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首页> 外文期刊>Statistica neerlandica >Scoring bank loans that may go wrong: a case study
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Scoring bank loans that may go wrong: a case study

机译：计分可能出错的银行贷款：一个案例研究

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摘要

A bank employs logistic regression with state-dependent sample selection to identify loans that may go wrong. The data consist of some 20000 loans for which a number of conventional accounting ratios of the debtor firm are known; after two years just over 600 have gone wrong. Inspection shows that the state-dependent sampling technique does not work because the data do not satisfy the standard logit model. Several variants on this model are considered, and it is found that a bounded logit with a ceiling of (far) less than 1 fits the data better. When it comes to their performance in an independent data-set, however, the differences between the various methods of analysis are negligible.
机译：一家银行采用逻辑回归和基于状态的样本选择来确定可能出问题的贷款。数据包括约2万笔贷款，已知债务人公司的许多常规会计比率；两年后，有600多处出现问题。检查表明，状态依赖的采样技术不起作用，因为数据不满足标准的logit模型。考虑了该模型的几种变体，发现上限（远）小于1的有界logit更好地拟合了数据。但是，就其在独立数据集中的性能而言，各种分析方法之间的差异可以忽略不计。

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