Positivity requirement and choice of base
First difference of natural log ≈ percentage change
The poor man's deflator
Trend measured in natural-log units ≈ percentage growth
Errors measured in natural-log units ≈ percentage errors
Linearization property: The logarithm function has the defining property that LOG (X*Y) = LOG(X) + LOG(Y)--i.e., the logarithm of a product equals the sum of the logarithms. Therefore, logging tends to convert multiplicative relationships to additive relationships, and it tends to convert exponential (compound growth) trends to linear trends. By taking logarithms of variables which are multiplicatively related and/or growing exponentially over time, we can often explain their behavior with linear models. For example, here is a graph of LOG(AUTOSALE). Notice that the log transformation converts the exponential growth pattern to a linear growth pattern, and it simultaneously converts the multiplicative (proportional-variance) seasonal pattern to an additive (constant-variance) seasonal pattern. (Compare this with the original graph of AUTOSALE.)
Positivity requirement and choice of base: The logarithm transformation can be applied only to data which are strictly positive--you can't take the log of zero or a negative number! Also, there are two kinds of logarithms in standard use: "natural" logarithms and base-10 logarithms. The only difference between the two is a scaling constant, which is not really important for modeling purposes. In Statgraphics, the LOG function is the natural log, and its inverse is the EXP function. (EXP(Y) is the natural logarithm base, 2.718..., raised to the Yth power.) The base-10 logarithm and its inverse are LOG10 and EXP10 in Statgraphics. However, in standard mathematical notation, and in Excel and most other analytic software, the natural logarithm function is written as LN instead, and LOG stands instead for the base-10 logarithm. For example, in Excel the expression =LN(X) computes the natural log of X.
The natural logarithm and its base number e have some magical properties, which you may remember from calculus. For example, the function eX is its own derivative, and the derivative of LN(X) is 1/X. But for purposes of business analysis, its great advantage is that small changes in the natural log of a variable are directly interpretable as percentage changes in the original variable, to a very close approximation. The reason for this is that the graph of Y = LN(X) passes through the point (1, 0) and has a slope of 1 there, so it is tangent to the straight line whose equation is Y = X-1 (the dashed line in the plot below):
This property of the natural log function implies that
LN(1+r) ≈ r
when r is much smaller than 1 in magnitude. Why is this important? Suppose X increases by a small percentage, such as 5%. This means that it changes from X to X(1+r), where r = 0.05. Now observe:
LN(X (1+r)) = LN(X) + LN(1+r) ≈ LN(X) + r
Thus, when X is increased by 5%, i.e., multiplied by a factor of 1.05, the natural log of X changes from LN(X) to LN(X) + 0.05, to a very close approximation. Thus, increasing X by 5% is equivalent to adding 0.05 to LN(X).
From now on I will refer to changes in natural logarithms as “diff-logs.” (In Statgraphics, the diff-log transformation of X is literally DIFF(LOG(X)).) The following table shows the exact correspondence for percentages in the range from -50% to +100%:
As you can see, percentage changes and diff-logs are almost exactly the same within the range +/- 5%, and they remain very close up to +/- 20%. For large percentage changes they begin to diverge in an asymmetric way. Note that the diff-log that corresponds to a 50% decrease is ‑0.693 while the diff-log of a 100% increase is +0.693, exactly the opposite number. This reflects the fact that a 50% decrease followed by a 100% increase (or vice versa) takes you back to the same spot.
First difference of natural log ≈ percentage change: When used in conjunction with differencing, logging converts absolute differences into relative (i.e., percentage) differences, when these differences are small. Thus, in Statgraphics, the series DIFF(LOG(Y)) represents the percentage change in Y from period to period. Strictly speaking, the percentage change in Y at period t is defined as (Y(t)-Y(t-1))/Y(t-1), which is only approximately equal to LOG(Y(t)) - LOG(Y(t-1)), but the approximation is almost exact if the percentage change is small. In Statgraphics terms, this means that DIFF(Y)/LAG(Y,1) is virtually identical to DIFF(LOG(Y)). If you don't believe me, here's a plot of the percent change in auto sales versus the first difference of its logarithm, zooming in on the last 5 years. The blue and red lines are virtually indistinguishable except at the highest and lowest points.
situation is one in which the percentage changes are potentially large enough
for this approximation to be inaccurate, it is better to use log units rather
than percentage units, because this takes compounding into account in a
(Return to top of page.)
The poor man's deflator: Logging a series often has an effect very similar to deflating: it dampens exponential growth patterns and reduces heteroscedasticity (i.e., stabilizes variance). Logging is therefore a "poor man's deflator" which does not require any external data (or any head-scratching about which price index to use). Logging is not exactly the same as deflating--it does not eliminate an upward trend in the data--but it can straighten the trend out so that it can be better fitted by a linear model. (Compare the logged auto sales graph with the deflated auto sales graph.)
If you're going to log the data and then fit a model that implicitly or explicitly uses differencing (e.g., a random walk, exponential smoothing, or ARIMA model), then it is usually redundant to deflate by a price index, as long as the rate of inflation changes only slowly: the percentage change measured in nominal dollars will be nearly the same as the percentage change in constant dollars. Mathematically speaking, DIFF(LOG(Y/CPI)) is nearly identical DIFF(LOG(Y)): the only difference between the two is a very faint amount of noise due to fluctuations in the inflation rate. To demonstrate this point, here's a graph of the first difference of logged auto sales, with and without deflation:
By logging rather than deflating, you avoid the need to incorporate an explicit forecast of future inflation into the model: you merely lump inflation together with any other sources of steady compound growth in the original data. Logging the data before fitting a random walk model yields a so-called geometric random walk--i.e., a random walk with geometric rather than linear growth. A geometric random walk is the default forecasting model that is commonly used for stock price data. (Return to top of page.)
measured in natural-log units ≈ percentage growth: Because changes in the natural logarithm are (almost) equal
to percentage changes in the original series, it follows that the slope
of a trend line fitted to logged data is equal to the average percentage
growth in the original series. For example, in the graph of LOG(AUTOSALE) shown above, if you "eyeball" a
trend line you will see that the magnitude of logged auto sales increases by
about 2.5 (from 1.5 to 4.0) over 25 years, which is an average increase of
about 0.1 per year, i.e., 10% per year. It is much easier to
estimate this trend from the logged graph than from the original unlogged
one! The 10% figure obtained here is nominal growth, including
inflation. If we had instead eyeballed a trend line on a plot of logged deflated
sales, i.e., LOG(AUTOSALE/CPI), its slope would be the average real percentage
Usually the trend is estimated more precisely by fitting a statistical model that explicitly includes a local or global trend parameter, such as a linear trend or random-walk-with-drift or linear exponential smoothing model. When a model of this kind is fitted in conjunction with a log transformation, its trend parameter can be interpreted as a percentage growth rate.
Errors measured in natural-log units ≈ percentage errors: Another interesting property of the logarithm is that errors in predicting the logged series can be interpreted as percentage errors in predicting the original series, albeit the percentages are relative to the forecast values, not the actual values. (Normally one interprets the "percentage error" to be the error expressed as a percentage of the actual value, not the forecast value, athough the statistical properties of percentage errors are usually very similar regardless of whether the percentages are calculated relative to actual values or forecasts.)
Thus, if you use least-squares estimation to fit a linear forecasting model to logged data, you are implicitly minimizing mean squared percentage error, rather than mean squared error in the original units--which is probably a good thing if the log transformation was appropriate in the first place. And if you look at the error statistics in logged units, you can interpret them as percentages. For example, the standard deviation of the errors in predicting a logged series is essentially the standard deviation of the percentage errors in predicting the original series, and the mean absolute error (MAE) in predicting a logged series is essentially the mean absolute percentage error (MAPE) in predicting the original series.
Coefficients in log-log regressions ≈ proportional percentage changes: In many economic situations (particularly price-demand relationships), the relationship between variables is linear in terms of percentage changes rather than absolute changes. In such cases, applying a natural log or diff-log transformation to both dependent and independent variables may be appropriate. This issue will be discussed in more detail in the regression chapter of these notes.
Statgraphics tip: In the Forecasting procedure in Statgraphics, the error statistics shown on the Model Comparison report are all in untransformed (i.e., original) units to facilitate a comparison among models, regardless of whether they have used different transformations. (This is a very useful feature of the Forecasting procedure--in most stat software it is hard to get a head-to-head comparison of models with and without a log transformation.) However, whenever a regression model or an ARIMA model is fitted in conjunction with a log transformation, the standard-error-of-the-estimate or white-noise-standard-deviation statistics on the Analysis Summary report refer to the transformed (logged) errors, in which case they are essentially the RMS percentage errors. (Return to top of page.)