AgEcon Search

AgEcon Search >
       Journal of Agricultural and Applied Economics >
          Volume 31, Number 01, April 1999 >

Please use this identifier to cite or link to this item: http://purl.umn.edu/15129

Title: APPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING
Authors: Novak, Michael P.
LaDue, Eddy L.
Keywords: finance
credit scoring
misclassification
recursive partitioning algorithm
Issue Date: 1999-04
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.
URI: http://purl.umn.edu/15129
Institution/Association: Journal of Agricultural and Applied Economics>Volume 31, Number 01, April 1999
Total Pages: 14
Language: English
From Page: 109
To Page: 122
Collections:Volume 31, Number 01, April 1999

Files in This Item:

File SizeFormat
31010109.pdf1170KbPDFView/Open
Recommend this item

All items in AgEcon Search are protected by copyright.

 

 

Brought to you by the University of Minnesota Department of Applied Economics and the University of Minnesota Libraries with cooperation from the Agricultural and Applied Economics Association.

All papers are in Acrobat (.pdf) format. Get Adobe Reader

Contact Us

Powered by: