Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| ARDL no lineal de Fourier (NARDL de Fourier)× | Prueba de Fronteras ARDL de Fourier× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2014–2020s | 2001-2021 |
| Autor original≠ | Extension of Shin, Yu & Greenwood-Nimmo (2014) NARDL, incorporating Fourier terms from Becker, Enders & Lee (2006) | Pesaran, Shin & Smith (ARDL foundation); Fourier extension by Nazlioglu and related authors |
| Tipo≠ | Nonlinear cointegrating model with smooth break approximation | Cointegration / bounds test |
| Fuente seminal≠ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer. link ↗ | Nazlioglu, S., Gormus, A., & Soytas, U. (2021). Oil prices and monetary policy in emerging markets: structural breaks, asymmetries, and Fourier approximations. Energy Economics, 95, 105119. link ↗ |
| Alias | Fourier NARDL, Fourier nonlinear ARDL, F-NARDL, Fourier asymmetric ARDL | Fourier ARDL, Fourier bounds testing, ARDL with Fourier approximation, F-ARDL cointegration test |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | Fourier NARDL extends the Nonlinear ARDL (NARDL) bounds-testing framework by adding Fourier trigonometric terms to the error-correction equation, allowing the model to capture smooth, gradual structural breaks in the long-run relationship without requiring the researcher to know or specify the break date in advance. | The Fourier ARDL bounds test augments the Pesaran-Shin-Smith cointegration framework with trigonometric (Fourier) terms that capture gradual, smooth structural breaks in the data-generating process. It tests for a long-run level relationship between variables without requiring the researcher to specify the number, timing, or form of structural breaks in advance. |
| ScholarGateConjunto de datos ↗ |
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