方法对比
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| 动态普通最小二乘法 (DOLS) 估计量× | 增广均值群 (AMG) 估计量× | 共同相关效应均值组 (CCEMG) 估计量× | |
|---|---|---|---|
| 领域 | 计量经济学 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 1993 | 2010 | 2006 |
| 提出者≠ | Stock & Watson (1993); panel extension Kao & Chiang (2001) | Eberhardt & Teal; Bond & Eberhardt | M. Hashem Pesaran |
| 类型≠ | Cointegrating regression estimator | Heterogeneous panel data estimator | Heterogeneous panel estimator |
| 开创性文献≠ | Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820. DOI ↗ | Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford. link ↗ | Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012. DOI ↗ |
| 别名≠ | DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS) | AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG) | common correlated effects, CCE, CCEMG, Pesaran CCE estimator |
| 相关≠ | 5 | 4 | 4 |
| 摘要≠ | 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. | The Augmented Mean Group estimator, developed by Eberhardt and Teal (2010), is a panel data method for estimating heterogeneous slope coefficients in the presence of cross-sectional dependence. It approximates the unobserved common dynamic process driving all units and folds it into unit-by-unit regressions, then averages the results. | 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. |
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