Testing distributional assumptions in CUB models for the analysis of rating data

Abstract

In this paper, we propose a portmanteau test for misspecification in combination of uniform and binomial (CUB) models for the analysis of ordered rating data. Specifically, the test we build belongs to the class of information matrix (IM) tests that are based on the information matrix equality. Monte Carlo evidence indicates that the test has excellent properties in finite samples in terms of actual size and power versus several alternatives. Differently from other tests of the IM family, finite-sample adjustments based on the bootstrap seem to be unnecessary. An empirical application is also provided to illustrate how the IM test can be used to supplement model validation and selection.