Endogenous treatment effects for count data models with endogenous participation or sample selection
提出一种用最大模拟似然估计的方法，处理二元内生处理变量对计数结果的影响，同时解决样本选择或内生参与问题，并用医生建议对饮酒量的影响数据展示了忽视内生性会导致错误结论。
In this paper, we propose an estimator for models in which an endogenous dichotomous treatment affects a count outcome in the presence of either sample selection or endogenous participation using maximum simulated likelihood. We allow for the treatment to have an effect on the participation or the sample selection rule and on the main outcome. Applications of this model are frequent in-but no limited to-health economics. We show an application of the model using data from Kenkel and Terza (2001), who investigate the effect of physician advice on the amount of alcohol consumption. Our estimates suggest that in these data (i) neglecting treatment endogeneity leads to a wrongly signed effect of physician advice on drinking intensity, (ii) accounting for treatment endogeneity but neglecting endogenous participation leads to an upward biased estimate of the treatment effect and (iii) advice affects only the drinking intensive margin but not drinking prevalence.