HETEROGENEITY, EXCESS ZEROS, AND THE STRUCTURE OF COUNT DATA MODELS

This paper demonstrates that the unobserved heterogeneity commonly ass umed to be the source of overdispersion in count data models has predi ctable implications for the probability structure of such mixture mode ls. In particular, the common observation of excess zeros is a strict implication of unobserved heterogeneity. This result has important imp lications for using count model estimates for predicting certain inter esting parameters. Test statistics to detect such heterogeneity-relate d departures from the null model are proposed and applied in a health- care utilization example, suggesting that a null Poisson model should be rejected in favour of a mixed alternative. (C) 1997 by John Wiley & Sons, Ltd.