方法对比
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| 因果推断的工具变量（IV）方法× | 系统GMM（Arellano-Bover / Blundell-Bond）× | |
|---|---|---|
| 领域≠ | 卫生经济学 | 计量经济学 |
| 方法族≠ | Process / pipeline | Regression model |
| 起源年份≠ | 1990s (modern applications) | 1998 |
| 提出者≠ | Angrist & Pischke (applied econometrics); rooted in econometric theory | Arellano & Bover (1995); Blundell & Bond (1998) |
| 类型≠ | Method | Dynamic panel data estimator |
| 开创性文献≠ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ | 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 ↗ |
| 别名 | IV, two-stage least squares, TSLS, causal estimation | Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond) |
| 相关≠ | 3 | 4 |
| 摘要≠ | 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. | System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small. |
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