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Linganisha mbinu

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Regression ya Kiasi (Quantile Regression)×Generalized Least Squares (GLS) Imara×
NyanjaEkonometrikiEkonometriki
FamiliaRegression modelRegression model
Mwaka wa asili19781936 / 1980
MwanzilishiKoenker & BassettAitken (GLS theory, 1936); White (robust covariance, 1980)
AinaConditional quantile regressionRobust linear regression
Chanzo asiliaKoenker, 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
Majina mbadalaconditional quantile regression, regression quantiles, Kantil Regresyonrobust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Zinazohusiana55
MuhtasariQuantile 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.
ScholarGateSeti ya data
  1. v1
  2. 2 Vyanzo
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  1. v1
  2. 2 Vyanzo
  3. PUBLISHED

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ScholarGateLinganisha mbinu: Quantile Regression · Robust GLS. Imepatikana 2026-06-18 kutoka https://scholargate.app/sw/compare