UCD GEARY INSTITUTE DISCUSSION PAPER SERIES Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults
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چکیده
The most used regression model with binary dependent variable is the logistic regression model. When the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particular, in a Generalized Linear Model (GLM) with binary dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure is the maximum likelihood method. This model accommodates skewness and it presents a generalization of GLMs with log-log link function. In credit risk analysis a pivotal topic is the default probability estimation. Since defaults are rare events, we apply the GEV regression to empirical data on Italian Small and Medium Enterprises (SMEs) to model their default probabilities.
منابع مشابه
Generalized Extreme Value Regression: an Application to Credit Defaults
We aim at proposing a Generalized Linear Model (GLM) with binary dependent variable Y , whose link function defined by the Generalized Extreme Value (GEV) distribution. We define this model as GEV regression. The goal of this paper is to overcome the drawbacks shown by the logistic regression in rare events: the probability of rare events is underestimated and the logit link is a symmetric func...
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تاریخ انتشار 2011