Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель случайных эффектов Фурье× | Модель случайных эффектов для панельных данных× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2006-2012 | 1966 |
| Автор метода≠ | Becker, Enders & Lee; Enders & Lee | Balestra & Nerlove |
| Тип≠ | Panel regression with Fourier approximation | Panel data estimator |
| Основополагающий источник≠ | Becker, R., Enders, W., & Lee, J. (2006). A stationary test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409. DOI ↗ | 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 | Fourier RE model, FFF random effects, flexible Fourier random effects, Fourier augmented random effects | random effects estimator, RE model, GLS random effects, error components model |
| Связанные | 5 | 5 |
| Сводка≠ | The Fourier Random Effects Model extends the standard random effects panel estimator by incorporating trigonometric (Fourier) terms to approximate smooth, gradual structural change in time trends or intercepts. It retains the GLS efficiency advantages of the random effects estimator while allowing parameters to shift continuously over time without requiring knowledge of exact break dates. | 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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