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Published online by Cambridge University Press: 05 June 2012
Chapter Preview. A model with a categorical dependent variable allows one to predict whether an observation is a member of a distinct group or category. Binary variables represent an important special case; they can indicate whether an event of interest has occurred. In actuarial and financial applications, the event may be whether a claim occurs, whether a person purchases insurance, whether a person retires or a firm becomes insolvent. This chapter introduces logistic regression and probit models of binary dependent variables. Categorical variables may also represent more than two groups, known as multicategory outcomes. Multicategory variables can be unordered or ordered, depending on whether it makes sense to rank the variable outcomes. For unordered outcomes, known as nominal variables, the chapter introduces generalized logits and multinomial logit models. For ordered outcomes, known as ordinal variables, the chapter introduces cumulative logit and probit models.
Binary Dependent Variables
We have already introduced binary variables as a special type of discrete variable that can be used to indicate whether a subject has a characteristic of interest, such as sex for a person or ownership of a captive insurance company for a firm. Binary variables also describe whether an event of interest, such as an accident, has occurred. A model with a binary dependent variable allows one to predict whether an event has occurred or a subject has a characteristic of interest.
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