Compară metode

	Estimatorul Common Correlated Effects Mean Group (CCEMG)	Regresia prin metoda celor mai mici pătrate ordinare (OLS)	Modelul cu Efecte Fixe pentru Date Panou
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	2006	2019	2014
Autorul original	M. Hashem Pesaran	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Tip	Heterogeneous panel estimator	Linear regression	Panel data regression
Sursa seminală	Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Denumiri alternative	common correlated effects, CCE, CCEMG, Pesaran CCE estimator	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Înrudite	4	5	5
Rezumat	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.	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).	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).