方法对比
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| 空间面板数据模型（固定效应/随机效应）× | 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≠ | 2014 | 2019 |
| 提出者≠ | Elhorst; Lee & Yu | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Spatial econometric panel model | Linear regression |
| 开创性文献≠ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | spatial panel FE/RE, spatial econometric panel, spatial lag/error panel, Uzamsal Panel Modeli (Spatial Panel FE/RE) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 4 | 5 |
| 摘要≠ | The spatial panel model is a family of econometric models that adds spatial dependence to panel data (units observed over time). It combines fixed- or random-effects panel structure with spatial lag, spatial error, or spatial Durbin components, and is developed in the modern spatial-econometrics literature by Elhorst (2014) and Lee & Yu (2010). | 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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