Salīdzināt metodes

	Dinamiskās instrumentālās mainīgās (Dinamiskais panelis IV / Arellano-Bond)	Instrumentālo mainīgo (IV) metode cēloņsakarību noteikšanai
Nozare	Cēloņsakarību secināšana	Veselības ekonomika
Saime	Regression model	Process / pipeline
Izcelsmes gads	1991	1990s (modern applications)
Autors	Arellano & Bond (1991); extended by Blundell & Bond (1998)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tips	Dynamic panel causal estimation	Method
Pirmavots	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.
Citi nosaukumi	Dynamic IV, Dynamic Panel IV, Arellano-Bond GMM, System GMM	IV, two-stage least squares, TSLS, causal estimation
Saistītās	5	3
Kopsavilkums	Dynamic Instrumental Variables estimation addresses endogeneity in panel models where the outcome depends on its own past values. By first-differencing to remove unit fixed effects and then using lagged levels as instruments for the differenced lagged outcome, it produces consistent causal estimates even when standard OLS or fixed-effects are biased by dynamic feedback.	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.