A cross-sectional survival analysis regression model with applications to consumer credit risk
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Date
2017Author
Marimo, Mercy
Breed, Douw Gerbrand
Malwandla, Musa Clive
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When performing long-range survival estimations, longitudinal survival analysis methods
such as Cox Proportional Hazards (PH) and accelerated lifetime models may produce estimates
that are outdated. This paper introduces a cross-sectional survival analysis regression model for
discrete-time survival analysis. The paper describes a number of variations to the model, including
how the model can be used to model competing risks. The model is applied to a portfolio of defaulted
loans to estimate the probability of loss. The model’s performance is benchmarked against the Cox
PH model. Results show that cross-sectional survival analysis performs better than the conventional
methods of survival. This is attributable to the fact that the cross-sectional survival method is able
to use only the most recent survival information to inform predictions
URI
http://hdl.handle.net/10394/26537http://hdl.handle.net/10520/EJC-67c598d31
https://journals.co.za/content/journal/10520/EJC-67c598d31