Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Datos de Panel con Transformada de Fourier× | Análisis de datos de panel con rupturas estructurales× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2006 (Fourier framework); panel extensions 2010s | 1998-2010 |
| Autor original≠ | Becker, Enders, and Lee (Fourier unit root framework); extended to panel data by subsequent applied econometricians | Bai & Perron (1998); extended to panels by Bai (2010) and Joseph et al. |
| Tipo≠ | Panel regression with Fourier terms | Panel time-series model with regime shifts |
| Fuente seminal≠ | 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 ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78. DOI ↗ |
| Alias | Fourier panel regression, smooth structural break panel model, trigonometric panel data model, Fourier-flexible panel estimator | panel structural break test, break-point panel model, panel change-point analysis, regime-shift panel analysis |
| Relacionados≠ | 6 | 4 |
| Resumen≠ | 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. | Structural break panel data analysis detects and estimates points in time — break dates — where the underlying regression coefficients shift permanently across a panel of cross-sectional units observed over multiple periods. By jointly exploiting cross-sectional and time-series variation, it offers sharper identification of regime shifts than single-series break tests, and it delivers separate coefficient estimates for each regime before and after each break. |
| ScholarGateConjunto de datos ↗ |
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