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Application of Recursive Partitioning to Agricultural Credit Scoring

Published online by Cambridge University Press:  28 April 2015

Michael P. Novak  and
Eddy LaDue
Michael P. Novak
Affiliation:
Federal Agricultural Mortgage Corporation, Washington, DC
Eddy LaDue
Affiliation:
Department of Agricultural, Resource, and Managerial Economics, Cornell University, Ithaca, NY
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Abstract

Recursive 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.

Keywords

financecredit scoringmisclassificationrecursive partitioning algorith
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 31 , Issue 1 , April 1999 , pp. 109 - 122
Copyright
Copyright © Southern Agricultural Economics Association 1999

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