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| Ước lượng Trung bình Nhóm Ảnh hưởng Tương quan Chung (CCEMG)× | Hồi quy Bình phương Tối thiểu Thông thường (OLS)× | Mô hình Hiệu ứng Cố định Dữ liệu Bảng× | |
|---|---|---|---|
| Lĩnh vực | Kinh tế lượng | Kinh tế lượng | Kinh tế lượng |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 2006 | 2019 | 2014 |
| Người khởi xướng≠ | M. Hashem Pesaran | Wooldridge (textbook treatment); classical least squares | Hsiao (textbook treatment); within transformation of panel data |
| Loại≠ | Heterogeneous panel estimator | Linear regression | Panel data regression |
| Công trình gốc≠ | 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 | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Tên gọi khác≠ | common correlated effects, CCE, CCEMG, Pesaran CCE estimator | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Liên quan≠ | 4 | 5 | 5 |
| Tóm tắt≠ | 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). | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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