A Bayesian Analysis of a Random Effects Small Business Loan Credit Scoring Model | Pakistan Journal of Statistics and Operation Research 2011

Author(s)

Patrick J. Farrell School of Mathematics and Statistics Carleton University, 1125 Colonel By Drive Ottawa, Ontario, CANADA

Brenda MacGibbon Département de mathématiques Université du Québec à Montréal C.P. 8888, Succursale Centre-Ville Montréal, Québec, CANADA

Thomas J. Tomberlin Sprott School of Business Carleton University, 1125 Colonel By Drive Ottawa, Ontario, CANADA

Dale Doreen Department of Decision Sciences and Management Information Systems John Molson School of Business Concordia University 1455 de Maisonneuve Blvd. West Montréal, Québec, CANADA

Abstract

One of the most important aspects of credit scoring is constructing a model that has low misclassification rates and is also flexible enough to allow for random variation. It is also well known that, when there are a large number of highly correlated variables as is typical in studies involving questionnaire data, a method must be found to reduce the number of variables to those that have high predictive power. Here we propose a Bayesian multivariate logistic regression model with both fixed and random effects for small business loan credit scoring and a variable reduction method using Bayes factors. The method is illustrated on an interesting data set based on questionnaires sent to loan officers in Canadian banks and venture capital companies.

Publication Details

Page(s) 433-449

DOI DOI not available

Published Journal: Pakistan Journal of Statistics and Operation Research, Volume: 7, Issue: 2, Year: 2011

Keywords

Variable selection MCMC Credit Scoring Bayes Factors

References

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Citations

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