Estimating log models: to transform or not to transform?

Health economists often use log models to deal with skewed outcomes, such a s health utilization or health expenditures. The literature provides a numb er of alternative estimation approaches for log models, including ordinary least-squares on In(y) and generalized linear models. This study examines h ow 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 fo r choosing among the alternative estimators. Even if the estimators conside red are consistent, there can be major losses in precision from selecting a less appropriate estimator. (C) 2001 Elsevier Science B.V. All rights rese rved.