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| Odhadovač Arellano-Bond GMM× | Metoda instrumentálních proměnných (IV) pro kauzální inferenci× | |
|---|---|---|
| Obor≠ | Ekonometrie | Ekonomika zdravotnictví |
| Rodina≠ | Regression model | Process / pipeline |
| Rok vzniku≠ | 1991 | 1990s (modern applications) |
| Tvůrce≠ | Manuel Arellano and Stephen Bond | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Typ≠ | GMM estimator for dynamic panel data | Method |
| Původní zdroj≠ | 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. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Další názvy | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator | IV, two-stage least squares, TSLS, causal estimation |
| Příbuzné≠ | 5 | 3 |
| Shrnutí≠ | The Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous. | 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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