Salīdzināt metodes

	Instrumentālo mainīgo (IV) metode cēloņsakarību noteikšanai	System GMM (Arellano-Bover / Blundell-Bond)
Nozare	Veselības ekonomika	Ekonometrija
Saime	Process / pipeline	Regression model
Izcelsmes gads	1990s (modern applications)	1998
Autors	Angrist & Pischke (applied econometrics); rooted in econometric theory	Arellano & Bover (1995); Blundell & Bond (1998)
Tips	Method	Dynamic panel data estimator
Pirmavots	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.
Citi nosaukumi	IV, two-stage least squares, TSLS, causal estimation	Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond)
Saistītās	3	4
Kopsavilkums	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.