Uporedite metode

	Динамички Обични Најмањи Квадрати (DOLS) Естиматор	Augmented Mean Group (AMG) estimator	Estimator zajedničkih koreliranih efekata – grupni prosek (CCEMG)
Oblast	Ekonometrija	Ekonometrija	Ekonometrija
Porodica	Regression model	Regression model	Regression model
Godina nastanka	1993	2010	2006
Tvorac	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
Temeljni izvor	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.
Drugi nazivi	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
Srodne	5	4	4
Sažetak	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.