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dc.contributor.authorOrth, Walteren_US
dc.date.accessioned2011-05-04T07:31:08Z-
dc.date.available2011-05-04T07:31:08Z-
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/10419/45360-
dc.description.abstractCredit ratings are ordinal predictions for the default risk of an obligor. To evaluate the accuracy of such predictions commonly used measures are the Accuracy Ratio or, equivalently, the Area under the ROC curve. The disadvantage of these measures is that they treat default as a binary variable thereby neglecting the timing of the default events and also not using the full information from censored observations. We present an alternative measure that is related to the Accuracy Ratio but does not suffer from these drawbacks. As a second contribution, we study statistical inference for the Accuracy Ratio and the proposed measure in the case of multiple cohorts of obligors with overlapping lifetimes. We derive methods that use more sample information and lead to more powerful tests than alternatives that filter just the independent part of the dataset. All procedures are illustrated in the empirical section using a dataset of S&P Long Term Credit Ratings.en_US
dc.language.isoengen_US
dc.publisherUniv., Seminar für Wirtschafts- und Sozialstatistik Kölnen_US
dc.relation.ispartofseriesDiscussion papers in statistics and econometrics 2/10en_US
dc.subject.jelC41en_US
dc.subject.jelC52en_US
dc.subject.jelG17en_US
dc.subject.jelG24en_US
dc.subject.jelG32en_US
dc.subject.ddc330en_US
dc.subject.keywordratingsen_US
dc.subject.keywordpredictive accuracyen_US
dc.subject.keywordAccuracy Ratioen_US
dc.subject.keywordHarrell's Cen_US
dc.subject.keywordoverlapping lifetimesen_US
dc.titleThe predictive accuracy of credit ratings: measurement and statistical inferenceen_US
dc.typeWorking Paperen_US
dc.identifier.ppn656637080en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:ucdpse:210-
Appears in Collections:Discussion Papers in Econometrics and Statistics, Institut für Ökonometrie und Statistik, Universität Köln

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