Finding correct elasticities in log-linear and exponential models allowing heteroskedasticity

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Log-linear models are popular in practice because the slope of a log-transformed regressor is believed to give an unit-free elasticity. This widely held belief is, however, not true if the model error term has a heteroskedasticity function that depends on the regressor. This paper examines various mean – and quantile-based elasticities (mean of elasticity, elasticity of conditional mean, quantile of elasticity, and elasticity of conditional quantile) to show under what conditions these are equal to the slope of a log-transformed regressor. A particular attention is given to the ‘elasticity of conditional mean (i.e., regression function)’, which is what most researchers have in mind when they use log-linear models, and we provide practical ways to find it in the presence of heteroskedasticity. We also examine elasticities in exponential models which are closely related to log-linear models. An empirical illustration for health expenditure elasticity with respect to income is provided to demonstrate our main findings.

Original languageEnglish
Pages (from-to)81-91
Number of pages11
JournalStudies in Nonlinear Dynamics and Econometrics
Issue number3
Publication statusPublished - 2020 Jun 1

Bibliographical note

Funding Information:
Research funding: This research has been supported by a Korea University Grant (K2009051).

Publisher Copyright:
© 2021 De Gruyter. All rights reserved.


  • Exponential model
  • Log-linear model
  • Mean elasticity
  • Quantile elasticity

ASJC Scopus subject areas

  • Analysis
  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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