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Modelo Robusto de Efeitos Aleatórios×Mínimos Quadrados Generalizados em Painel (Panel GLS)×
ÁreaEconometriaEconometria
FamíliaRegression modelRegression model
Ano de origem1980s–2000s1935 / developed for panels 1980s–1990s
Autor originalWooldridge; White (sandwich covariance); ArellanoAitken (1935); extended to panel data by Baltagi and others
TipoPanel GLS estimator with robust inferenceGeneralized linear regression
Fonte seminalWooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586
Outros nomesrobust RE model, sandwich random effects estimator, cluster-robust random effects, GLS-robust REPanel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel
Relacionados53
ResumoThe 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.Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified.
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ScholarGateComparar métodos: Robust Random Effects Model · Panel GLS. Recuperado em 2026-06-15 de https://scholargate.app/pt/compare