Compară metode

	Estimatorul „Between” pentru Date Panel	Modelul cu Efecte Fixe pentru Date Panou	Ordinary Least Squares Agregat (Pooled OLS) pentru Date Paneli
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	2008	2014	2010
Autorul original	Badi Baltagi (treatment)	Hsiao (textbook treatment); within transformation of panel data	Jeffrey Wooldridge (treatment)
Tip	OLS on group means	Panel data regression	Linear regression on stacked panel observations
Sursa seminală	Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). John Wiley & Sons. ISBN: 978-0-470-51886-1	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0-262-23258-8
Denumiri alternative	Between-Groups Estimator, Cross-Sectional Averages Estimator, Panel Between Estimator, Gruplar-Arası Tahmin Edici	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	Pooled OLS, Pooled Ordinary Least Squares, Simple Panel OLS, Havuzlanmış EKK
Înrudite	2	5	2
Rezumat	The Between Estimator is a panel data regression technique that identifies regression coefficients exclusively from cross-sectional variation across individuals, by collapsing the panel to individual-specific time-averaged observations and applying ordinary least squares to those group means. It is used in economics, sociology, and political science when researchers are interested in long-run or structural differences between units rather than short-run within-unit dynamics.	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).	Pooled OLS applies standard ordinary least squares to panel data by stacking all cross-sectional and time observations into a single dataset and ignoring the panel structure during estimation. It is the most transparent starting point for panel data analysis, widely used in economics, finance, and social sciences when researchers wish to estimate average partial effects across individuals and time periods without imposing strong distributional assumptions about unobserved heterogeneity.