**Inflation adjustment**, or
"deflation", is accomplished
by *dividing a monetary time series by a price index*, such as
the
Consumer Price Index (CPI). The
deflated series is then said to be measured
in "constant dollars," whereas the original series was
measured in "nominal dollars" or "current dollars."
Inflation is often a significant component of apparent growth
in any series measured in dollars (or yen, euros, pesos, etc.).
By adjusting for inflation, you uncover the real growth, if any.
You also may stabilize the variance of random or seasonal fluctuations
and/or
highlight cyclical patterns in the data. Inflation-adjustment is
not always necessary when dealing with monetary variables--sometimes it
is simpler to forecast the data in nominal terms or to use a logarithm transformation for stabilizing the
variance--but it is an important tool in the toolkit for analyzing
economic data.

The Consumer Price Index is
probably the best known US price index, but other
price indices may be appropriate for some data. The Producer Price
Index and the GDP Implicit
Price Deflator are some
other commonly used indices, and numerous industry-specific indices
are also available. The U.S. Bureau of Economic Analysis compiles
a wide array if "chain-type" price indices for various kinds of
personal consumption goods. A chain-type index is one that is
obtained by chaining together monthly, quarterly, or annual changes in
relative prices that are adjusted for changes in the composition of the
commodity basket, so as to reflect changes in consumer tastes.
(For more details on chain-type indices, see the following article.)

Use of the "correct" price
index is important if you are interested in knowing the exact
magnitudes of trends in real terms and/or if the relevant price history
has undergone sudden jumps or significant changes in trend rather than
consistent increases over time. However, deflation by a
general-purpose index such as the CPI is often adequate for rough
estimates of trends in real terms when doing exploratory data analysis
or when fitting a forecasting model that adapts to changing trends
anyway. Keep in mind that when you deflate a sales or consumer
expenditures series by a general index such as the CPI, you are not
necessarily converting from dollars spent to units sold or consumed,
rather, you are converting from dollars spent on one type of good to
equivalent quantities of other
consumer goods (e.g., hamburgers and hot dogs) that could have
been purchased with the same money. Sometimes this is of interest
in its own right because it reveals growth in relative terms (i.e.,
relative to the other goods).

Here is a graph of the auto sales in nominal dollars plotted alongside the CPI over the last 25 years, where the CPI has been scaled so that the January 1990 value is 1.0.

Now here is a graph of auto sales divided by (i.e. deflated by) the CPI. Note that much (though not all) of the upward trend has been removed, accentuating the seasonal and cyclical components of the data. The recessionary periods in the mid-1970's, early 1980's, and early 1990's are especially evident:

Notice that to perform the inflation adjustment in Statgraphics, you merely have to type the expression AUTOSALE/CPI as the input variable (assuming that AUTOSALE and CPI are pre-existing variables). If you wanted to perform the same calculation on a spreadsheet, it would look like this:

When looking at descriptions of
time series in Economagic or other data sources,
the identifier "$" or "dollars" means the
series is in *nominal* dollars (i.e.,* not*
inflation-adjusted).
An identifier such as "1990 dollars" or "1990 $"
means that the series is in *constant* (inflation-adjusted)
dollars, with 1990 taken as the reference point. For modeling
purposes, the choice of a reference point doesn't matter, since
changing the reference point merely multiplies or divides the
whole series by a constant. To move the reference point to a different
base year, you would just divide the whole price index series
by the current value of the index at the desired reference date.
However, the parameters of a model are easier to interpret if
the *same* reference point is used for all inflation adjustments.
The thing you wish to avoid at all costs is having some variables
which *are* inflation adjusted and others which *aren't*:
this will introduce apparent nonlinear relationships which are
merely artifacts of inconsistent units.

The following spreadsheet illustrates how you could adjust the auto sales series to 1970 dollars instead of 1990 dollars. First, the CPI series is divided through by its original value in January 1970, to obtain a new consumer price index series called CPI70 in which the 1970 value is equal to 1.0. Then the auto sales series is divided by the CPI70 index:

Of course, the graph of the auto sales series in 1970 dollars would look identical to the graph in 1990 dollars: only the axis scale numbers would change.

Finally, remember that
inflation adjustment is only appropriate for
series which are measured in units of money: *if the series
is measured in number of widgets produced or hamburgers served
or percent interest, it makes no sense to deflate*. If a non-monetary
series nonetheless shows signs of exponential growth or increasing
variance, it may be useful to try a logarithm
transformation instead.