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A transform-both-sides modulus power model: an application in health care

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  • Howard Tuckman
  • Cyril Chang
  • Albert Okunade

Abstract

The Box-Cox transformation is suitable for stabilizing variance of the response variable and for inducing functional form flexibility in single-equation regression models. However, it becomes incapacitated if the data contain zero or negative values. This paper, using profitability data for US psychiatric hospitals, illustrates the capability of modulus power transformations to symmetrize the response variable distribution. Compared with the untransformed data model, the ML estimates of the modulus power model are found to be superior.

Suggested Citation

  • Howard Tuckman & Cyril Chang & Albert Okunade, 1999. "A transform-both-sides modulus power model: an application in health care," Applied Economics Letters, Taylor & Francis Journals, vol. 6(11), pages 741-745.
    • Handle: RePEc:taf:apeclt:v:6:y:1999:i:11:p:741-745
    DOI: 10.1080/135048599352321
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    Cited by:

    1. Z. L. Yang & Y. K. Tse, 2006. "Modelling firm‐size distribution using Box–Cox heteroscedastic regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 641-653, July.
    2. Thierry Nianogo & Albert Okunade & Demba Fofana & Weiwei Chen, 2016. "Determinants of US Prescription Drug Utilization using County Level Data," Health Economics, John Wiley & Sons, Ltd., vol. 25(5), pages 606-619, May.

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