Salīdzināt metodes

	DOLS (Dynamic Ordinary Least Squares) novērtēšanas rīks	Augmented Mean Group (AMG) novērtētājs	Kopējo saistīto efektu vidējās grupas (CCEMG) novērtētājs
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1993	2010	2006
Autors	Stock & Watson (1993); panel extension Kao & Chiang (2001)	Eberhardt & Teal; Bond & Eberhardt	M. Hashem Pesaran
Tips	Cointegrating regression estimator	Heterogeneous panel data estimator	Heterogeneous panel estimator
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	4	4
Kopsavilkums	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.