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| Nonlineares GMM-Differenzenmodell× | Instrumentalvariablen-Methode (IV) zur Kausalinferenz× | |
|---|---|---|
| Fachgebiet≠ | Ökonometrie | Gesundheitsökonomie |
| Familie≠ | Regression model | Process / pipeline |
| Entstehungsjahr≠ | 1991–2010 | 1990s (modern applications) |
| Urheber≠ | Wooldridge; building on Arellano and Bond (1991) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Typ≠ | Nonlinear panel estimator | Method |
| Wegweisende Quelle≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 9780262232586 | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Aliasnamen | nonlinear diff-GMM, nonlinear Arellano-Bond GMM, first-difference nonlinear GMM, NL-GMM | IV, two-stage least squares, TSLS, causal estimation |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | Nonlinear Difference GMM extends the Arellano-Bond difference GMM estimator to models where the structural relationship between the outcome and its predictors is inherently nonlinear. By first-differencing to eliminate individual fixed effects and then applying GMM moment conditions with lagged levels as instruments, it consistently estimates parameters in dynamic panel settings without requiring a linear functional form. | 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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