Uporedite metode

	Estimator zajedničkih koreliranih efekata – grupni prosek (CCEMG)	Динамички Обични Најмањи Квадрати (DOLS) Естиматор	Regresija običnih najmanjih kvadrata (OLS)
Oblast	Ekonometrija	Ekonometrija	Ekonometrija
Porodica	Regression model	Regression model	Regression model
Godina nastanka	2006	1993	2019
Tvorac	M. Hashem Pesaran	Stock & Watson (1993); panel extension Kao & Chiang (2001)	Wooldridge (textbook treatment); classical least squares
Tip	Heterogeneous panel estimator	Cointegrating regression estimator	Linear regression
Temeljni izvor	Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.	Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Drugi nazivi	common correlated effects, CCE, CCEMG, Pesaran CCE estimator	DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Srodne	4	5	5
Sažetak	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.	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.	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).