方法对比
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| 共同相关效应均值组 (CCEMG) 估计量× | 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≠ | 2006 | 2019 |
| 提出者≠ | M. Hashem Pesaran | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Heterogeneous panel estimator | Linear regression |
| 开创性文献≠ | Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名≠ | common correlated effects, CCE, CCEMG, Pesaran CCE estimator | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 4 | 5 |
| 摘要≠ | The Common Correlated Effects Mean Group estimator, introduced by Pesaran in 2006, is a heterogeneous panel-data estimator that controls for cross-sectional dependence by approximating unobserved common factors with the cross-section averages of the variables. It remains consistent when the slope coefficients differ across units. | 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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