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。
| 贝叶斯动态面板数据模型× | 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≠ | 2002–2007 | 1972–1995 |
| 提出者≠ | Hsiao, Pesaran, Tahmiscioglu; Arellano & Bonhomme | Lindley & Smith (1972); extended by Gelman, Rubin and colleagues |
| 类型≠ | Bayesian panel model | Bayesian hierarchical panel model |
| 开创性文献≠ | Hsiao, C., Pesaran, M. H., & Tahmiscioglu, A. K. (2002). Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods. Journal of Econometrics, 109(1), 107–150. 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 DPD model, Bayesian lagged dependent variable panel model, Bayesian autoregressive panel model, B-DPD | Bayesian hierarchical model, Bayesian mixed effects model, Bayesian multilevel model, BREM |
| 相关≠ | 6 | 5 |
| 摘要≠ | The Bayesian dynamic panel data model extends standard dynamic panel models — which include a lagged dependent variable to capture state dependence — by estimating all parameters within a Bayesian framework. Prior distributions are combined with the likelihood to yield a full posterior distribution over model parameters, enabling probabilistic inference and coherent uncertainty quantification even in short panels. | 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. |
| ScholarGate数据集 ↗ |
|
|