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Anderson-Hsiao instrumentvariabelskattingsmetode×Instrumentelle variabler (IV) metode for kausal inferens×
FagfeltØkonometriHelseøkonomi
FamilieRegression modelProcess / pipeline
Opprinnelsesår19811990s (modern applications)
OpphavspersonTheodore Anderson & Cheng HsiaoAngrist & Pischke (applied econometrics); rooted in econometric theory
TypeInstrumental variables estimator for dynamic panel dataMethod
Opprinnelig kildeAnderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375), 598–606. DOI ↗Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗
AliasAnderson-Hsiao Estimator, AH IV Estimator, Dynamic Panel IV Estimator, Anderson-Hsiao Araçsal Değişken TahmincisiIV, two-stage least squares, TSLS, causal estimation
Relaterte23
SammendragThe 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.
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ScholarGateSammenlign metoder: Anderson-Hsiao IV · Instrumental Variables in Health Research. Hentet 2026-06-19 fra https://scholargate.app/no/compare