Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель случайных эффектов для панельных данных× | Панельный МНК (объединенный метод наименьших квадратов)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1966 | 1986-2003 |
| Автор метода≠ | Balestra & Nerlove | Classical least squares applied to pooled panels; foundational treatment in Hsiao (2003) and Wooldridge (2010) |
| Тип≠ | Panel data estimator | Linear panel regression |
| Основополагающий источник≠ | 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 ↗ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Другие названия | random effects estimator, RE model, GLS random effects, error components model | pooled OLS, pooled ordinary least squares, panel least squares, POLS |
| Связанные≠ | 5 | 4 |
| Сводка≠ | 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. | Panel OLS — also called Pooled OLS — applies the classical ordinary least squares estimator to panel data by stacking all cross-sectional units and time periods into a single sample. It estimates one common set of slope coefficients under the assumption that the intercept and slopes are homogeneous across units and time. |
| ScholarGateНабор данных ↗ |
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