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dc.contributor.authorTofallis, C.
dc.date.accessioned2016-03-03T12:17:26Z
dc.date.available2016-03-03T12:17:26Z
dc.date.issued2015-08-01
dc.identifier.citationTofallis , C 2015 , ' A better measure of relative prediction accuracy for model selection and model estimation ' , Journal of the Operational Research Society , vol. 66 , no. 8 , pp. 1352-1362 . https://doi.org/10.1057/jors.2014.103
dc.identifier.issn0160-5682
dc.identifier.otherPURE: 9313779
dc.identifier.otherPURE UUID: cec29230-e4c5-4fff-8f57-95f6e6f4fa45
dc.identifier.otherScopus: 84937509634
dc.identifier.urihttp://hdl.handle.net/2299/16654
dc.description.abstractSurveys show that the mean absolute percentage error (MAPE) is the most widely used measure of forecast accuracy in businesses and organizations. It is however, biased: When used to select among competing prediction methods it systematically selects those whose predictions are too low. This is not widely discussed and so is not generally known among practitioners. We explain why this happens. We investigate an alternative relative accuracy measure which avoids this bias: the log of the accuracy ratio: log (prediction / actual). Relative accuracy is particularly relevant if the scatter in the data grows as the value of the variable grows (heteroscedasticity). We demonstrate using simulations that for heteroscedastic data (modelled by a multiplicative error factor) the proposed metric is far superior to MAPE for model selection. Another use for accuracy measures is in fitting parameters to prediction models. Minimum MAPE models do not predict a simple statistic and so theoretical analysis is limited. We prove that when the proposed metric is used instead, the resulting least squares regression model predicts the geometric mean. This important property allows its theoretical properties to be understood.en
dc.language.isoeng
dc.relation.ispartofJournal of the Operational Research Society
dc.rightsEmbargoed
dc.subjectprediction
dc.subjectforecasting
dc.subjectmodel selection
dc.subjectloss function
dc.subjectregression
dc.subjecttime series
dc.subjectDecision Sciences(all)
dc.subjectEconomics and Econometrics
dc.subjectBusiness, Management and Accounting (miscellaneous)
dc.titleA better measure of relative prediction accuracy for model selection and model estimationen
dc.contributor.institutionSocial Sciences, Arts & Humanities Research Institute
dc.contributor.institutionHertfordshire Business School
dc.contributor.institutionCentre for Research on Management, Economy and Society
dc.contributor.institutionStatistical Services Consulting Unit
dc.contributor.institutionHealthcare Management and Policy Research Unit
dc.description.statusPeer reviewed
dc.date.embargoedUntil2016-05-12
dc.relation.schoolHertfordshire Business School
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2015-08-01
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1057/jors.2014.103
rioxxterms.licenseref.startdate2016-05-12
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.date.embargo2016-05-12
herts.rights.accesstypeEmbargoed


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