Compară metode

	Estimatorul DOLS (Dynamic Ordinary Least Squares)	Estimatorul Augmented Mean Group (AMG)	Estimatorul Common Correlated Effects Mean Group (CCEMG)
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	1993	2010	2006
Autorul original	Stock & Watson (1993); panel extension Kao & Chiang (2001)	Eberhardt & Teal; Bond & Eberhardt	M. Hashem Pesaran
Tip	Cointegrating regression estimator	Heterogeneous panel data estimator	Heterogeneous panel estimator
Sursa seminală	Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.	Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford.	Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.
Denumiri alternative	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
Înrudite	5	4	4
Rezumat	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.