Módszerek összehasonlítása

	CCEMG (Common Correlated Effects Mean Group) becslő	Augmented Mean Group (AMG) Estimátor	Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel	Paneladatok rögzített hatású modellje
Tudományterület	Ökonometria	Ökonometria	Ökonometria	Ökonometria
Módszercsalád	Regression model	Regression model	Regression model	Regression model
Keletkezés éve	2006	2010	2019	2014
Megalkotó	M. Hashem Pesaran	Eberhardt & Teal; Bond & Eberhardt	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Típus	Heterogeneous panel estimator	Heterogeneous panel data estimator	Linear regression	Panel data regression
Alapmű	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	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Alternatív nevek	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	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Kapcsolódó	4	4	5	5
Összefoglaló	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).	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).