方法对比
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| 稳健工具变量估计× | 面板数据工具变量 (Panel IV / 2SLS)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1949–2019 | 1978-1991 |
| 提出者≠ | Anderson & Rubin (1949); Stock, Wright & Yogo (2002); Andrews, Stock & Sun (2019) | Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991) |
| 类型≠ | Causal inference / robust estimation | Causal inference / panel regression |
| 开创性文献≠ | Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518-529. DOI ↗ | 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 | Robust IV, Weak-instrument-robust IV, Robust 2SLS, Weak-instrument-robust inference | Panel IV, Panel 2SLS, Within-IV, Fixed-Effects IV |
| 相关 | 4 | 4 |
| 摘要≠ | Robust Instrumental Variables estimation extends standard IV and two-stage least squares (2SLS) by guarding against weak-instrument bias and non-standard inference. Methods such as the Anderson-Rubin test, Limited Information Maximum Likelihood (LIML), and the Conditional Likelihood Ratio test provide valid confidence sets and hypothesis tests even when instruments are weak or only partially identified, making IV inference reliable in settings where standard 2SLS breaks down. | 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. |
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