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Application of Recursive Partitioning to Agricultural Credit Scoring

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  • Novak, Michael P.
  • LaDue, Eddy

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.

Suggested Citation

  • Novak, Michael P. & LaDue, Eddy, 1999. "Application of Recursive Partitioning to Agricultural Credit Scoring," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 31(1), pages 109-122, April.
    • Handle: RePEc:cup:jagaec:v:31:y:1999:i:01:p:109-122_02
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    Cited by:

    1. Natalia Nehrebecka, 2016. "Approach to the assessment of credit risk for non-financial corporations. Evidence from Poland," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Combining micro and macro data for financial stability analysis, volume 41, Bank for International Settlements.
    2. Sanjay J. Bhayani & Butalal Ajmera, 2011. "A Study on Performance Evaluation of Dinesh Mills Ltd," Indian Journal of Commerce and Management Studies, Educational Research Multimedia & Publications,India, vol. 2(6), pages 114-123, September.
    3. Gloy, Brent A. & LaDue, Eddy L. & Gunderson, Michael A., 2004. "Credit Risk Migration Experienced By Agricultural Lenders," Working Papers 127147, Cornell University, Department of Applied Economics and Management.

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