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dc.contributor.authorGüler, Kemal
dc.contributor.authorNg, Pin T.
dc.contributor.authorXiao, Zhijie
dc.date.accessioned2019-10-20T21:12:35Z
dc.date.available2019-10-20T21:12:35Z
dc.date.issued2017
dc.identifier.issn0277-6693
dc.identifier.issn1099-131X
dc.identifier.urihttps://dx.doi.org/10.1002/for.2462
dc.identifier.urihttps://hdl.handle.net/11421/19012
dc.descriptionWOS: 000407647500004en_US
dc.description.abstractForecasts are pervasive in all areas of applications in business and daily life. Hence evaluating the accuracy of a forecast is important for both the generators and consumers of forecasts. There are two aspects in forecast evaluation: (a) measuring the accuracy of past forecasts using some summary statistics, and (b) testing the optimality properties of the forecasts through some diagnostic tests. On measuring the accuracy of a past forecast, this paper illustrates that the summary statistics used should match the loss function that was used to generate the forecast. If there is strong evidence that an asymmetric loss function has been used in the generation of a forecast, then a summary statistic that corresponds to that asymmetric loss function should be used in assessing the accuracy of the forecast instead of the popular root mean square error or mean absolute error. On testing the optimality of the forecasts, it is demonstrated how the quantile regressions set in the prediction-realization framework of Mincer and Zarnowitz (in J. Mincer (Ed.), Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance (pp.14-20), 1969) can be used to recover the unknown parameter that controls the potentially asymmetric loss function used in generating the past forecasts. Finally, the prediction-realization framework is applied to the Federal Reserve's economic growth forecast and forecast sharing in a PC manufacturing supply chain. It is found that the Federal Reserve values overprediction approximately 1.5 times more costly than underprediction. It is also found that the PC manufacturer weighs positive forecast errors (under forecasts) about four times as costly as negative forecast errors (over forecasts).en_US
dc.description.sponsorshipTUBITAK [BIDEP 2236]; TUBITAK; Boston College; NSFC [71571110]en_US
dc.description.sponsorshipWe thank the Editor and two referees for their very helpful comments on earlier versions of this paper. The research supporting the final revision of this paper was undertaken during Kemal Guler's visit to Bilkent University, supported by a TUBITAK BIDEP 2236 Co-Circulation fellowship. He thanks TUBITAK for financial support, colleagues at Bilkent University Industrial Engineering Department for their hospitality, and Baris, Ali, Betul, Elfe and Sertug of Sundogs for their big hearts and warm Ankara memories. Xiao thanks Boston College and NSFC via grant number 71571110 for research support.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.isversionof10.1002/for.2462en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAsymmetric Lossen_US
dc.subjectExpectile Regressionen_US
dc.subjectForecast Evaluationen_US
dc.subjectQuantile Regressionen_US
dc.titleMincer-Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functionsen_US
dc.typearticleen_US
dc.relation.journalJournal of Forecastingen_US
dc.contributor.departmentAnadolu Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümüen_US
dc.identifier.volume36en_US
dc.identifier.issue6en_US
dc.identifier.startpage651en_US
dc.identifier.endpage679en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US]


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