A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health
开发了一个两方程Copula模型来解决医疗支出内生性问题，支出边际采用复合伽马分布处理零值堆积和右偏，应用兰德健康保险实验数据发现1000美元支出增加使低心理健康概率降低1.9个百分点但统计不显著，忽略内生性会导致虚假正向效应。
We develop a flexible two-equation copula model to address endogeneity of medical expenditures in a distribution regression for health. The expenditure margin uses the compound gamma distribution, a special case of the Tweedie family of distributions, to account for a spike at zero and a highly skewed continuous part. An efficient estimation algorithm offers flexible choices of copulae and link functions, including logit, probit and cloglog for the health margin. Our empirical application revisits data from the Rand Health Insurance Experiment. In the joint model, using random insurance plan assignment as instrument for spending, a $1000 increase is estimated to reduce the probability of a low post-program mental health index by 1.9 percentage points. The effect is not statistically significant. Ignoring endogeneity leads to a spurious positive effect estimate.