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dc.contributor.authorOrth, Walteren
dc.date.accessioned2011-05-04T07:31:12Z-
dc.date.available2011-05-04T07:31:12Z-
dc.date.issued2011-
dc.identifier.urihttp://hdl.handle.net/10419/45363-
dc.description.abstractIn credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy.en
dc.language.isoengen
dc.publisher|aUniversity of Cologne, Seminar of Economic and Social Statistics |cCologneen
dc.relation.ispartofseries|aDiscussion Papers in Statistics and Econometrics |x3/11en
dc.subject.jelC41en
dc.subject.jelC53en
dc.subject.jelC58en
dc.subject.jelG17en
dc.subject.jelG32en
dc.subject.jelG33en
dc.subject.ddc330en
dc.subject.keywordcredit defaulten
dc.subject.keywordmulti-period predictionsen
dc.subject.keywordhazard modelsen
dc.subject.keywordpanel dataen
dc.subject.keywordout-of-sample testsen
dc.titleMulti-period credit default prediction with time-varying covariates-
dc.typeWorking Paperen
dc.identifier.ppn65665192Xen
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:ucdpse:311en

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