	Anderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375), 598–606.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	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.
Outros nomes	Anderson-Hsiao Estimator, AH IV Estimator, Dynamic Panel IV Estimator, Anderson-Hsiao Araçsal Değişken Tahmincisi	IV, two-stage least squares, TSLS, causal estimation	Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond)
Relacionados	2	3	4
Resumo	The Anderson-Hsiao IV estimator is a method for consistently estimating dynamic panel data models that include a lagged dependent variable as a regressor. Proposed by Theodore Anderson and Cheng Hsiao in 1981, it resolves the Nickell bias that arises when fixed effects are eliminated by first-differencing, by instrumenting the differenced lagged dependent variable with its own second lag in levels or differences.	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.	System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small.