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

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dc.creatorRambaldi, Alicia N.
dc.creatorZapata, Hector O.
dc.creatorChristy, Ralph D.
dc.date2017-04-01T17:52:34Z
dc.date.accessioned2026-07-09T04:08:59Z
dc.descriptionA 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.
dc.identifierdoi:10.22004/ag.econ.30380
dc.identifierhttps://ageconsearch.umn.edu/record/30380/files/24010163.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/30380
dc.identifier.urihttp://hdl.handle.net/123456789/545550
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/30380
dc.titleSELECTING THE "BEST" PREDICTION MODEL: AN APPLICATION TO AGRICULTURAL COOPERATIVES
dc.typeText

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