Estimating log models: to transform or not to transform?
Estimating log models: to transform or not to transform?
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DOI：
10.1016/s0167-6296(01)00086-8
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发表时间：
2001-07-01
影响因子：
3.5
通讯作者：
Mullahy, J
中科院分区：
文献类型：
--
作者：
Manning, WG;Mullahy, J
卫生经济学家经常使用对数模型来处理歪斜的结果，如卫生利用或卫生支出。文献为对数模型提供了许多可选的估计方法，包括In(y)上的普通最小二乘和广义线性模型。本研究考察了当数据偏斜或有其他常见数据问题（异方差、重尾等）时，替代估计器在计量经济学上的偏差和精度表现如何。没有一种选择在所有条件下都是最好的。本文提供了一个简单的算法来选择备选估计量。即使所考虑的估计量是一致的，也可能因为选择不太合适的估计量而导致精度上的重大损失。(C) 2001 Elsevier Science B.V.版权所有
Health economists often use log models to deal with skewed outcomes, such as health utilization or health expenditures. The literature provides a number of alternative estimation approaches for log models, including ordinary least-squares on In(y) and generalized linear models. This study examines how well the alternative estimators behave econometrically in terms of bias and precision when the data are skewed or have other common data problems (heteroscedasticity, heavy tails, etc.). No single alternative is best under all conditions examined. The paper provides a straightforward algorithm for choosing among the alternative estimators. Even if the estimators considered are consistent, there can be major losses in precision from selecting a less appropriate estimator. (C) 2001 Elsevier Science B.V. All rights reserved.