Scoring Bank Loans that may go wrong: A Case Study

A bank employs logistic regression with state-dependent sample selection to identify loans thatmay go wrong. Inspection shows that the logit model is inappropriate. A bounded logit model witha ceiling of (far) less than 1 fits the data much better

Ordered logit analysis for selectively sampled data

When customers are classified into ordered categories, which are defined from the outset, it may happen that the majority belongs to a single category. If a market researcher is interested in the correlation between the classification and individual characteristics, the natural question is whether one needs to collect data for all customers in that particular category. We address this question for the ordered logit model. We show that there is no need to consider all those customers. All that is required is a simple modification of the log-likelihood, which is based on Bayes' rule. We illustrate our proposed method on simulated data and on data concerning risk profiles of customers of an investment bank.ordered logit model;selective sampling;Bayes' rule

On the number of categories in an ordered regression model

We show that there is no formal statistical testing method to combine categories in a standard ordered regression model. We discuss practical implications of this result.Number of categories;Ordered regression model