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| Ποσοστιαία VAR (Quantile VAR)× | Μέθοδος Στιγμών για Παλινδρόμηση Ποσοστημορίων× | |
|---|---|---|
| Πεδίο | Οικονομετρία | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 2006 | 2004 |
| Δημιουργός≠ | Koenker and Xiao | Roger Koenker and colleagues |
| Τύπος≠ | Distribution impulse response | Distribution regression |
| Θεμελιώδης πηγή≠ | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ | Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74-89. DOI ↗ |
| Εναλλακτικές ονομασίες | Quantile-based impulse response | GMM quantile regression |
| Συναφείς | 3 | 3 |
| Σύνοψη≠ | 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. | Method of Moments Quantile Regression combines moment-based estimation (GMM) with quantile regression to estimate distribution parameters while handling endogeneity, panel structure, and dynamic relationships. Introduced by Koenker (2004) and developed by Machado and Mata (2005), it enables distributional analysis (not just mean regression) in complex settings like dynamic panels and instrumental-variable contexts. This approach is powerful for understanding heterogeneity in treatment effects and policy impacts. |
| ScholarGateΣύνολο δεδομένων ↗ |
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