Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Mínimos Cuadrados Generalizados para Datos de Panel (Panel GLS)× | Modelo de efectos aleatorios para datos de panel× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1935 / developed for panels 1980s–1990s | 1966 |
| Autor original≠ | Aitken (1935); extended to panel data by Baltagi and others | Balestra & Nerlove |
| Tipo≠ | Generalized linear regression | Panel data estimator |
| Fuente seminal≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | Balestra, P., & Nerlove, M. (1966). Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34(3), 585–612. DOI ↗ |
| Alias | Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel | random effects estimator, RE model, GLS random effects, error components model |
| Relacionados≠ | 3 | 5 |
| Resumen≠ | 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. | The panel random effects (RE) model treats individual-specific effects as random draws from a population distribution rather than fixed constants, enabling efficient estimation by generalised least squares and allowing inference about time-invariant regressors that are swept away in fixed effects estimation. |
| ScholarGateConjunto de datos ↗ |
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