SELECTING THE "BEST" PREDICTION MODEL: AN APPLICATION TO AGRICULTURAL COOPERATIVES

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.


Subject(s):
Issue Date:
1992-07
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/30380
Published in:
Southern Journal of Agricultural Economics, Volume 24, Number 1
Page range:
163-169
Total Pages:
7




 Record created 2017-04-01, last modified 2017-04-28

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