The Role of the Log Transformation in Forecasting Economic Variables

Helmut Lütkepohl and Fang Xu

No 2591, CESifo Working Paper Series from CESifo

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.

Keywords: autoregressive moving average process; forecast mean squared error; instantaneous transformation; integrated process; heteroskedasticity (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Related works:
Journal Article: The role of the log transformation in forecasting economic variables (2012)
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