Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression Robuste Quantile par Quantile (RQQR)× | Régression quantile× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2015–2020s | 1978 |
| Auteur d'origine≠ | Sim and Zhou (2015) for QQ regression; robust extensions developed subsequently in the literature | Koenker & Bassett |
| Type≠ | Nonparametric quantile regression | Conditional quantile regression |
| Source fondatrice≠ | Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | RQQR, robust QQ regression, robust quantile-on-quantile, outlier-robust QQR | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Apparentées≠ | 3 | 5 |
| Résumé≠ | Robust Quantile-on-Quantile Regression extends the QQ framework of Sim and Zhou (2015) by adding resistance to outliers and heavy-tailed distributions. It estimates how each quantile of one variable responds to each quantile of another, producing a full dependence surface while guarding against leverage points that can distort standard QQ estimates. | 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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