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Linear Rescaling to Accurately Interpret Logarithms

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  • Nick Huntington-Klein

Abstract

The standard approximation of a natural logarithm in statistical analysis interprets a linear change of \(p\) in \(\ln(X)\) as a \((1+p)\) proportional change in \(X\), which is only accurate for small values of \(p\). I suggest base-\((1+p)\) logarithms, where \(p\) is chosen ahead of time. A one-unit change in \(\log_{1+p}(X)\) is exactly equivalent to a \((1+p)\) proportional change in \(X\). This avoids an approximation applied too broadly, makes exact interpretation easier and less error-prone, improves approximation quality when approximations are used, makes the change of interest a one-log-unit change like other regression variables, and reduces error from the use of \(\log(1+X)\).

Suggested Citation

  • Nick Huntington-Klein, 2021. "Linear Rescaling to Accurately Interpret Logarithms," Papers 2106.03070, arXiv.org, revised Oct 2021.
  • Handle: RePEc:arx:papers:2106.03070
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    1. Halvorsen, Robert & Palmquist, Raymond, 1980. "The Interpretation of Dummy Variables in Semilogarithmic Equations," American Economic Review, American Economic Association, vol. 70(3), pages 474-475, June.
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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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