方法对比
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| 面板数据工具变量 (Panel IV / 2SLS)× | 因果推断的工具变量（IV）方法× | |
|---|---|---|
| 领域≠ | 因果推断 | 卫生经济学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 1978-1991 | 1990s (modern applications) |
| 提出者≠ | Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 类型≠ | Causal inference / panel regression | Method |
| 开创性文献≠ | 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 ↗ |
| 别名 | Panel IV, Panel 2SLS, Within-IV, Fixed-Effects IV | IV, two-stage least squares, TSLS, causal estimation |
| 相关≠ | 4 | 3 |
| 摘要≠ | 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. |
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