Comparer des méthodes

	Régression Bayésienne Quantile-sur-Quantile	Régression quantile-quantile (QQ)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2015–2019	2015
Auteur d'origine	Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001)	Sim and Zhou
Type	Nonparametric quantile regression with Bayesian estimation	Nonparametric quantile regression
Source fondatrice	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8.	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.
Alias	Bayesian QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regression	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression
Apparentées	6	6
Résumé	Bayesian Quantile-on-Quantile (BQQ) Regression extends the Sim-Zhou quantile-on-quantile framework by replacing frequentist local linear estimation with Bayesian posterior inference. For each pair of quantiles (theta of the outcome, tau of the predictor), the method yields a full posterior distribution over the slope, enabling uncertainty quantification across the entire bivariate quantile surface — a key advantage when sample sizes are moderate and tail quantiles are sparse.	Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the outcome and the quantile of the predictor, revealing heterogeneous and asymmetric dependency structures invisible to standard regression.