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The role of the log transformation in forecasting economic variables

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  • Helmut Lütkepohl
  • Fang Xu

Abstract

For forecasting and economic analysis many variables are used in logarithms (logs). In time series analysis this transformation is often considered to stabilize the variance of a series. We investigate under which conditions taking logs is beneficial for forecasting. Forecasts based on the original series are compared to forecasts based on logs. It is found that it depends on the data generation process whether the former or the latter are preferable. For a range of economic variables substantial forecasting improvements from taking logs are found if the log transformation actually stabilizes the variance of the underlying series. Using logs can be damaging for the forecast precision if a stable variance is not achieved.
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Suggested Citation

  • Helmut Lütkepohl & Fang Xu, 2012. "The role of the log transformation in forecasting economic variables," Empirical Economics, Springer, vol. 42(3), pages 619-638, June.
    • Handle: RePEc:spr:empeco:v:42:y:2012:i:3:p:619-638
    DOI: 10.1007/s00181-010-0440-1
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    More about this item

    Keywords

    Autoregressive moving average process; Forecast mean squared error; Instantaneous transformation; Integrated process; Heteroskedasticity; C22;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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