Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел с произволни ефекти за панелни данни× | Обобщен метод на най-малките квадрати за панелни данни (Panel GLS)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1966 | 1935 / developed for panels 1980s–1990s |
| Създател≠ | Balestra & Nerlove | Aitken (1935); extended to panel data by Baltagi and others |
| Тип≠ | Panel data estimator | Generalized linear regression |
| Основополагащ източник≠ | 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 |
| Други названия | random effects estimator, RE model, GLS random effects, error components model | Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel |
| Свързани≠ | 5 | 3 |
| Резюме≠ | 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 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. |
| ScholarGateНабор от данни ↗ |
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