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      Estimating log models: to transform or not to transform?

      1 ,
      Journal of health economics
      Elsevier BV

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          Abstract

          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.

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          Author and article information

          Journal
          J Health Econ
          Journal of health economics
          Elsevier BV
          0167-6296
          0167-6296
          Jul 2001
          : 20
          : 4
          Affiliations
          [1 ] Department of Health Studies, Harris School of Public Policy Studies, The University of Chicago, IL 60637, USA. w-manning@uchicago.edu
          Article
          S0167629601000868
          10.1016/s0167-6296(01)00086-8
          11469231
          48779da6-2e0e-4a7a-9620-e3f0126c738a
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