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| Bayesiansk kvantil-på-kvantil-regression× | Kvantilregression× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2015–2019 | 1978 |
| Ophavsperson≠ | Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001) | Koenker & Bassett |
| Type≠ | Nonparametric quantile regression with Bayesian estimation | Conditional quantile regression |
| Oprindelig kilde≠ | Sim, 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 ↗ |
| Aliasser≠ | Bayesian QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | 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 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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