Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Estimateur par Moindres Carrés Ordinaires Dynamiques (DOLS)× | Estimateur Augmenté du Groupe Moyen (AMG)× | Régression par Moindres Carrés Ordinaires (MCO)× | |
|---|---|---|---|
| Domaine | Économétrie | Économétrie | Économétrie |
| Famille | Regression model | Regression model | Regression model |
| Année d'origine≠ | 1993 | 2010 | 2019 |
| Auteur d'origine≠ | Stock & Watson (1993); panel extension Kao & Chiang (2001) | Eberhardt & Teal; Bond & Eberhardt | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Cointegrating regression estimator | Heterogeneous panel data estimator | Linear regression |
| Source fondatrice≠ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS) | AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Apparentées≠ | 5 | 4 | 5 |
| Résumé≠ | 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. | 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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