forecast horizons. JOCCI ranked first
at the 3-month and 6-month horizons,
and SMPS took the lead at the 12-
month horizon. Although CRB did
better than the no-indicator model in
the short run, its forecasting ability
deteriorated at longer forecast hori-
zons. The results in figure 2 seem to
indicate that JOCCI and SMPS im-
proved the performance of the fore-
casting model, since they succeeded
in lowering the average forecast error.
However, the differences between the
forecast errors of the no-indicator
model and the forecast errors of the
indicator models were very small,
averaging less than one-tenth of a
percentage point. Such a small im-
provement in the forecast error seems
insignificant when we consider that
between January 1970 and June 1994
the annual inflation rate ranged from
approximately 2% to over 12%. As
figure 2 shows, we also calculated
significance levels to measure the
probability that the mean of the dif-
ferences of the squared forecast er-
rors was actually zero. Values above
0.05 indicate that the average differ-
ences between the forecast errors
were so small that they are likely to be
truly zero in the long run and hence
insignificant. Conversely, values be-
low 0.05 indicate that we can reject
the hypothesis that the mean of the
differences is zero. In the latter case,
we would consider the improvement
in the forecast significant. Only the
SMPS model reduced the forecast
error by any statistically significant
amount, and then only at the 12-
month horizon.
Figure 3 allows a visual check of how
similar the forecast errors from the
various models truly are over time.
The chart depicts the difference be-
tween actual inflation and forecasts of
inflation at 12-month horizons (fore-
cast errors) produced by the no-indi-
cator and indicator models from Janu-
ary 1970 to June 1994. It is clear that
with only a few minor exceptions, the
path of forecast errors from the three
indicator models (depicted by the
shaded band in the figure) is almost
identical to the path of forecast errors
from the no-indicator model. This
shows that the difference between the
forecast errors tends to average zero
over the time period. It also shows
that the size of the forecast errors
from all of the models is very similar.
Clearly, commodity-based indicators
appear to add no valuable informa-
tion to that already provided by past
inflation.
Conclusion
Economic indicators have value only
to the extent that they possess unique
and independent information. In
addition, they can be useful forecast-
ing tools if they reliably and consis-
tently satisfy the purpose for which
they were designed. The three com-
modity price indexes we analyzed
were all created to measure anticipat-
ed inflation. Yet our findings show
that they don’t do any better than the
past history of prices. That is, even
though CRB, JOCCI, and SMPS con-
tain some qualitative information on
price movements, they possess no
unique information for measuring
changes in inflation. Although these
indexes fail in their role as forecasters
of inflation, they still provide valuable
real-time information on aggregate
price movements. The task of the
sophisticated analyst is to interpret
these movements carefully in light of
the compositional problems that char-
acterize commodity-based indicators.
—Francesca Eugeni and
Joel Krueger
1Francesca Eugeni, Charles Evans, and
Steven Strongin, “Commodity-based indica-
tors: Separating the wheat from the chaff,”
Chicago Fed Letter, No. 75, November 1993.
2The Commodity Research Bureau Futures
Price Index (1967=100) is compiled by the
Commodity Research Bureau, Inc., Chica-
go. TheJournal of Commerce Industrial
Price Index (1980=100) is compiled by the
Center for International Business Cycle
Research at Columbia University, New
York. The Change in Sensitive Materials
Prices (1987=100) is calculated as the
moving average of the monthly changes in
the Index of Sensitive Materials Prices,
which is compiled by the U.S. Department
of Commerce, U.S. Department of Labor,
and Commodity Research Bureau, Inc.
3Robert S. Pindyck and Julio J. Rotemberg,
“The excess co-movement of commodity
prices,” National Bureau of Economic
Research, Washington, DC, working paper,
No. 2671, July 1988.
4Our forecasts were out of sample and were
recursively estimated using Kalman filter-
ing techniques from January 1970 to June
1994. The full sample period was January
1963 to June 1994.
David R. Allardice, Vice President and Director
of Regional Econ omic Programs and Statistics;
Janice Weiss, Editor.
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