The Box-Cox Transformation: Computational and Interpretation Features of the Parameters

  • Daniel A. Griffith
  • Jean H. P. Paelinck
  • Reinaud A. van Gastel
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 35)


Recently attention has returned in spatial statistics and spatial econometrics to the Box-Cox and Box-Tidwell families of power transformations (e.g., van Gastel and Paelinck, 1995; Kobayashi and McAleer, 1995). In part this renewed interest anses from geo-referenced data sets almost always falling under the heading of small sample theory. In such cases design-based inference cannot rely upon the conventional central limit theorem, and model-based inference risks confusing specification and sampling error. Power transformations offer a remedy for retaining the symmetry, constant spread, and linearity assumptions typifying conventional statistical analyses. These transformations often preserve the target parameter, and produce a change in measurement scale that better characterizes the population under study. Power transformations are monotone--changing only the distance between successive numbers along a measurement scale--tend to offer modest gains in statistical efficiency and to introduce no difficulties with the consistency of inferences.


Spatial Autocorrelation Measurement Scale Power Transformation Spatial Econometric Bivariate Regression 
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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Daniel A. Griffith
  • Jean H. P. Paelinck
  • Reinaud A. van Gastel

There are no affiliations available

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