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| Квантилна АРДЛ (Quantile Autoregressive Distributed Lag)× | Квантилен ВАР× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване | 2006 | 2006 |
| Създател≠ | Roger Koenker and Zhijie Xiao | Koenker and Xiao |
| Тип≠ | Conditional distribution model | Distribution impulse response |
| Основополагащ източник | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ |
| Други названия | Quantile ARDL | Quantile-based impulse response |
| Свързани | 3 | 3 |
| Резюме≠ | 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. | Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times. |
| ScholarGateНабор от данни ↗ |
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