APPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING

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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.

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http://hdl.handle.net/123456789/528664

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https://ageconsearch.umn.edu/record/15129/files/31010109.pdf

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University of Minnesota