Inadimplência no crédito agrícola: a utilização do modelo de regressão logística: LOGIT

• Ano de defesa: 2009
• Autor(a) principal: Gonçalves Júnior, Carlos Alberto
• Orientador(a): Opazo, Miguel Angel Uribe
• Banca de defesa: Gimenes, Régio Marcio Toesca , Pereira, Joaquim Odilon
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual do Oeste do Parana
• Programa de Pós-Graduação: Programa de Pós-Graduação Stricto Sensu em Desenvolvimento Regional e Agronegócio
• Departamento: Desenvolvimento regional e do Agronegócio
• País: BR
• Palavras-chave em Português: Assimetria de informações; Regressão logística; Inadimplência; Crédito agrícola; Cooperativas agropecuárias - Finanças; Cooperativas de crédito; Modelo de regressão logística; LOGIT
• Palavras-chave em Inglês: Asymmetry of information; Logistic regression; Default
• Área do conhecimento CNPq: CNPQ::CIENCIAS SOCIAIS APLICADAS::ECONOMIA::ECONOMIAS AGRARIA E DOS RECURSOS NATURAIS::ECONOMIA AGRARIA
• Link de acesso: http://tede.unioeste.br:8080/tede/handle/tede/2249

This research work aims to create a tool that can help a credit cooperative in the region of Toledo in the analysis and concession of agricultural credit calculating the probability of completion of the contracts, what permits to predict a possible default, using the logistic regression model - Logit. The theoretical framework used, based on the Theory of Transaction Costs, identifies the default as the result of the incompleteness of contracts and of the asymmetry of information between the parties in a transaction. The goal is that this tool can reduce the asymmetry of information between the borrowers and the credit cooperative, in order to avoid the granting of credit to possible defaulters. To do so, it was collected information about the borrowers from the records of the cooperative from the year 2004 to 2007, aiming to draw a profile of the credit borrower. Later, it was estimated the logistic regression model for 10 different samples to identify the sample that received the greatest number of his between payers and defaulters. It was established that the estimated model was more efficient to identify the payers contracts than defaulters contracts, and even with a not too high percentage average of correct the model can assist the decision-making of the cooperative in granting credit.