APPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING

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.


Issue Date:
1999-04
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/15129
Published in:
Journal of Agricultural and Applied Economics, Volume 31, Number 1
Page range:
109-122
Total Pages:
14




 Record created 2017-04-01, last modified 2017-08-23

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