Predicting Credit Default in an Agricultural Bank: Methods and Issues

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dc.creatorOdeh, Oluwarotimi O.
dc.creatorFeatherstone, Allen M.
dc.creatorSanjoy, Das
dc.date2017-04-01T19:44:37Z
dc.date.accessioned2026-07-09T04:25:12Z
dc.descriptionThis study examines the performance of logistic regression, artificial neural networks and adaptive neuro-fuzzy inference system in predicting credit default using data from Farm Credit System. Empirical findings show that credit default predictions vary with empirical model used.
dc.identifierdoi:10.22004/ag.econ.35359
dc.identifierhttps://ageconsearch.umn.edu/record/35359/files/sp06od02.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/35359
dc.identifier.urihttp://hdl.handle.net/123456789/549623
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/35359
dc.titlePredicting Credit Default in an Agricultural Bank: Methods and Issues
dc.typeText

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