A computationally efficient correlated mixed probit model for credit risk inference
A computationally efficient correlated mixed probit model for credit risk inference
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DOI：
10.1111/rssc.12352
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Elisa Tosetti;V. Vinciotti
中科院分区：
文献类型：
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作者：
Elisa Tosetti;V. Vinciotti
混合概率单位模型广泛应用于对二元响应进行预测的许多领域。通常，随机效应被假定为是独立的，但对于许多真实的应用来说，很少是这样。在信用风险的应用，被认为是在文件中，随机效应存在于工业部门的水平，他们预计将相关，因为公司间的信贷联系诱导依赖于公司的违约风险。不幸的是，现有的相关混合概率模型的推理程序计算非常密集已经为中等数量的影响。借鉴大型网络推理的相关文献，提出了一种有效的无约束惩罚似然估计的期望最大化算法，并给出了估计的渐近标准误差.一个广泛的模拟研究表明，所提出的方法享有相当大的计算增益相对于标准的蒙特卡罗方法，同时仍然提供准确的参数估计。使用近64000帐户的数据在英国的中小企业在2013年在13个工业部门，我们发现，通过相关的混合概率模型的网络效应会计显着增加的默认预测能力的模型相比，传统的默认预测模型，使这些模型的有效推理程序在这一领域特别有用。
Mixed probit models are widely applied in many fields where prediction of a binary response is of interest. Typically, the random effects are assumed to be independent but this is seldom so for many real applications. In the credit risk application that is considered in the paper, random effects are present at the level of industrial sectors and they are expected to be correlated because of interfirm credit links inducing dependences in the firms’ risk to default. Unfortunately, existing inferential procedures for correlated mixed probit models are computationally very intensive already for a moderate number of effects. Borrowing from the literature on large network inference, we propose an efficient expectation–maximization algorithm for unconstrained and penalized likelihood estimation and derive the asymptotic standard errors of the estimates. An extensive simulation study shows that the approach proposed enjoys substantial computational gains relative to standard Monte Carlo approaches, while still providing accurate parameter estimates. Using data on nearly 64000 accounts for small and medium‐sized enterprises in the UK in 2013 across 13 industrial sectors, we find that accounting for network effects via a correlated mixed probit model increases significantly the default prediction power of the model compared with conventional default prediction models, making efficient inferential procedures for these models particularly useful in this field.