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| 机器学习增强面板事件研究× | 面板数据固定效应模型× | |
|---|---|---|
| 领域≠ | 因果推断 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2019-2021 | 2014 |
| 提出者≠ | Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments) | Hsiao (textbook treatment); within transformation of panel data |
| 类型≠ | Causal inference / quasi-experimental | Panel data regression |
| 开创性文献≠ | Chernozhukov, V., Wuthrich, K., & Zhu, Y. (2021). An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls. Journal of the American Statistical Association, 116(536), 1849-1864. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| 别名 | ML-augmented event study, ML event study, panel event study with ML, machine learning event study | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| 相关≠ | 3 | 5 |
| 摘要≠ | The machine learning-augmented panel event study extends the classical panel event study by replacing or augmenting parametric counterfactual models with machine learning estimators — such as LASSO, random forests, or matrix completion — to construct more accurate pre-event baselines, detect violations of parallel trends, and produce valid causal effect estimates across multiple post-event periods. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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