A simple, flexible estimator for count and other ordered discrete data
提出一种基于风险率分解的灵活建模方法，适用于几乎任何离散分布，蒙特卡洛证据表明在泊松分布下能良好估计协变量对期望计数的影响，且对更复杂过程也表现稳健。
SUMMARY 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. Copyright © 2010 John Wiley & Sons, Ltd.