Instrumental-Variable Estimation of Count Data Models: Applications to Models of Cigarette Smoking Behavior
提出用非线性工具变量方法解决计数数据模型中未观测异质性与解释变量相关导致的估计不一致问题，并以吸烟行为为例展示应用。
As with most analyses involving microdata, applications of count data models must somehow account for unobserved heterogeneity. The count model literature has generally assumed that unobservables and observed covariates are statistically independent. Yet for many applications this independence assumption is clearly tenuous. When the unobservables are omitted variables correlated with included regressors, standard estimation methods will generally be inconsistent. Though alternative consistent estimators may exist in special circumstances, it is suggested here that a nonlinear instrumental-variable strategy offers a reasonably general solution to such estimation problems. This approach is applied in two examples that focus on cigarette smoking behavior. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology