A semiparametric method for predicting bankruptcy
AbstractBankruptcy prediction methods based on a semiparametric logit model are proposed for simple random (prospective) and case-control (choice-based; retrospective) data. The unknown parameters and prediction probabilities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. The semiparametric bankruptcy prediction methods using these two types of data are shown to be essentially equivalent. Thus our proposed prediction model can be directly applied to data sampled from the two important designs. One real data example and simulations confirm that our prediction method is more powerful than alternatives, in the sense of yielding smaller out-of-sample error rates. Copyright © 2007 John Wiley & Sons, Ltd.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 26 (2007)
Issue (Month): 5 ()
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
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- Hwang, Ruey-Ching, 2012. "A varying-coefficient default model," International Journal of Forecasting, Elsevier, vol. 28(3), pages 675-688.
- Harada, Nobuyuki & Kageyama, Noriyuki, 2011. "Bankruptcy dynamics in Japan," Japan and the World Economy, Elsevier, vol. 23(2), pages 119-128, March.
- Hwang, Ruey-Ching & Chung, Huimin & Chu, C.K., 2010. "Predicting issuer credit ratings using a semiparametric method," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 120-137, January.
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