Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/298407 
Year of Publication: 
2022
Series/Report no.: 
Working Paper No. 7/2022
Publisher: 
Norges Bank, Oslo
Abstract: 
We 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.
Subjects: 
Bankruptcy prediction
credit risk
corporate bank debt
Lasso
weighted logistic regression
JEL: 
C25
C33
C53
G33
D22
Persistent Identifier of the first edition: 
ISBN: 
978-82-8379-237-9
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper
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