Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| ARDL Transversal× | ARDL Quantílic× | |
|---|---|---|
| Camp | Econometria | Econometria |
| Família | Regression model | Regression model |
| Any d'origen | 2006 | 2006 |
| Autor original≠ | Pesaran and colleagues | Roger Koenker and Zhijie Xiao |
| Tipus≠ | Dynamic panel model | Conditional distribution model |
| Font seminal≠ | Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117. link ↗ | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ |
| Àlies | Panel ARDL with cross-sectional dependence | Quantile ARDL |
| Relacionats | 3 | 3 |
| Resum≠ | CS-ARDL (Cross-Sectional ARDL) applies the ARDL framework to panel data while explicitly accounting for cross-sectional dependence—correlation of shocks and relationships across units (countries, firms, regions). Introduced by Pesaran and colleagues (2016), it extends panel ARDL methods to handle common factors or global shocks affecting all units simultaneously. This is crucial for realistic modeling of internationally integrated economies and firm networks. | QARDL (Quantile Autoregressive Distributed Lag) combines quantile regression with ARDL modeling to estimate conditional relationships at different points of the distribution, revealing heterogeneous short-run and long-run effects. Introduced by Koenker and Xiao (2006) and refined by Cho et al. (2015), it captures how the effect of explanatory variables on outcomes varies across quantiles, essential for understanding tail behavior and distributional impacts rather than just mean effects. |
| ScholarGateConjunt de dades ↗ |
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