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Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions

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dc.contributor.authorGÖLER, KEMAL
dc.contributor.authorPin Ng
dc.contributor.authorZhijie Xiao
dc.contributor.orcid0000-0003-2175-3477
dc.contributor.orcid0000-0002-5018-9513
dc.contributor.orcid0000-0003-3626-8220
dc.date.accessioned2025-11-13T10:34:41Z
dc.date.issued2017-03-27
dc.identifier.doihttps://doi.org/10.1002/for.2462
dc.identifier.endpage679
dc.identifier.issn0277-6693
dc.identifier.issue6
dc.identifier.openalexW2599071934
dc.identifier.startpage651
dc.identifier.urihttps://hdl.handle.net/11421/4594
dc.identifier.urihttps://doi.org/10.1002/for.2462
dc.identifier.volume36
dc.language.isoen
dc.relation.ispartofJournal of Forecasting
dc.rightsrestrictedAccess
dc.subjectQuantile
dc.subjectEconometrics
dc.subjectStatistic
dc.subjectConsensus forecast
dc.subjectForecast verification
dc.subjectForecast error
dc.subjectStatistics
dc.subjectForecast skill
dc.subjectQuantile regression
dc.subjectFunction (biology)
dc.subjectEconomics
dc.subjectMathematics
dc.subject.sdg8
dc.titleMincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions
dc.typeArticle
dspace.entity.typePublication
local.authorid.openalexA5072324882

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