Solved – Why are time-related covariates log transformed in modelling?

I have seen a lot of examples of transforming time-related variables (e.g., age, year, days, etc.), but I don't understand the reasoning behind doing this. I don't think it's for stabilizing the variance (because it is a covariate). I suppose it's because of something related to linearity. This is even more confusing when the transformed covariate is in a mixed model.

Why are temporal variables often log transformed?

A common habit among economists is to take logs. Often times, this is done routinely or automatically without giving it much thought. It's true that taking logs can be used to, say, induce stable variance (see Luetkepohl & Xu, 2009) or to deal with outlying data, however, that's not necessarily the main reason behind taking logs.

The reason why many economists have developed this habit is simply for interpretability. Working in logs makes (some) models easier to interpret because a log transformed variable can be interpreted in terms of percentages or percentage change.

UCLA has a webpage with an example of this sort and there is a nice set of notes on logarithms in economics by Ron Mitchener.


Helmut Luetkepohl & Fang Xu, 2009. "The Role of the Log Transformation in Forecasting Economic Variables," CESifo Working Paper Series 2591, CESifo Group Munich.

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