方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 随机效应模型 (Random Effects model)× | 因果推断的工具变量（IV）方法× | |
|---|---|---|
| 领域≠ | 计量经济学 | 卫生经济学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2021 | 1990s (modern applications) |
| 提出者≠ | Baltagi (textbook treatment); classical random-effects panel estimator | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 类型≠ | Panel data regression | Method |
| 开创性文献≠ | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| 别名 | random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli | IV, two-stage least squares, TSLS, causal estimation |
| 相关≠ | 5 | 3 |
| 摘要≠ | The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Analysis of Panel Data (2021). | 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. |
| ScholarGate数据集 ↗ |
|
|