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| Δια-ποσοστημοριακό διάγραμμα (Cross-Quantilogram)× | QARDL (Quantile Autoregressive Distributed Lag)× | |
|---|---|---|
| Πεδίο | Οικονομετρία | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 2012 | 2006 |
| Δημιουργός≠ | Oliver Linton and Yoon-Jin Whang | Roger Koenker and Zhijie Xiao |
| Τύπος≠ | Correlation measure | Conditional distribution model |
| Θεμελιώδης πηγή≠ | Linton, O., & Whang, Y. J. (2012). Quantile comparisons of time series data. Journal of Econometrics, 170(2), 242-257. link ↗ | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ |
| Εναλλακτικές ονομασίες≠ | — | Quantile ARDL |
| Συναφείς | 3 | 3 |
| Σύνοψη≠ | The cross-quantilogram extends the cross-correlogram concept to quantile pairs of two time series, measuring dependence at different quantile levels. Introduced by Linton and Whang (2012), it captures how shocks at specific quantile levels in one series relate to movements in another, enabling asymmetric dependence analysis. This approach is particularly valuable when downside and upside risk correlations differ materially. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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