方法对比
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| 面板数据的混合普通最小二乘法× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010 | 2019 |
| 提出者≠ | Jeffrey Wooldridge (treatment) | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Linear regression on stacked panel observations | Linear regression |
| 开创性文献≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0-262-23258-8 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | Pooled OLS, Pooled Ordinary Least Squares, Simple Panel OLS, Havuzlanmış EKK | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 2 | 5 |
| 摘要≠ | Pooled OLS applies standard ordinary least squares to panel data by stacking all cross-sectional and time observations into a single dataset and ignoring the panel structure during estimation. It is the most transparent starting point for panel data analysis, widely used in economics, finance, and social sciences when researchers wish to estimate average partial effects across individuals and time periods without imposing strong distributional assumptions about unobserved heterogeneity. | 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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