This paper examines a flexible way to model empirically discrete data outcomes using hazard rate decompositions. It presents a general data-generating mechanism based on potential outcomes to describe why the approach should work for almost any discrete distribution. Monte Carlo evidence indicates that these models estimate well the impacts of covariates on expected counts when the data follow a Poisson distribution. With data from more complex processes, these estimators continue to perform well. Since most economic count outcomes arise from occurrence-dependent behavioral processes, using flexibly estimated distributions should reduce the dependence of results on convenient but invalid assumptions.
