Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Фурье-анализ панельных данных× | Модель с фиксированными эффектами панели× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2006 (Fourier framework); panel extensions 2010s | 1978 |
| Автор метода≠ | Becker, Enders, and Lee (Fourier unit root framework); extended to panel data by subsequent applied econometricians | Mundlak (1978); classical treatment in Wooldridge (2010) and Baltagi (2021) |
| Тип≠ | Panel regression with Fourier terms | Panel regression 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 ↗ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Другие названия | Fourier panel regression, smooth structural break panel model, trigonometric panel data model, Fourier-flexible panel estimator | within estimator, FE model, within-group estimator, LSDV model |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Fourier panel data analysis embeds trigonometric sine and cosine terms into a standard panel regression to approximate smooth, gradual structural shifts in the data-generating process. Rather than assuming a sharp break at a known date, the Fourier approach lets the data reveal the timing and shape of any structural change through a flexible trigonometric approximation, while retaining the cross-sectional and time-series structure of panel data. | The panel fixed effects (FE) model controls for all time-invariant, unit-specific unobserved heterogeneity by absorbing it into individual intercepts. By sweeping out unit means through the within transformation, FE yields unbiased estimates of the effect of time-varying regressors even when omitted unit-level confounders are correlated with those regressors. |
| ScholarGateНабор данных ↗ |
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