期刊
JOURNAL OF HEALTH ECONOMICS
卷 24, 期 3, 页码 465-488出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jhealeco.2004.09.011
关键词
health econometrics; log models; generalized linear models; skewed outcomes
资金
- NIAAA NIH HHS [1R01 AA12664-01 A2] Funding Source: Medline
There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., ordinary least square (OLS) on In(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we encompass these two classes of models using the three parameter generalized Gamma (GGM) distribution, which includes several of the standard alternatives as special cases-OLS with a normal error, OLS for the log-normal, the standard Gamma and exponential with a log link, and the Weibull. Using simulation methods, we find the tests of identifying distributions to be robust. The GGM also provides a potentially more robust alternative estimator to the standard alternatives. An example using inpatient expenditures is also analyzed. (c) 2005 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据