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| 稳健加权最小二乘法 (Robust WLS)× | 分位数回归× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1964/1981 | 1978 |
| 提出者≠ | Huber, P. J. | Koenker & Bassett |
| 类型≠ | Robust weighted regression | Conditional quantile regression |
| 开创性文献≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 别名≠ | robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 相关 | 5 | 5 |
| 摘要≠ | Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient 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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