COPULA BIVARIATE PROBIT MODELS: WITH AN APPLICATION TO MEDICAL EXPENDITURES
改进了双变量Probit模型，用连接函数引入非正态依赖关系，并在保险状态对门诊医疗支出影响的应用中发现Frank连接函数模型优于标准模型。
The bivariate probit model is frequently used for estimating the effect of an endogenous binary regressor (the 'treatment') on a binary health outcome variable. This paper discusses simple modifications that maintain the probit assumption for the marginal distributions while introducing non-normal dependence using copulas. In an application of the copula bivariate probit model to the effect of insurance status on the absence of ambulatory health care expenditure, a model based on the Frank copula outperforms the standard bivariate probit model.