Comparer des méthodes

	Modèle à effets aléatoires robuste	Analyse de données de panel robustes
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1980s–2000s	1987
Auteur d'origine	Wooldridge; White (sandwich covariance); Arellano	Arellano (1987); White (1980) heteroscedasticity-consistent framework
Type	Panel GLS estimator with robust inference	Robust estimation / inference correction
Source fondatrice	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	Arellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics, 49(4), 431–434.
Alias	robust RE model, sandwich random effects estimator, cluster-robust random effects, GLS-robust RE	robust panel regression, cluster-robust panel estimation, panel regression with robust standard errors, HC/CR panel estimator
Apparentées	5	6
Résumé	The Robust Random Effects model estimates panel data relationships using the GLS random effects estimator while replacing the conventional standard errors with sandwich (heteroscedasticity- and cluster-robust) variance estimates. This protects inference against arbitrary within-group correlation and heteroscedasticity without discarding the efficiency gains of random effects when unit-specific effects are genuinely uncorrelated with the regressors.	Robust panel data analysis applies standard panel estimators — fixed effects, random effects, or pooled OLS — while replacing conventional standard errors with cluster-robust or heteroscedasticity-consistent (HC) variants. The point estimates remain unchanged; what changes is the variance-covariance matrix used for inference, making t-tests and F-tests valid even when errors are heteroscedastic or correlated within cross-sectional units over time.