方法对比
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| 全局空间误差模型 (SEM)× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 空间分析 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1988 | 2019 |
| 提出者≠ | Luc Anselin | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Spatial regression model | Linear regression |
| 开创性文献≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | SEM, spatial error model, spatial error regression, global SEM | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关 | 5 | 5 |
| 摘要≠ | The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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