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The Box-Cox Transformation-of-Variables in Regression

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  • Kim, Minbo
  • Hill, R Carter

Abstract

The application of the Box-Cox transformation to the dependent and independent variables is discussed. Maximum likelihood and iterative GLS estimators are used and bootstrapping is carried out to compare the bootstrap sample variability with the finite sample variability (RMSE) and improve RMSE estimation. The biases of parameter estimators were shown to be substantial in small samples. The standard errors obtained from the Hessian matrix were a poor measure of the finite sample variability. The "t"-ratios of the linear parameter estimators may not be normally distributed in small samples.

Suggested Citation

  • Kim, Minbo & Hill, R Carter, 1993. "The Box-Cox Transformation-of-Variables in Regression," Empirical Economics, Springer, vol. 18(2), pages 307-319.
  • Handle: RePEc:spr:empeco:v:18:y:1993:i:2:p:307-19
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    Cited by:

    1. James Bugden, 2013. "Renovations and the Repeat-Sales House Price Index," Working Papers 2013.08, School of Economics, La Trobe University.
    2. Wen-Jen Hsieh, 1995. "Test of variable output and scale elasticities for 20 US manufacturing industries," Applied Economics Letters, Taylor & Francis Journals, vol. 2(8), pages 284-287.

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