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Article: Level robust methods based on the least squares regression estimator

TitleLevel robust methods based on the least squares regression estimator
Authors
KeywordsBootstrap
Heteroscedasticity
Level Robust Methods
Issue Date2009
PublisherWayne State University, College of Education. The Journal's web site is located at http://www.jmasm.com/
Citation
Journal Of Modern Applied Statistical Methods, 2009, v. 8 n. 2, p. 384-395 How to Cite?
AbstractHeteroscedastic consistent covariance matrix (HCCM) estimators provide ways for testing hypotheses about regression coefficients under heteroscedasticity. Recent studies have found that methods combining the HCCM-based test statistic with the wild bootstrap consistently perform better than non-bootstrap HCCM-based methods (Davidson & Flachaire, 2008; Flachaire, 2005; Godfrey, 2006). This finding is more closely examined by considering a broader range of situations which were not included in any of the previous studies. In addition, the latest version of HCCM, HC5 (Cribari-Neto, et al., 2007), is evaluated. © 2009 JMASM, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/175505
ISSN
2015 SCImago Journal Rankings: 0.144
References

 

DC FieldValueLanguage
dc.contributor.authorNg, Men_US
dc.contributor.authorWilcox, RRen_US
dc.date.accessioned2012-11-26T08:59:00Z-
dc.date.available2012-11-26T08:59:00Z-
dc.date.issued2009en_US
dc.identifier.citationJournal Of Modern Applied Statistical Methods, 2009, v. 8 n. 2, p. 384-395en_US
dc.identifier.issn1538-9472en_US
dc.identifier.urihttp://hdl.handle.net/10722/175505-
dc.description.abstractHeteroscedastic consistent covariance matrix (HCCM) estimators provide ways for testing hypotheses about regression coefficients under heteroscedasticity. Recent studies have found that methods combining the HCCM-based test statistic with the wild bootstrap consistently perform better than non-bootstrap HCCM-based methods (Davidson & Flachaire, 2008; Flachaire, 2005; Godfrey, 2006). This finding is more closely examined by considering a broader range of situations which were not included in any of the previous studies. In addition, the latest version of HCCM, HC5 (Cribari-Neto, et al., 2007), is evaluated. © 2009 JMASM, Inc.en_US
dc.languageengen_US
dc.publisherWayne State University, College of Education. The Journal's web site is located at http://www.jmasm.com/en_US
dc.relation.ispartofJournal of Modern Applied Statistical Methodsen_US
dc.subjectBootstrapen_US
dc.subjectHeteroscedasticityen_US
dc.subjectLevel Robust Methodsen_US
dc.titleLevel robust methods based on the least squares regression estimatoren_US
dc.typeArticleen_US
dc.identifier.emailNg, M: marieng@hku.hken_US
dc.identifier.authorityNg, M=rp01451en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-82355187737en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-82355187737&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume8en_US
dc.identifier.issue2en_US
dc.identifier.spage384en_US
dc.identifier.epage395en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridNg, M=36155754200en_US
dc.identifier.scopusauthoridWilcox, RR=7202527113en_US

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