Comparar métodos
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| Modelo de efectos aleatorios robusto× | Mínimos Cuadrados Generalizados para Datos de Panel (Panel GLS)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1980s–2000s | 1935 / developed for panels 1980s–1990s |
| Autor original≠ | Wooldridge; White (sandwich covariance); Arellano | Aitken (1935); extended to panel data by Baltagi and others |
| Tipo≠ | Panel GLS estimator with robust inference | Generalized linear regression |
| Fuente seminal | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Alias | robust RE model, sandwich random effects estimator, cluster-robust random effects, GLS-robust RE | Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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