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Kvantilregression×Robust Generaliserad Minsta Kvadrat (Robust GLS)×
ÄmnesområdeEkonometriEkonometri
FamiljRegression modelRegression model
Ursprungsår19781936 / 1980
UpphovspersonKoenker & BassettAitken (GLS theory, 1936); White (robust covariance, 1980)
TypConditional quantile regressionRobust linear regression
UrsprungskällaKoenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Aliasconditional quantile regression, regression quantiles, Kantil Regresyonrobust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Närliggande55
SammanfattningQuantile 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.
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ScholarGateJämför metoder: Quantile Regression · Robust GLS. Hämtad 2026-06-18 från https://scholargate.app/sv/compare