Health expenditure estimation and functional form: applications of the generalized gamma and extended estimating equations models
利用美国医疗支出面板调查数据，比较广义伽马模型和扩展估计方程模型与其他常用回归模型在预测医疗支出时的偏差、准确性和边际效应，发现扩展估计方程模型因灵活连接函数而表现稳健。
Health-care expenditure regressions are used in a wide variety of economic analyses including risk adjustment and program and treatment evaluations. Recent articles demonstrated that generalized gamma models (GGMs) and extended estimating equations (EEE) models provide flexible approaches to deal with a variety of data problems encountered in expenditure estimation. To date there have been few empirical applications of these models to expenditures. We use data from the US Medical Expenditure Panel Survey to compare the bias, predictive accuracy, and marginal effects of GGM and EEE models with other commonly used regression models in a cross-validation study design. Health-care expenditure distributions vary in the degree of heteroskedasticity, skewness, and kurtosis by type of service and population. To examine the ability of estimators to address a range of data problems, we estimate models of total health expenditures and prescription drug expenditures for two populations, the elderly and privately insured adults. Our findings illustrate the need for researchers to examine their assumptions about link functions: the appropriate link function varies across our four distributions. The EEE model, which has a flexible link function, is a robust estimator that performs as well, or better, than the other models in each distribution.