APPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING

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dc.creatorNovak, Michael P.
dc.creatorLaDue, Eddy L.
dc.date2017-04-01T19:43:17Z
dc.date.accessioned2026-07-09T03:13:09Z
dc.descriptionRecursive Partitioning Algorithm (RPA) is introduced as a technique for credit scoring analysis, which allows direct incorporation of misclassification costs. This study corroborates nonagricultural credit studies, which indicate that RPA outperforms logistic regression based on within-sample observations. However, validation based on more appropriate out-of-sample observations indicates that logistic regression is superior under some conditions. Incorporation of misclassification costs can influence the creditworthiness decision.
dc.identifierdoi:10.22004/ag.econ.15129
dc.identifierhttps://ageconsearch.umn.edu/record/15129/files/31010109.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/15129
dc.identifier.urihttp://hdl.handle.net/123456789/528664
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/15129
dc.titleAPPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING
dc.typeText

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