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Regresión Bayesiana Cuantil-sobre-Cuantil×Regresión Cuantílica×
CampoEconometríaEconometría
FamiliaRegression modelRegression model
Año de origen2015–20191978
Autor originalBayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001)Koenker & Bassett
TipoNonparametric quantile regression with Bayesian estimationConditional quantile regression
Fuente seminalSim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
AliasBayesian QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regressionconditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados65
ResumenBayesian 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 regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.
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ScholarGateComparar métodos: Bayesian Quantile-on-Quantile Regression · Quantile Regression. Recuperado el 2026-06-17 de https://scholargate.app/es/compare