方法对比
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| 结构断裂随机效应模型× | 面板数据结构性断点分析× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1998–2000s | 1998-2010 |
| 提出者≠ | Bai & Perron (break detection); Baltagi (panel RE framework) | Bai & Perron (1998); extended to panels by Bai (2010) and Joseph et al. |
| 类型≠ | Panel regression with regime shifts | Panel time-series model with regime shifts |
| 开创性文献≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78. DOI ↗ |
| 别名 | RE model with structural breaks, break-adjusted random effects, random effects break model, panel RE with regime shifts | panel structural break test, break-point panel model, panel change-point analysis, regime-shift panel analysis |
| 相关≠ | 5 | 4 |
| 摘要≠ | The structural break random effects model extends standard panel RE estimation by allowing one or more breakpoints at which slope coefficients or error variances shift across time. It combines structural change detection (e.g., Bai-Perron) with the GLS-based random effects estimator, producing regime-specific parameter estimates while retaining the efficiency gains of pooling individual-level variation as random draws from a common distribution. | Structural break panel data analysis detects and estimates points in time — break dates — where the underlying regression coefficients shift permanently across a panel of cross-sectional units observed over multiple periods. By jointly exploiting cross-sectional and time-series variation, it offers sharper identification of regime shifts than single-series break tests, and it delivers separate coefficient estimates for each regime before and after each break. |
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