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| Ước lượng Trung bình Nhóm Ảnh hưởng Tương quan Chung (CCEMG)× | Ước lượng Bình phương Tối thiểu Thông thường Động (DOLS)× | Hồi quy Bình phương Tối thiểu Thông thường (OLS)× | |
|---|---|---|---|
| Lĩnh vực | Kinh tế lượng | Kinh tế lượng | Kinh tế lượng |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 2006 | 1993 | 2019 |
| Người khởi xướng≠ | M. Hashem Pesaran | Stock & Watson (1993); panel extension Kao & Chiang (2001) | Wooldridge (textbook treatment); classical least squares |
| Loại≠ | Heterogeneous panel estimator | Cointegrating regression estimator | Linear 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 ↗ | Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Tên gọi khác≠ | common correlated effects, CCE, CCEMG, Pesaran CCE estimator | DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 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. | Dynamic OLS is a cointegrating-regression estimator introduced by Stock and Watson (1993) that recovers the long-run relationship between I(1) variables. It augments the static regression with leads and lags of the differenced regressors, correcting endogeneity bias parametrically so that the long-run coefficient can be estimated by ordinary least squares. | 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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