Chapter 11. Categorical Dependent Variables

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 or not an event of interest has occurred. In actuarial and financial applications, the event may be whether a claim occurs, a person purchases insurance, a person retires or a firm becomes insolvent. The 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 may 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.

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