We generalize an empirical likelihood approach to missing data to the case of consumer credit scoring and provide a Hausman test for nonignorability of the missings. An application to recent consumer credit data shows that our model yields parameter estimates which are significantly different (both statistically and economically) from the case where customers who were refused credit are ignored.

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SummaryWe propose a new test against a change in the probability of multivariate tail events. The test is based on partial sums of a suitably defined indicator function and detects abrupt changes in joint tail probabilities better than a previously suggested competitor.

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