Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| NARDL con Parámetros Variables en el Tiempo (TVP-NARDL)× | Prueba de fronteras ARDL (Prueba de fronteras de Pesaran)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2019 (TVP extension); 2014 (NARDL base) | 2001 |
| Autor original≠ | Bagnai & Ospina-Rojas (TVP extension); NARDL base by Shin, Yu & Greenwood-Nimmo | Pesaran, Shin & Smith |
| Tipo≠ | Nonlinear time-series model with time-varying coefficients | Cointegration test / Autoregressive distributed lag model |
| Fuente seminal≠ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. Horrace & R. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer. link ↗ | Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326. DOI ↗ |
| Alias | TVP-NARDL, time-varying NARDL, rolling NARDL, dynamic asymmetric ARDL | Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test) |
| Relacionados≠ | 3 | 4 |
| Resumen≠ | The Time-Varying Parameter NARDL (TVP-NARDL) model extends the Nonlinear ARDL framework by allowing the coefficients on positive and negative partial sums of a regressor to change over time. This combination captures both asymmetric responses and structural instability in long-run and short-run relationships within a single cointegrating specification. | The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations. |
| ScholarGateConjunto de datos ↗ |
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