Compară metode

	Model cu Efecte Aleatorii pentru Date Panel	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	1978	2019	2014
Autorul original	Baltagi (textbook treatment); Hausman specification test	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Tip	Panel data regression	Linear regression	Panel data regression
Sursa seminală	Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271.	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	random effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler Modeli	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	5	5	5
Rezumat	The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and it is developed in standard treatments such as Baltagi's Econometric Analysis of Panel Data.	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).