Censored Gamma Regression Models For Limited Dependent Variables With An Application To Loss Given Default
Regression models for limited continuous dependent variables having a non-negligible probability of attaining exactly their limits are presented. The models differ in the number of parameters and in their flexibility. It is shown how to fit these models and they are applied to a Loss Given Default dataset from insurance to which they provide a good fit. In insurance, losses are frequently restricted to be positive and below an upper bound defined by a contract. The authors analyze a loss given default dataset from an insurance category called "Surety".