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dc.contributor.authorOrth, Walteren_US
dc.date.accessioned2011-05-04T07:31:12Z-
dc.date.available2011-05-04T07:31:12Z-
dc.date.issued2011en_US
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_US
dc.language.isoengen_US
dc.publisherUniv., Seminar für Wirtschafts- und Sozialstatistik Kölnen_US
dc.relation.ispartofseriesDiscussion papers in statistics and econometrics 3/11en_US
dc.subject.jelC41en_US
dc.subject.jelC53en_US
dc.subject.jelC58en_US
dc.subject.jelG17en_US
dc.subject.jelG32en_US
dc.subject.jelG33en_US
dc.subject.ddc330en_US
dc.subject.keywordcredit defaulten_US
dc.subject.keywordmulti-period predictionsen_US
dc.subject.keywordhazard modelsen_US
dc.subject.keywordpanel dataen_US
dc.subject.keywordout-of-sample testsen_US
dc.titleMulti-period credit default prediction with time-varying covariatesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn65665192Xen_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:ucdpse:311-
Appears in Collections:Discussion Papers in Econometrics and Statistics, Institut für Ökonometrie und Statistik, Universität Köln

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