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Selecting The “Best” Prediction Model: An Application To Agricultural Cooperatives

Published online by Cambridge University Press:  09 September 2016

Alicia N. Rambaldi ,
Hector O. Zapata  and
Ralph D. Christy
Alicia N. Rambaldi
Affiliation:
Department of Economics, Louisiana State University
Hector O. Zapata
Affiliation:
Department of Agricultural Economics and Agribusiness, Louisiana Agricultural Experiment Station, Louisiana State UniversityAgricultural Center, Baton Rouge, Louisiana
Ralph D. Christy
Affiliation:
Department of Agricultural Economics, Cornell University, Ithaca, New York
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Abstract

A credit scoring function incorporating statistical selection criteria was proposed to evaluate the credit worthiness of agricultural cooperative loans in the Fifth Farm Credit District. In-sample (1981-1986) and out-of-sample (1988) prediction performance of the selected models were evaluated using rank transformation discriminant analysis, logit, and probit. Results indicate superior out-of-sample performance for the management oriented approach relative to classification of unacceptable loans, and poor performance of the rank transformation in out-of-sample prediction.

Keywords

cooperativesdiscriminant analysislogitprobitrank transformation
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 24 , Issue 1 , July 1992 , pp. 163 - 169
Copyright
Copyright © Southern Agricultural Economics Association 1992

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