Estimating log models: to transform or not to transform?
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 ln(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.
Manning
W G
WG
Department of Health Studies, Harris School of Public Policy Studies, The University of Chicago, IL 60637, USA. w-manning@uchicago.edu
Mullahy
J
J
D019033:Delivery of Health Care, Integrated / Q000191:economics*; D005102:Health Expenditures / Q000706:statistics & numerical data; D006302:Health Services Research / Q000191:economics / Q000379:methods; D006801:Humans; D016015:Logistic Models; D017059:Models, Econometric; D014481:United States