方法对比
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| 贝叶斯普通最小二乘回归 (Bayesian OLS)× | 贝叶斯固定效应模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1971 | 2000–2008 |
| 提出者≠ | Arnold Zellner | Chib (2008); Lancaster (2000) |
| 类型≠ | Bayesian linear regression | Bayesian panel regression |
| 开创性文献≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 | Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391–413. DOI ↗ |
| 别名 | Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares | Bayesian within estimator, Bayesian FE model, Bayesian individual fixed effects, Bayesian least squares dummy variable |
| 相关 | 5 | 5 |
| 摘要≠ | Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior knowledge is available or when samples are small. | 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. |
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