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。
| 空间回归（空间滞后和空间误差模型）× | 看似无关的回归 (SUR)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1988 | 1962 |
| 提出者≠ | Luc Anselin | Arnold Zellner |
| 类型≠ | Spatial regression (cross-sectional) | System regression (multi-equation) |
| 开创性文献≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. DOI ↗ | Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368. DOI ↗ |
| 别名≠ | spatial econometrics, spatial lag model, spatial error model, SAR / SEM | SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR) |
| 相关 | 5 | 5 |
| 摘要≠ | Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model, where the dependence sits in the error term. | Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS. |
| ScholarGate数据集 ↗ |
|
|