Methoden vergelijken

	Instrumentele Variabelen voor Paneeldata (Panel IV / 2SLS)	Instrumentele Variabelen (IV) Methode voor Causale Inferentie
Vakgebied	Causale inferentie	Gezondheidseconomie
Familie	Regression model	Process / pipeline
Jaar van ontstaan	1978-1991	1990s (modern applications)
Grondlegger	Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Causal inference / panel regression	Method
Oorspronkelijke bron	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.
Aliassen	Panel IV, Panel 2SLS, Within-IV, Fixed-Effects IV	IV, two-stage least squares, TSLS, causal estimation
Verwant	4	3
Samenvatting	Panel data instrumental variables combines the bias-correcting power of instrumental variables (IV) with the within-unit variation exploited by panel data methods. It addresses endogeneity — omitted variables, reverse causation, or measurement error — in longitudinal settings where observations are repeated across units and time. Seminal contributions come from Hausman (1978) on specification testing and Arellano and Bond (1991) on GMM-based panel IV.	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.