Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Оцінювач загальних корельованих ефектів групи середніх (CCEMG)× | Оцінювач динамічних звичайних найменших квадратів (DOLS)× | Регресія звичайно найменших квадратів (ЗНК)× | |
|---|---|---|---|
| Галузь | Економетрика | Економетрика | Економетрика |
| Родина | Regression model | Regression model | Regression model |
| Рік появи≠ | 2006 | 1993 | 2019 |
| Автор методу≠ | M. Hashem Pesaran | Stock & Watson (1993); panel extension Kao & Chiang (2001) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Heterogeneous panel estimator | Cointegrating regression estimator | Linear regression |
| Основоположне джерело≠ | 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 |
| Інші назви≠ | 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 |
| Пов'язані≠ | 4 | 5 | 5 |
| Підсумок≠ | 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). |
| ScholarGateНабір даних ↗ |
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