Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/45363
Authors: 
Orth, Walter
Year of Publication: 
2011
Series/Report no.: 
Discussion papers in statistics and econometrics 3/11
Abstract: 
In 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.
Subjects: 
credit default
multi-period predictions
hazard models
panel data
out-of-sample tests
JEL: 
C41
C53
C58
G17
G32
G33
Document Type: 
Working Paper

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