方法对比
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| 贝叶斯固定效应模型× | 贝叶斯随机效应模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2000–2008 | 1972–1995 |
| 提出者≠ | Chib (2008); Lancaster (2000) | Lindley & Smith (1972); extended by Gelman, Rubin and colleagues |
| 类型≠ | Bayesian panel regression | Bayesian hierarchical panel model |
| 开创性文献≠ | Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391–413. DOI ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| 别名 | Bayesian within estimator, Bayesian FE model, Bayesian individual fixed effects, Bayesian least squares dummy variable | Bayesian hierarchical model, Bayesian mixed effects model, Bayesian multilevel model, BREM |
| 相关 | 5 | 5 |
| 摘要≠ | The Bayesian fixed effects model applies Bayesian inference to the classical within-group panel estimator. Unit-specific intercepts capture time-invariant unobserved heterogeneity, while prior distributions on all parameters allow probability statements about coefficients and full uncertainty quantification via the posterior distribution. | The Bayesian random effects model combines panel-data random effects with a Bayesian prior framework, allowing unit-specific effects to be treated as draws from a population distribution whose hyperparameters are estimated from the data. This produces regularised, uncertainty-quantified estimates that borrow strength across units — particularly valuable for short panels, sparse groups, or settings where frequentist variance-component estimation is unstable. |
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