方法对比
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| 鲁棒分位数-分位数 (RQQR) 回归× | 分位数-分位数（QQ）回归× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2015–2020s | 2015 |
| 提出者≠ | Sim and Zhou (2015) for QQ regression; robust extensions developed subsequently in the literature | Sim and Zhou |
| 类型 | Nonparametric quantile regression | Nonparametric quantile regression |
| 开创性文献≠ | Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8. DOI ↗ | 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 ↗ |
| 别名 | RQQR, robust QQ regression, robust quantile-on-quantile, outlier-robust QQR | QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression |
| 相关≠ | 3 | 6 |
| 摘要≠ | 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-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. |
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