方法对比

	分位数回归	稳健广义最小二乘法 (Robust GLS)	稳健OLS（具有稳健标准误的OLS）
领域	计量经济学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model
起源年份	1978	1936 / 1980	1980
提出者	Koenker & Bassett	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Halbert White
类型	Conditional quantile regression	Robust linear regression	Linear regression with robust inference
开创性文献	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
别名	conditional quantile regression, regression quantiles, Kantil Regresyon	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
相关	5	5	6
摘要	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.	Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure.	Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.