Gini Regressions and Heteroskedasticity

Arthur Charpentier (), Ndéné Ka (), Stéphane Mussard and Oumar Ndiaye
Additional contact information
Arthur Charpentier: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique
Oumar Ndiaye: CHROME - Détection, évaluation, gestion des risques CHROniques et éMErgents (CHROME) - Université de Nîmes - UNIMES - Université de Nîmes

Post-Print from HAL

Abstract: We propose an Aitken estimator for Gini regression. The suggested A-Gini estimator is proven to be a U-statistics. Monte Carlo simulations are provided to deal with heteroskedasticity and to make some comparisons between the generalized least squares and the Gini regression. A Gini-White test is proposed and shows that a better power is obtained compared with the usual White test when outlying observations contaminate the data.

Keywords: U-statistics; jackknife; heteroskedasticity; Gini (search for similar items in EconPapers)
Date: 2019-03
New Economics Papers: this item is included in nep-ecm
Note: View the original document on HAL open archive server: https://hal.science/hal-02131746v1
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Econometrics, 2019, 7 (1), pp.4. ⟨10.3390/econometrics7010004⟩

Downloads: (external link)
https://hal.science/hal-02131746v1/document (application/pdf)

Related works:
Journal Article: Gini Regressions and Heteroskedasticity (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02131746

DOI: 10.3390/econometrics7010004

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().