 Models and forecasts of credit card balancePak Shun Hon and Tony Bellotti European Journal of Operational Research, 2016, vol. 249, issue 2, 498-505 Abstract: Credit card balance is an important factor in retail finance. In this article we consider multivariate models of credit card balance and use a real dataset of credit card data to test the forecasting performance of the models. Several models are considered in a cross-sectional regression context: ordinary least squares, two-stage and mixture regression. After that, we take advantage of the time series structure of the data and model credit card balance using a random effects panel model. The most important predictor variable is previous lagged balance, but other application and behavioural variables are also found to be important. Finally, we present an investigation of forecast accuracy on credit card balance 12 months ahead using each of the proposed models. The panel model is found to be the best model for forecasting credit card balance in terms of mean absolute error (MAE) and the two-stage regression model performs best in terms of root mean squared error (RMSE). Keywords: Credit cards; Balance estimation; Mixture model; Panel model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:249:y:2016:i:2:p:498-505 DOI: 10.1016/j.ejor.2014.12.014 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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