Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель робастных случайных эффектов× | Модель случайных эффектов для панельных данных× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1980s–2000s | 1966 |
| Автор метода≠ | Wooldridge; White (sandwich covariance); Arellano | Balestra & Nerlove |
| Тип≠ | Panel GLS estimator with robust inference | Panel data estimator |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | robust RE model, sandwich random effects estimator, cluster-robust random effects, GLS-robust RE | random effects estimator, RE model, GLS random effects, error components model |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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