Show simple item record

dc.contributor.authorHjelseth, Ida Nervik
dc.contributor.authorRaknerud, Arvid
dc.contributor.authorVatne, Bjørn H.
dc.description.abstractWe propose an econometric model for predicting the share of bank debt held by bankrupt firms by combining a novel set of firm-level financial variables and macroeconomic indicators. Our firm-level data include payment remarks in the form of debt collections from private agencies and attachments from private and public agencies and cover all Norwegian limited liability companies for the period 2010–2021. We use logistic Lasso regressions to select bankruptcy predictors from a large set of potential predictors, comparing a highly sparse variable selection criterion (“the one standard error rule”) with the minimum cross validation error (CVE) criterion. Moreover, we examine the implications of using debt shares as weights in the estimation and find that weighting has a large impact on variable selection and predictions and, generally, leads to lower out-of-sample prediction errors than alternative approaches. Debt weighting combined with sparse variable selection gives the best predictions of the risk of bankruptcy in firms holding high shares of the bank debt.en_US
dc.publisherNorges Banken_US
dc.relation.ispartofseriesWorking paper;7/2022
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.subjectJEL: C25en_US
dc.subjectJEL: C33en_US
dc.subjectJEL: C53en_US
dc.subjectJEL: G33en_US
dc.subjectJEL: D22en_US
dc.subjectbankruptcy predictionen_US
dc.subjectcredit risken_US
dc.subjectcorporate bank debten_US
dc.subjectweighted logistic regressionen_US
dc.titleA bankruptcy probability model for assessing credit risk on corporate loans with automated variable selectionen_US
dc.typeWorking paperen_US
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212en_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal