השוואת שיטות

	אומד ה-Common Correlated Effects Mean Group (CCEMG)	אומד קבוצת הממוצעים המוגברת (AMG)	רגרסיית ריבועים פחותים רגילים (OLS)
תחום	אקונומטריקה	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model	Regression model
שנת המקור	2006	2010	2019
הוגה השיטה	M. Hashem Pesaran	Eberhardt & Teal; Bond & Eberhardt	Wooldridge (textbook treatment); classical least squares
סוג	Heterogeneous panel estimator	Heterogeneous panel data estimator	Linear regression
מקור מכונן	Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.	Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
כינויים	common correlated effects, CCE, CCEMG, Pesaran CCE estimator	AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
קשורות	4	4	5
תקציר	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.	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).