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https://hdl.handle.net/10419/50001
Kompletter Metadatensatz
DublinCore-Feld | Wert | Sprache |
---|---|---|
dc.contributor.author | Förstemann, Till | en |
dc.date.accessioned | 2011-09-27 | - |
dc.date.accessioned | 2011-09-29T15:46:16Z | - |
dc.date.available | 2011-09-29T15:46:16Z | - |
dc.date.issued | 2011 | - |
dc.identifier.isbn | 978-3-86558-745-9 | en |
dc.identifier.uri | http://hdl.handle.net/10419/50001 | - |
dc.description.abstract | Group-specific estimations can significantly improve the predictive power of accountingbased rating models. This is shown using a binary logistic regression model applied to the Deutsche Bundesbank's USTAN dataset, which contains 300,000 financial statements provided by German companies for the years 1994 to 2002, i. e. throughout a complete business-cycle. The robustness and the representability of this result is verified through out-of-sample tests and through comparisons with a benchmark model which applies the variables of Moody's RiskCalcTM for Germany. | en |
dc.language.iso | eng | en |
dc.publisher | |aDeutsche Bundesbank |cFrankfurt a. M. | en |
dc.relation.ispartofseries | |aDiscussion Paper Series 2 |x2011,11 | en |
dc.subject.jel | G21 | en |
dc.subject.jel | G33 | en |
dc.subject.jel | C52 | en |
dc.subject.ddc | 330 | en |
dc.subject.keyword | Credit Risk | en |
dc.subject.keyword | Credit Rating | en |
dc.subject.keyword | Probability of Default | en |
dc.subject.keyword | Logistic Regression | en |
dc.title | Improvements in rating models for the German corporate sector | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 668914521 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
dc.identifier.repec | RePEc:zbw:bubdp2:201111 | en |
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