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Квантилна АРДЛ (Quantile Autoregressive Distributed Lag)×Напречно селективен АРДЛ×
ОбластИконометрияИконометрия
СемействоRegression modelRegression model
Година на възникване20062006
СъздателRoger Koenker and Zhijie XiaoPesaran and colleagues
ТипConditional distribution modelDynamic panel model
Основополагащ източникKoenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117. link ↗
Други названияQuantile ARDLPanel ARDL with cross-sectional dependence
Свързани33
РезюмеQARDL (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.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.
ScholarGateНабор от данни
  1. v1
  2. 2 Източници
  3. PUBLISHED
  1. v1
  2. 2 Източници
  3. PUBLISHED

Към търсенето Изтегляне на слайдове

ScholarGateСравнение на методи: QARDL · CS-ARDL. Извлечено на 2026-06-19 от https://scholargate.app/bg/compare