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Federal Reserve Bank of Cleveland
Economic Review
July 1980
ISSN 0013-0281
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July 1980
Economic Review
Federal Reserve Bank of Cleveland
Contents
Interpreting Movements in Seasonally Adjusted
Money-supply D a ta ........................................................1
Since policymakers need to evaluate money-growth rates between
dates of different seasons, they must estimate what portion of
money growth is seasonally transient and what portion is enduring.
Unfortunately, the process o f identifying the seasonal component
of a time series is not a perfect one, and it may introduce errors
into the interpretation o f the raw data. Alternative seasonal adjust
ment methods are applied to recent money supply data to illus
trate this interpretation problem. Particular attention is paid to
the influence of the income tax season on money supply changes.
Monitoring the Economy: Survey of Fourth District
Manufacturers.................................................................7
Monitoring changes in regional manufacturing activity is one
means o f analyzing current business-cycle conditions. Since 1965,
the Federal Reserve Bank of Cleveland has been surveying manu
facturing firms within the Fourth Federal Reserve District on a
monthly basis to ascertain basic conditions of production activity.
This article describes the survey and the cyclical properties of the
indexes derived from the survey and provides a statistical analysis
of the performance o f the index over the last ten years.
Interpreting Movements
in Seasonally Adjusted
Money-supply Data
by John B. Carlson, Paulette M. Maclin, and Mark S. Snkjerman
Why Seasonal Adjustment?
Many economic time series commonly are adjusted
for seasonal fluctuations. Seasonal adjustment is
designed to eliminate movements in a time series
that result only from seasonal events, such as holiday
shopping and regularly occurring weather changes.
Variations in the money supply are influenced
strongly by income-tax filing and Treasury disburse
ment patterns. Tax refunds are held temporarily
in checking accounts, while those who owe taxes
usually fund their accounts just before the payment
date. Both actions increase the narrowly defined
money supply. All seasonal-adjustment techniques
are designed essentially to “ redistribute” the un
adjusted (raw) data throughout a year in a pattern
that differs from actual observance. The resulting
seasonally adjusted data follow a pattern that should
no longer be influenced by seasonal events. With
seasonality thus neutralized, the basic movement
in the data becomes more readily apparent.
If policymakers were interested only in com
paring the current money-supply level with the
level o f the same season one year earlier, the raw
figures frequently would be sufficient. Since policy
makers need to evaluate money-growth rates between
dates of different seasons, they must estimate the
seasonal influences and take them into account when
assessing the changes in the actual money-supply
figures. The convenience o f employing seasonally
adjusted data is not necessarily costless, however.
Often, the seasonal influences cannot be accurately
estimated, for a variety of reasons. As a practical
m atter, the correct adjustment process for each data
series can never be known with certainty in advance.
An improper set of seasonal-adjustment factors
introduces errors into the interpretation of the raw
data, and the errors are likely to be inversely related
to the length of the time interval being considered.
For example, the seasonal component of weekly
money-supply data is more difficult to ascertain than
the seasonal component of quarterly money-supply
data. In addition, the money supply is an aggregate of
parts, such as currency and checking accounts, each
of which has its own seasonal process. Failure to
account adequately and separately for each process
may introduce error.
If seasonal processes are considered to be
basically stable, the seasonal pattern would be ex
tracted from a raw-data series by a technique that
treats each year of the series with equal weight.
But, if seasonal processes are expected to change
frequently and along a trend, the seasonal pattern
would be extracted by a technique that gives more
weight to the most recent years in the data series.
Some analysts argue that changes in seasonal processes
cannot be determined until several years after the
fact; these analysts would claim that stable seasonal
ity ought to be presumed when adjusting current
data, unless overwhelming evidence to the contrary
exists. Other analysts are confident that unequal
weighting of the years in a data series can be jus
tified, based on information about changes in the
underlying determinants of the seasonal process.
One’s interpretation of a raw-data series there
fore is conditioned by the “prescription o f the
eyeglasses” that one wears. The raw data will appear
differently when viewed through different seasonaladjustment lenses. The analyst’s problem stems
from the fact that there is no single procedure known
to be correct. Analysts who differ in the assumptions
regarding the appropriate seasonal influences on the
money supply consequently may differ in their in
terpretations of current money-supply growth. More
over, the differences introduced by seasonal-adjustment techniques can be sizable. An examination of
the seasonal factors used to adjust M-1A for the
1972-1979 period appears in table 1. The data
indicate that the February-July factors have become
more volatile over time, while the August-January
factors have become less volatile. The intra-period
means have been very stable, suggesting that changes
in m onthly factors within a period have been entirely
offsetting. The consequence of these developments
is that a constantly growing raw-money supply would
appear, on a seasonally adjusted basis, to change first
more sharply, then less sharply, in 1979 than in 1972.
Table 1 Means and Standard Deviations
in M-1A Seasonals: 1972-1979
February-July
Standard
Mean deviation
1979
1978
1977
1976
1975
1974
1973
1972
99.39
99.38
99.44
99.43
99.34
99.35
99.26
99.23
1.16
1.07
0.86
0.85
0.77
0.79
0.75
0.73
August-January
Mean
100.76
100.80
100.72
100.71
100.71
100.70
100.74
100.78
Standard
deviation
1.55
1.64
1.74
1.75
1.72
1.71
1.75
1.84
Seasonal-adjustment Techniques
When approaching the problem of seasonality,
the analyst should have some knowledge of the mech
anism generating the data. This prior knowledge
suggests the appropriate technique for adjustment. In
some cases, it may not pay to attem pt seasonal
adjustm ent—for example, when seasonal variation is
dwarfed by irregular movements or for a new series
offering little or no historical experience. Economic
theory may suggest reasons for relating changes in the
seasonality of the money supply to readily measured
variables, such as the timing of concentrated volumes
of tax receipts or refunds. In principle, a regression
model could be used to estimate these relationships
to forecast changes in seasonality. Unfortunately, the
science of economics has yet to produce a model that
explains enough about seasonality to be useful at
this level. In the absence of a more complete theo
retical structure, most seasonal-adjustment techniques
estimate and remove seasonal variations in the data
by a weighted-average smoothing process that is based
on the past history of the variable being adjusted. The
most widely used technique is the X -ll seasonaladjustment program of the Bureau of the Census.
Because the X -ll program does not use infor
mation exogenous to the data series being adjusted, it
is not likely to anticipate and capture abrupt changes
in seasonality.1
The X -ll is the method employed in seasonally
adjusting the money supply. It is applied to each of
the basic aggregate components, such as currency and
demand deposits in M-1A. The X -ll is an iterative
procedure involving two fundamental steps designed
to separate any monthly time series into three dis
tinct series, identified as the trend-cycle, seasonal,
and irregular components. The first step seeks to iso
late the trend-cycle component from the seasonal
and irregular components by dividing the original
series by an estimate of the trend-cycle component.
The second step is designed to separate the seasonal
and irregular components. The X -ll procedure is
iterative in two senses: (1) it repeats the second step,
using a revised seasonal component in which extreme
irregular values are eliminated or replaced with damp
1. In recent years, a considerable amount of research has pro
vided several alternatives to, and improvements in, X -ll.
A review of this material is found in David A. Pierce, “A
Survey of Recent Developments in Seasonal Adjustment,”
American Statistician, vol. 34 (August 1980), pp. 125-34.
For further information on the X -ll method, see
Julius Shishkin, Allan H. Young, and John C. Musgrave,
The X - ll Variant o f the Census M ethod II Seasonal A djust
ment Program, Technical Paper No. 15 (U.S. Department
of Commerce, February \96 1 ),X -1 1 : Information fo r the
User, Papers prepared for the Seminar on Seasonal Adjust
ments of the National Association of Business Economists,
March 10, 1969 (U.S. Department of Commerce); and
Thomas A. Lawler, “ Seasonal Adjustment of the Money
Stock: Problems and Policy Implications,” Federal R e
serve Bank o f Richm ond Economic Review, vol. 63 (No
vember/December 1977), pp. 19-27.
ened ones; (2) it repeats both steps by reestimating
the trend-cycle component, using alternative averaging
methods employed on a preliminary seasonally ad
justed series obtained in subsequent rounds.
Although the technique is considered me
chanical, it permits the use of judgment to the
extent that some parameters o f the X-l 1 program can
be varied. Until recently, however, the staff o f the
Board o f Governors o f the Federal Reserve System
made additional judgmental modifications to the final
estimates o f the seasonal-adjustment factors obtained
from the X-l 1 procedures and used in the forthcoming
year. For example, the X-l 1 is not designed to handle
abrupt changes in seasonal patterns that may result
from a change in a tax-filing date or from the im
plementation o f new tax-processing methods. Given
prior knowledge, such events can be anticipated by
modifying the seasonal factors estimated by the X-l 1.
Although this practice is often necessary, it is difficult
to perform consistently over time. In recent years,
the Board staff has limited itself to making adjust
ments that are permitted within the structure of
the X -ll procedure. The adjusted series that con
forms most closely to prior expectations based
on all available information is selected and used.
The discretion allowed by the X -ll program is
best exemplified by the user’s options for choosing
both the length o f the period and the weighting
structure of the moving average. The moving average
options are available when estimating both the trendcycle and seasonal components of the series. Although
the X -ll automatically selects default values for
these options, the user has available alternatives
that permit variations in the degrees o f smoothing.2
When estimating the final trend cycle, the degree
of smoothing (length o f moving average) desired
would depend on the relative importance (average
percent change) o f the irregular variations to the
trend-cycle movements. The greater the irregular
movements relative to the trend cycle, the longer the
moving average needed to smooth out the short-term
2. Experience has shown that the seasonal component of
many economic time series can be adequately estimated
by the same choices of X -ll options. Consequently, the
X-l 1 program is preset to these default options, which can
be changed as circumstances warrant.
movements and reveal the trend. Conversely, if
cyclical movements dominate, then a short moving
average would better reveal the systematic move
ments of the series.
Similarly, when estimating the seasonal com
ponents, the degree of smoothing desired would
depend on the relative importance of the regular
variation. If a seasonal for a given m onth is believed
to be stationary, then all the movement in the seasonalirregular component for the m onth must result
from irregular variation. Thus, the user would choose
to average as many years of that m onth as possible
in order to average out the noise. For this reason, the
X -ll has an option that averages seasonal-irregular
(S-I) values of the same m onth for all prior years
available, giving equal weight to each year.
On the other hand, if the seasonal factor is
believed to be changing, then movements in the S-I
component reflect movements of both individual
components, and the default option may be de
sirable. This option takes a five-year moving average
that weights most heavily the S-I component in
the year being estimated. The two years before and
the two years after are weighted with lesser weights
(declining away from the year). When the seasonal
being estimated is for the most recently available
year, only the two prior years are included. Although
a short moving average may fail to average out irreg
ular noise, it enhances the probability that a seasonal
factor would correctly incorporate movements
reflecting fundamental changes in the determinants
of seasonality. It also enhances the probability
of removing irregular variations under the guise of
seasonal variations. The trade-off is clear. If a priori
evidence exists that movements in the seasonal are
large relative to irregular variation, then a short
averaging period is desired.
Finally, if seasonal factors are believed to be
changing, and the character and extent of the changes
are thought to be known, then it may be desirable to
estimate this change and project it into the forthcom
ing year. An option of the X -ll adds one-half the
change from the previous year to the seasonals of the
last available year to obtain the seasonals for the up
coming year. While such a formula may be rigid, it
may be preferable to using the last available seasonals
when there is strong evidence that seasonals in fact
are changing.
Impact of the Tax-filing Season on Money
The difficulties in estimating the seasonal
factors during the personal tax-filing season (pri
marily February-May) perhaps best exemplify one of
the shortcomings of the X -ll method. While it is
quite predictable that the nonseasonally adjusted
money supply increases greatly around the tax-filing
deadline of April 15, a number of variables affect the
exact time pattern of this phenomenon. If these
factors all changed regularly along trends, then the
X -ll probably would adequately capture the move
ment in the seasonal factors. Unfortunately, some
clearly identifiable determinants of the timing of tax
flows have been behaving in an unpredictable manner,
causing abrupt changes in the time pattern of tax
payments and refunds. Because the X -ll ignores
economic structure, it offers no mechanism for
prompt incorporation of such changes into the
seasonal factors.
Table 2 Federal Income-tax Refunds
of Individuals
Chart 1 M-1A Monthly Growth Rates: 1980
As a percent of nonseasonally adjusted
demand deposits of previous m onth
1975
February
March
April
May
2.0
4.0
3.1
6.1a
Percent (saar)
1976
1977
1978
1979
1980
1.9
4.1
3.5
2.4
1.8
4.3
3.4
2.4
1.3
5.0
3.3
2.9
1.1
4.5
3.4
3.0
1.9
4.6
4.1
4.2
a. Includes income-tax rebate.
Table 3 Non-withheld Income-tax Receipts
of Individuals
As a percent o f nonseasonally adjusted
demand deposits of previous m onth
February
March
April
May
period of March to May. Thus, refunds can produce a
sizable bulge in the money supply during this period.
Second, and quite aside from refunds, persons owing
taxes fund their checking accounts on or before the
date the tax is paid. The time required for mail
service and actual processing of the tax payments
tends to increase the money supply until the Treasury
cashes the checks. The monthly patterns of tax
refunds and payments relative to the level of demand
deposits for recent years are given in tables 2 and 3.
Variables that affect the timing of the tax-related
impact on the money supply include the timing of
tax filing, taxes and tax-withholding rates, the effi
ciency of the tax-processing equipment, the number
of people employed to process returns, and the mail
service. In the past two years, for example, individuals
filed returns later than in previous years. As the April
15 deadline approached, the Treasury faced a larger
than usual bottleneck in tax processing.
1975
1976
1977
1978
1979
1980
0.5
1.3
6.2
0.4
0.4
1.2
6.0
0.3
0.5
1.2
6.5
0.9
0.4
1.1
5.7
2.7
0.4
1.3
7.3
2.2
0.5
1.2
9.3
0.8
Tax processing affects the money supply in two
particular ways. The typical tax refund (averaging
roughly $250) is frequently deposited in checking
accounts, if only temporarily. Refunds in the past
two years totaled $31 billion, or 8 percent of M-1A.
Almost 90 percent of this amount is paid out in the
Although new equipment was in operation, the
Treasury’s budget did not permit the overtime required
to process the tax returns (particularly those with tax
payments) at the rate achieved in the previous years.
As a result, a greater percentage of the tax payments
were not actually collected until May.
This series of events may explain some of the
extraordinary M-1A growth in April of 1978 and 1979
(see table 4). Furthermore, because the X -ll often is
used in a manner that weights the experience of recent
years more heavily, the current year’s seasonal factors
probably overstate the tax-processing impact in April
1980. This seems all the more probable since a greater
dollar volume of taxes was processed in April 1980
than in April 1978 and 1979. One consequence o f in
creased processing could be that the seasonal adjust
ments in April are stronger than they should be,
contributing to the negative growth of M-1A last
April. In effect, tax-processing delays in 1978 and
1979 may have operated through the seasonal-adjust
ment procedure to contribute to the sharp drop in
the money supply in April. Without a well-estimated
model, however, a quantitative assessment of this
impact is extremely hazardous.
Recent M-l A Growth
By looking at the raw data through somewhat
different sets of eyeglasses, one can obtain a rough
idea of the extent to which the 1978-1979 taxprocessing problems may have contributed to the
steep drop in M-l A this past April. As noted earlier,
the official 1980 seasonal factors were constructed
using the default option, in which greater weight was
given to the data of the two most recent years. This
m ethod is consistent with the assumption that the
M-l A seasonals are changing. Alternatively, how
ever, one can assume that the seasonals are relatively
stable and use a longer averaging process in which
each year receives equal weight.
Charts 1 through 3 illustrate how this alternate
weighing process can influence one’s perception of
money growth, not only for April but for the first
six months of the year as well. When using the con
stant seasonals, M-l A still shows a considerable decline
Chart 2 Official M-l A Series
Billions of dollars (sa)
390
Table 4 April Growth Rates for M-l A
Seasonally adjusted annual rates
1975
1976
1977
1978
1979
1980
1980
seasonal
factors
Stable
seasonals
M-l A growth
for year ending
in April
-3.0
6.6
9.2
12.6
14.7
-18.5
-3.2
9.4
15.1
20.7
23.7
-9.6
3.4
6.2
6.9
7.3
5.8
3.7
While it is conceivable that the relationships
between seasonal events and the money supply could
be modeled and well estimated as a practical m atter,
the problems involved are enormous. The seasonal
factors used to adjust current data are calculated at
the beginning o f the year. Consequently, optimal
adjustment o f the data would be required to forecast
the exact time pattern o f each seasonal event for
the entire year ahead. It is not likely that any of these
events could be forecast with the desired degree of
accuracy. Thus, it may be of little consequence that
the X-l 1 ignores this information.
Billions of dollars (sa)
in April but at only about half the rate obtained using
the official factors. Thus, the April drop appears some
what less alarming.
At the same time, however, by redistributing
money growth somewhat differently throughout the
entire tax-filing period, the equal-weight factors ap
pear to signal some cause for concern about money
growth earlier in the year. As charts 2 and 3 show,
money growth, officially adjusted, was close to or
above the midpoint of its long-run growth range
throughout the first quarter. In February, M-1A
actually moved above the top of its growth range, con
tributing to the adoption of more restrictive money
and credit policies. By contrast, the February bulge in
the stable seasonal money series merely carried M-1A
to within the lower half of its long-run growth range.
Otherwise, the equal-weight series has been below
path throughout the period. This growth pattern
would have been consistent with a less restrictive
policy in the first quarter.
The two different policy prescriptions indicated
by applying alternate sets of seasonal factors to the
same raw data highlight the problem faced by policy
makers in trying to separate seasonal from trend-cycle
movements. Moreover, it is quite probable that neither
of the two sets of seasonal factors used here is entirely
correct. The equal-weight factors do not capture
known changes in the determinants of the seasonal
process. The official factors, which are influenced
heavily by the extraordinary events of 1978 and 1979,
may fail to average out enough of the irregular com
ponent of the data.
April Growth and Moving-average Options
The different growth patterns of M-1A implied
by alternative smoothing assumptions are illustrated
in table 4. In the years 1976 through 1979, seasonally
adjusted growth rates for April (both official and those
assuming stable seasonals) were greater than growth
rates for the year ending in that month. Furthermore,
the disparity increased over that period. In the absence
of any explicit information, it seems unlikely that
solely nonseasonal determinants could account for
the four straight years of increasing disparity. It
appears as if the seasonal relationship is changing. By
comparison, it is evident that the official seasonals for
March and April embody some change over time,
which is verified in table 5. The trends that decrease
the seasonal factors in March and increase them in
April tend to dampen the growth of seasonally ad
justed demand deposits in April. Thus, based on
strictly empirical grounds, the official seasonal factors
appear closer to the true seasonals than those that
assume stability for this period.
Table 5 Seasonal Factors
for Demand Deposits
March
1975
1976
1977
1978
1979
1980b
April
Actual3
Stable
option
Actual3
0.985
0.982
0.981
0.979
0.978
0.978
0.984
0.984
0.984
0.984
0.984
0.984
1.009
1.011
1.013
1.013
1.014
1.014
Stable
option
1.010
1.010
1.010
1.010
1.010
1.010
a. Estimated in 1980.
b. The 1980 seasonal.
In 1980, however, the April growth trend was
reversed, suggesting an abrupt change in seasonality.
However, a number of cyclical factors (such as the
declines in industrial production and business loans)
also could have accounted for much of the sharp drop
in M-1A in April. In the absence of a well-estimated
model of seasonality, it is difficult to defend how
much of this change, if any, should be embodied in
the 1981 seasonals.
As noted earlier, some analysts would argue that
the Treasury’s expeditious performance in tax process
ing during 1980 explains a significant portion of the
M-1A decline in April. If the Treasury continues to
perform in such a manner consistently over the next
few years, perhaps the true April seasonal would sta
bilize around a particular value. However, the April
seasonal is probably related to other determinants that
are changing both randomly and systematically. In
either case, it could be argued that the five-year
weighted-moving-average option is likely to be appro
priate most of the time. In terms of time-series analy
sis, seasonality is not likely to be stationary, whereas
the stable option assumes it is. Anyone who has
applied time-series methods to economic variables
appreciates the rare occurrence of stationarity in the
original series.
Monitoring the Economy:
Survey of Fourth District Manufacturers
by Robert H. Schnorbus
Monitoring changes in economic activity among
manufacturing firms on a timely basis provides a
valuable, but seldom possible, means o f analyzing
current business-cycle conditions. In fulfilling its
responsibilities toward formulating monetary policy,
the Federal Reserve Bank of Cleveland monitors the
current performance of the regional economy,
especially regional changes in economic activity that
may signal national cyclical developments. In response
to the need for regional information, in 1965 the bank
developed a monthly survey of manufacturing firms
within the Fourth Federal Reserve District (4D) to
ascertain basic conditions o f production activity.
When converted into diffusion indexes that measure
the direction o f change in eight key indicators of
economic activity, the 4D survey findings can be
used to interpret the timing, amplitude, and duration
of the current phase o f the business cycle.
As with any analytical tool, the value of the
indexes obtained from the 4D survey depends on an
understanding of both the strengths and weak
nesses of the survey. Major weaknesses in businesscycle analysis are the limitations imposed by the
quality, timeliness, and availability of data. Often,
key indicator series for the national economy either
are not published on a m onthly basis or are published
with considerable time lags. Comparable regional
data, where available, may involve even longer lags
than national data. Although detailed monthly
employment data for the United States, for example,
are available within a week after the end of each
m onth, state (payroll) employment data by industry
are not released for another one to two months.
Other monthly data, such as new orders, shipments,
and inventories, take much longer to collect and
process at the national level, and often are not
available at the regional level. As a consequence,
short-term economic forecasting and policymaking
exclude key measures that may signal impending
change in economic activity.
The monthly survey of the 4D manufacturers
collects a variety of reliable information about the
district’s production base with a minimal time lag.
The shorter time lag of the 4D survey, compared with
alternative sources of information, is its most valuable
contribution. However, it is not simply availability
and timeliness of data that are important. A data
series should have a record for accuracy in identifying
important characteristics, such as turning points in
economic activity. Ideally, the 4D indexes should
closely correspond to the timing, amplitude, and
duration of comparable national series published by
other sources, except where distinct regional character
istics are captured by the 4D survey. This article
assesses the 4D survey indexes and their accuracy
over the past ten years by statistically comparing
them with rates of change in comparable aggregate
series at the national level.1
1. Earlier evaluations of the 4D survey found the indexes to
be consistent with behavior of their national counterparts.
However, the survey sample has undergone changes in re
cent years, and the popularity of surveys, in general, has
declined with the growth of econometric forecasting mod
els. For earlier studies, see “ Diffusion Indexes and Eco
nomic Activity,” Economic Review, Federal Reserve Bank
of Cleveland, January 1971, pp. 3-17; and Theodore S.
Torda, “The Monthly Survey of Fourth District Manufac
turers-A n Early Warning Signal,” Economic Commentary,
Federal Reserve Bank of Cleveland, October 27,1969.
Table 1 Survey Sam ple D istrib ution :
Industry
Food
Furniture
Paper
Printing
Petroleum
Primary metals
Fabricated metals
Machinery
Electrical equipment
Transportation equip
ment
Sample total
1978
Total
employment
of sample3
Survey
employment,
percent
Ohio
employment,
percent^5
U.S.
employment,
percent*5
1
3
2
2
1
6
4
5
4
2
27,100
16,300
37,000
13,698
12,927
227,505
16,105
93,894
205,000
14,500
4.1
2.5
5.6
2.1
1.9
34.3
2.4
14.1
30.9
2.2
7.2
1.7
3.8
6.3
1.5
14.9
16.5
20.9
11.8
15.4
12.7
3.6
5.2
8.8
1.5
8.9
12.4
17.5
14.7
14.6
32
664,029
100.0
100.0
100.0
Number
of
firms
a. Employment figures are based on a survey sample taken in July 1978.
b. Percentages are based on sum of employment for listed industries.
SOURCES: Bureau of Labor Statistics and Federal Reserve Bank of Cleveland.
Description of the 4D Survey
The survey is derived from a sample of 4D
manufacturing firms intended to reflect the regional
manufacturing sector, especially the disproportionate
share of durable-goods industries. To be sure, some of
the participating firms in the current sample have
only corporate headquarters remaining in the 4D, but
most of the firms still have operating plant facilities
within the district. To the extent that these firms also
have nationwide operations, the sample captures
national characteristics. (Table 1 compares a typical
survey sample with the actual distribution of employ
ment for Ohio and the nation among a limited set of
ten industries.) Because of the predominance of
durable-goods producers in the 4D, the sample has
tended to overemphasize durable-goods industries,
such as primary metals and electrical equipment.
Other durable-goods industries, such as fabricated
metals and transportation equipment, have been
underestim ated.2 (Some im portant industries, such as
rubber, are not represented.) While the sample size
can vary from m onth to m onth, the number o f active
participants currently averages 30 to 35.
The monthly survey consists of eight indicators
of economic activity and a composite index. One set
of indicators tends to be production-oriented, includ
ing employment, hours worked per week, prices paid
for materials, and inventories (see chart 1). Another
set tends to be sales-oriented and includes new orders,
backlog of orders, delivery times, and shipments (see
chart 2). For each of these indicators, participants are
asked whether manufacturing activity increased, de
creased, or remained unchanged in the previous m onth
and what is expected for the m onth in progress. By
adding the percentage of responses that report in2. For example, 34.3 percent of the total employment repre
sented by the survey sample was contained in primary
metals, while only 14.9 percent in Ohio (a proxy for the
4D) and 8.9 percent in the nation would have been re
quired for an unbiased sample. Likewise, 2.4 percent of
the sample’s employment was well below a required 16.5
percent in Ohio and 12.4 percent in the nation. In order
to test the comparability of the sample employment dis
tribution with the national distribution, a Spearman rank
order correlation test was performed, using the percentage
figures in table 1. The null hypothesis that the percentage
distribution of employment is similar must be rejected on
the basis of the computed correlation v a lu e (p s = 0.34,
with nine degrees of freedom).
Chart 1 Production-oriented Indexes
(Nov.)
P
(Dec.) (Nov.)
Percent
p
70
__ Emp] Dyment
60
(Mar.)
T
\
50
40
V' V / \
\
\
30 —
\I
20
....
70
60
_
Hour
50
A
V
40
V
/
l r -W
v
A
i
\ A
A
r
30
;
7
20
70
60 —
50
Inver tories
/" S
/\
/ V
40
A/A
V
\ / v
K
30
20
90
/ \\/n
1 tn il
' VV
/ /
b u /
* 7*^
1\l1
1
Price
80
70
60
50
40
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
NOTES: Dotted lines indicate anticipated values for month in progress.
Shaded areas indicate periods of contraction.
creases and one-half the percentage o f responses that
report no change, a numerical score—called a diffusion
index—is com puted for each indicator.3 The score for
the m onth in progress is computed as a forecast value,
although it may be based on partial information avail
able at the time of the survey. By a simple averaging
technique, the eight index values are then combined
into a composite diffusion index that serves as a
measure of overall manufacturing activity.
The 4D indexes suffer from statistical problems
that are both inherent in the method of computing
diffusion indexes and specific to the 4D sample.
First, the sample distribution can be affected by the
inclusion (or exclusion) of some of the largest firms
in the 4D. For example, the overemphasis on primary
The index values are published after a seasonal-weight fac
tor is applied to produce raw scores, which are then aver
aged with the raw scores of the previous month. The
month-to-month fluctuations thus are smoothed out to
reveal more clearly any underlying cyclical pattern. For
alternative methods o f constructing diffusion indexes, see
Arthur F. Bums, “ New Facts on Business Cycles,” in
Geoffrey H. Moore, Ed., Business Cycle Indicators, vol. 1
(National Bureau of Economic Research/Princeton Univer
sity Press, 1961), pp. 1344.
Chart 2 Sales-oriented Indexes
Percent
60 L
20
(Dec.)
P
(Nov.)
T
(Nov.)
P
(Mar.)
T
Delivery Time
—
B
—
I
I
---------------------------------------------------------------------------------------L
—
1—
-- ----------------------------------------------------------------
Shipi tents
A
n
1969
1970
1971
1972
1973
1974
/V A _
V
f
1975
1976
'
1977
rAAa\ j ft'hL
' vvv%f\
1978
1979
_
1980
NOTES: Dotted lines indicate anticipated values for month in progress.
Shaded areas indicate periods of contraction.
metals was partly the result of including three of the
largest steel-producing firms in the nation. The indexes
are likely to show a bias toward events that are specific
to a given industry, such as steel, and to the 4D
economy, such as localized labor-management dis
putes. A second related source of bias results from
the treatm ent of each participating firm on a “ one
firm-one vote” basis, regardless of the relative size of
the firm ’s work force. Thus, a large firm is given as
much weight in the com putation of the indexes as a
small firm, even though the magnitude o f the change
associated with the response of a large firm may
represent a greater economic impact on the nation.
Third, the eight individual indexes are not all
independent. Delivery time, for example, is equiv
alent to backlogs divided by shipments, and the
new-orders index is equivalent to the change in
backlogs plus shipments. The problem of overlapping
indexes is particularly relevant to the composite
index, which, as a result, may overemphasize certain
aspects of manufacturing activity. Finally, diffusion
indexes, in general, are valuable in terms of indicating
the direction of change in activity, but are more
limited in terms of measuring amplitude of change.
Cyclical Properties of the 4D Indexes
The rationale for the 4D indexes is derived from
the tendency for economic expansions and contrac
tions to spread by a cumulative process among firms,
industries, and regions. The relationship between
cyclical spreading, or diffusion, of expansions or con
tractions and the timing, duration, and amplitude of
business cycles reflects the interconnection among
business activities of firms and the process by which
adjustments to the cycle are made. A diffusion index
can be related to the rate of change of a comparable
aggregate series through the assumption that the
deeper a contraction (and, therefore, the greater the
rate of decline in the aggregate series), the more wide
spread the contraction becomes among firms and
industries (see Properties of a Diffusion Index, p. 12).
The expected behavior o f the 4D survey indexes then
is based on the performance of comparable aggregate
series with which the indexes should have close his
torical relationships.
The mixture o f production-oriented and salesoriented indicators creates some unique properties for
the behavior of the 4D composite index. Some of these
properties tend to lead and others tend to lag the ref
erence cycle as defined by the official dating o f peaks
and troughs by the National Bureau o f Economic Re
search (NBER). The combination of cyclical properties
causes the composite index to differ from a diffusion
series based on other aggregate series, such as the FRB
Index of Production, which tends to coincide with
cyclical peaks and troughs. Although the construction
of the composite diffusion index as an average o f the
eight individual indexes would suggest the likelihood
of a coincident indicator, the expected behavior of the
composite index depends on the predominance o f the
lead-lag relationships among the eight individual sur
vey indexes.
Among the production-oriented indicators, only
manufacturing employment qualifies as an aggregate
series that is roughly coincident with the overall
business cycle. The timing of the turning points of
the employment series is expected to be coincident,
because employment is a product of cyclical changes
in demand. Thus, employment is at the center of
cyclical movement, as it adjusts to changes in demand.
The amplitude of employment swings may be mild,
however, compared with changes in production, be
cause employers often try to protect their skilled work
force. Employers have the alternatives of not replacing
workers who leave or retire and of slowing the rate of
expansion of their work force. Production may expe
rience wider fluctuations than other aggregate series,
so that the relative adjustments of employment reflect
the extent to which further adjustments in the other
production-oriented aggregate series may be required.
The flexibility of hours worked and, to a degree,
of prices of materials over the cycle makes them
potentially leading indicators. Shortening of the work
week in response to an economic slowdown usually
occurs before other employment adjustments. Changes
in hours are easier to administer, easier to reverse when
necessary, and just as likely to reduce costs per hour
(for example, when overtime is reduced or when train
ing costs for new employees are avoided). The prices
of materials, too, generally change quickly as inven
tories of raw materials accumulate or contract over
the business cycle. When demand for a firm’s output
is expanding, the firm’s demand for input expands. If
the rise in input demand cannot be accommodated by
existing inventories, suppliers are tempted to raise
prices. If inventories accumulate during a slowdown,
suppliers may offer price discounts to manufacturers.
Whether adjustments are made by altering hours
worked or prices, the failure to keep production in line
with demand has a spillover effect on inventory levels
of finished products. Manufacturers produce either to
stock, as in products such as nuts and bolts, or to
order, as in products with special requirements. Pro
duction to order requires lower inventories of finished
goods and greater control in managing inventory levels
of materials. Production to stock depends on the accu
racy of the firm’s economic forecasts of demand for
its product. As a rule, inventory levels begin to accumu
late beyond the cyclical peak in economic activity be
cause of the lags inherent in detecting and confirming
a peak. Once inventories are high relative to desired
levels, firms will attem pt to liquidate their oversupply.
The liquidation may continue beyond the cycle’s
trough, not only because of the problem of recognizing
a trough, but also because orders should pick up be
fore production will be expanded. Yet, even though
inventory levels may lag the cycle, additions to or re-
Properties of a Diffusion Index
Stage
1
2
3
4
a diffusion index that
implies that the aggregate series
rises (50% — 100%)
falls (100% — 50%)
falls (50% — 0)
rises (0 — 50%)
increases at an increasing rate
increases at a decreasing rate
declines at an increasing rate
declines at a decreasing rate
In computing a numerical value for a given diffusion index, one-half of the percentage of the partici
pants reporting no change is added to the percentage of participants reporting increases. Thus, if 30 per
cent o f the participants report “increasing,” 40 percent report “ unchanged,” and 30 percent report
“ decreasing,” the value of the diffusion index would be 50 percent. While an aggregate series measures the
actual level attained by a particular business-activity indicator, such as employment, the diffusion index
measures the percentage of firms participating in the expansion (or contraction) in any given m onth. The
proper interpretation, then, is not that employment is expanding, for example, but that one-half of the
firms are experiencing expanding employment. The amplitude of the diffusion index above (or below) the
50 percent level measures the intensity of the expansion (or contraction), while the intersection of the 50
percent level signifies a peak (or trough). The distance between peaks measures the duration of the cycle.
In the special case illustrated above, the diffusion index curve is equivalent to the rate of change in the
aggregate series. 1
1. A discussion of the diffusion process as it relates to aggregate series appears in Geoffrey H. Moore, “The Diffusion of
Business Cycles,” in Geoffrey H. Moore, Ed., Business Cycle Indicators, vol. 1 (National Bureau of Economic Research/
Princeton University Press, 1961), pp. 261-81. Although a diffusion index is similar to a rate of change, there are fun
damental differences. As indicated earlier, the diffusion index takes into account only the direction, not the magnitude,
of change. Thus, if a general expansion is under way in a specific aggregate series, a diffusion index would show
whether it has been spreading among firms. While the scope of an expansion in its early stages appears to be roughly
correlated with the magnitude of the expansion, the same is not necessarily true for the rates of change in most eco
nomic aggregates during expansions. A diffusion index can do no more than measure the scope of period-to-period
changes. For a discussion of the conditions required to make a diffusion index proportional to a rate of change of a
comparable aggregate series, see Geoffrey H. Moore, “Diffusion Indexes, Rates of Change, and Forecasting,” in
Geoffrey H. Moore, Ed., Business Cycle Indicators, vol. 1 (National Bureau of Economic Research/Princeton Univer
sity Press, 1961), pp. 282-93.
ductions in inventories (that is, the rate of change)
may lead the cycle, especially near the peaks.4
The lead-lag relationship among the salesoriented series depends upon its placement within the
production process, beginning with the receipt of a
new order through final shipment. New orders for
durable goods particularly show a tendency to lead
the business cycle in general (as well as to lead the
output of the industry receiving the orders) because
of the causal connection between commitment to
buy and production.5 The amplitude o f fluctuations
in new orders is augmented by two factors. First,
firms tend to place orders with more than one m anu
facturer to assure delivery and an adequate supply
around the peak of an expansion. Second, firms are
able to cancel orders when they see the first signs
of an economic slowdown, a problem further exacer
bated by double ordering. Firms producing to stock
have some advantage in using inventories to adjust to
changes in new orders, while firms producing to order
must rely on more complex adjustment mechanisms.
Shipments, especially for firms producing to
order, are expected to run parallel with new orders,
separated only by production time, which may vary
from industry to industry. In addition to production
time, however, the span between receipt and shipment
of a new order can be altered by the supply con
straints that result in backlogs and by changes in
delivery time, which serve a role similar to inventories
as a “buffer” to unexpected changes. Whether pro
ducing to stock or to order, firms may attem pt to
stabilize their production by allowing backlogs to
accumulate before capacity is reached (especially if
employment expansion is required) and only run
them down after a slowdown begins. Backlogs, then,
are generally designated as leading a cycle peak, but
lagging a cycle trough.
Changes in backlogs and shipments also appear
in the adjustments of delivery time. Indeed, the ratio
of backlogs to shipments represents the number of
months needed to dispose of existing backlogs at
present rates of operation (average time interval
between an order’s receipt and delivery). Delivery
time includes both the time spent in production
and the time spent in backlogs. In a seller’s market,
where advanced orders are strong, manufacturing
firms have a degree of discretion over delivery dates.
(Even if firms cannot fill orders in excess of capacity
constraints, they still can receive them.) When de
mand is rising, buyers seek early delivery dates; yet
once a slowdown begins, buyers, too, are often
willing to wait. In general, delivery time leads the
cycle at both the peak and trough.
Behavior of the 4D Indexes
4. Because of the uncertainty in supply and demand fluctua
tions and the time lags involved in the production process,
inventories often serve as a buffer. Inventories, however,
may accumulate for two different reasons. Businessmen
may be building inventories voluntarily in anticipation of
expanding sales, or businessmen involuntarily may be
accumulating inventories from overestimating that expan
sion. In the first case, the change in inventories would be
a leading indicator; in the second, a lagging indicator.
Over the past ten years, the 4D indexes have
been effective in monitoring economic activity in the
nation and in the district. In comparing the 4D indexes
with the corresponding aggregate series (expressed as
rates o f change), the 4D indexes generally have proved
to be statistically significant in explaining current and
future levels of the aggregate series (see Appendix).
Except for the inventory series, the 4D indexes are able
to provide meaningful information on the behavior of
the aggregate series beyond what is known from exam
ining past rates of change in the aggregate series. In
deed, the shipments index has proved to be more
successful than relying on past rates of change in the
shipments series for indicating current rates of change.
5. Many firms experience large cyclical fluctuations in de
mand and react to them strongly through output adjust
ments. Industries in which there are many such firms con
stitute an important part of durable-goods manufacturing,
which, in turn, is a sector that carries much weight within
the economy as a whole. See Victor Zarnowitz, “The
Timing of Manufacturers’ Orders During Business Cycles,”
in Geoffrey H. Moore, Ed., Business Cycle Indicators, vol.
1 (National Bureau of Economic Research/Princeton Uni
versity Press, 1961), pp. 420-84.
The 4D indexes, however, are less successful in
forecasting rates of change in the next m onth than an
aggregate series. Only half of the indexes (employment
prices, backlogs, and delivery time) exhibited statistical
significance. In three of the remaining indexes (new
orders, shipments, and the composite index), even
knowledge of the past behavior of the series has proved
to be unsatisfactory for forecasting rates of change in
the subsequent month. Although confidence in the
Table 2 Comparison of Turning Points: 4D Index and Comparable Series
1969-70 recession
Peak
NBER reference
points
Deviation3
Trough
1973-75 recession
Deviation3
Dec. 1969
Nov. 1970
Nov. 1969
Oct. 1969
Oct. 1971
Nov. 1971
+11
Dec. 1969
Oct. 1969
Mar. 1971
Nov. 1970
+4
Peak
Deviation3
Trough
Deviation3
Mar. 1975
Nov. 1973
Employment
Index
Series
Oct. 1975
June 1975
+7
+3
July 1975
Mar. 1975
+4
+18
May 1976
June 1976
+15
+10
Sept. 1974
Sept. 1974
+10
+10
May 1975
Apr. 1975
+2
Oct. 1974
Dec. 1974
+12
+14
Apr. 1975
Apr. 1975
+1
+1
Mar. 1976
Mar. 1976
+13
Apr. 1976
Mar. 1975
+14
May 1975
Mar. 1975
+3
Oct. 1974
Aug. 1974
+12
Oct. 1974
Dec. 1973
+12
0
Dec. 1972
Jan. 1972
+25
+14
Dec. 1974
Mar. 1975
+14
Oct. 1969
Dec. 1969
Jan. 1971
Dec. 1970
+2
+1
Nov. 1969
Dec. 1969
Jan. 1971
Dec. 1970
+12
+10
Hours
Index
Series
+1
0
Inventory
Index
Series
May 1970
Dec. 1970
+6
+12
New orders
Index
Series
+1
Shipments
Index
Series
+2
+1
Backlogs
Index
Series
Nov. 1969
Dec. 1969
-1
0
-1
Oct. 1970
Dec. 1970
+1
Nov. 1969
Oct. 1969
Oct. 1970
J a n .1971
+2
Dec. 1969
Dec. 1969
Jan. 1971
Dec. 1970
+2
Sept. 1974
Nov. 1974
+11
Oct. 1974
Mar. 1974
+12
+13
+13
Delivery time
Index
Series
-1
+5
0
Composite
Index
Series
+3
Oct. 1974
Oct. 1974
+12
+12
0
a. Numbers indicate difference in months between NBER reference cycle turning points and individual indicators. The specific
NBER turning points are December 1969 (peak), November 1970 (trough), November 1973 (peak), and March 1975
(trough).
NOTE: Prices were excluded because no peaks were registered by the 4D index over the period studied.
SOURCES: Department of Commerce (Bureau of Economic Analysis) and Federal Reserve Bank of Cleveland.
forecasting ability of the 4D indexes may be limited,
the survey generally provides information that is con
sistent with the expected cyclical behavior of the aggre
gate series, at least in terms of duration and amplitude.
The success of the 4D indexes in capturing the
pattern of fluctuation in the comparable aggregate
series does not guarantee that the timing of a 4D index
peak or trough will closely correspond to the peak or
trough of the aggregate series. In fact, almost one-third
of the turning points among the 4D indexes followed
their expected turning points (as determined by the
rate o f change in the comparable aggregate series) by
more than a one-quarter time period (see table 2).
Almost half of these deviations were concentrated in
the inventory index, which has a weak correlation with
the aggregate series. Of the remaining deviations, most
were contained in the 1973-75 recession, which sharply
contrasted with the more accurate signaling of turning
points in the 1969-70 recession.
During the 1969-70 recession, the actual devia
tion of the 4D index peak and trough from the NBER
reference cycle peak and trough tended to be no more
than one to two months. At the reference cycle peak
(December 1969), for example, all of the 4D indexes
except inventories closely corresponded to the refer
ence cycle peak and to the specific aggregate series
peak. At the reference cycle trough (November 1970),
all of the indexes except employment, hours, and in
ventories closely tracked both the reference and the
specific cycles. Both the index and the specific cycle
for employment reached a trough about one year after
the reference cycle, so that the index was still accu
rately representing the employment series. The hours
index reached a trough four months after the reference
cycle and the specific series in both the 1969-70 and
the 1973-75 recessions.
The inability of the inventories index in the
1969-70 recession to coincide with the turning points
of the aggregate series was perhaps expected from the
relative weak correlation with the specific aggregate
series. The index lagged the reference cycle peak by 6
months and the trough by 25 months, while the spe
cific cycle of the aggregate series indicated a 12-month
to 14-month lag at each turning point. Still, the index
may indicate a pattern of behavior peculiar to the re
gion. Inventory adjustments in the 4D may have lagged
the reference cycle at the peak because o f difficulties
in making prom pt production adjustments or hesitancy
among 4D manufacturers to acknowledge that a reces
sion was developing. At the trough, 4D manufacturers
may have been cautious about building inventories
until sales were increasing faster than production.
Although the number of 4D indexes that devi
ated substantially from the turning points of their
counterparts increased during the 1973-75 recession,
the aggregate series themselves tended to lag the refer
ence cycle by roughly one year, especially at the peak
(November 1973). However, the recession did not
actually affect the 4D economy until late 1974, not
the peak of late 1973 determined by the NBER. At
the reference trough (March 1975), new orders, ship
ments, backlogs, and the composite index behaved
satisfactorily with respect to specific turning points,
but the other indicators signaled turning points much
later than the specific aggregate series.
In addition to the inventories index, which re
peated its earlier pattern of reaching a peak sooner and
a trough later than its specific cycle, hours and delivery
time indexes had the largest deviation from their
specific cycles. Because the 1973-75 recession was in
many respects atypical, the discrepancies in the hours
and delivery-time indexes may reflect regional charac
teristics of the recession. If much of the miscalculation
that led to the overextension of inventories was con
centrated among 4D manufacturers, the expansion of
hours worked might have lasted longer, and the sub
sequent contraction may have led to a later trough in
the 4D than elsewhere in the nation. With production
expanding, delivery times may have continued to be
extended in the 4D, while new orders and backlogs
were tapering off in other regions. If so, the 4D in
dexes may be more indicative of the regional pattern
of activity than the specific aggregate series.
Despite the shortcomings of individual 4D in
dexes, the 4D composite diffusion index has proved
to be an effective indication of overall manufacturing
activity. When compared with the FRB index, the
composite index revealed a close correspondence with
manufacturing activity, but its forecast was no better
than could be obtained with past knowledge of the
FRB index (see chart 3). The turning points and, thus,
durations of the two cycles were almost perfectly co
incident with the reference cycle. But the amplitude
of the two cycles may have been exaggerated in the
composite index, as the 4D economy was dispropor
tionately affected during both the expansion and re
cession that followed.6 Particularly, expansion that
occurred during 1974 appears to be much more in
tense in the composite index than suggested by the
rate of change in the FRB index. Expansion was
strongest in inventory and business fixed investment,
hence in primary metals and metal-working industries
that held up well into the recession. Indeed, both in
dicators revealed a spurt of activity in the middle of
1974 that appeared to be largely concentrated in the
4D economy. However, the subsequent sharp contrac
6. The regression residuals reflect the fact that most of the
indexes overestimated the decline in November and De
cember of 1974, presumably because of the intensity of
the inventory liquidation and decline in goods-producing
industries in the 4D. The plot of the two indicators also
shows the effects of the automobile and expected steel
strikes in 1970 on the FRB index, but not on the composite
index (which was, of course, controlled for in the correla
tion tests with the dummy variable, Z). The residuals of
the regression revealed other exogenous shocks experienced
by individual series, but few were widespread among the
indexes.
Chart 3 Overall Manufacturing Activity
Percent
70
(Dec.)
P
(Nov.)
T
(Nov.)
P
Percent (rate o f change)
NOTE: Dotted line indicates anticipated values for m onth in progress.
SOURCES: Board of Governors o f the Federal Reserve System and Federal Reserve Bank of Cleveland.
tion in 1975 is captured by both indicators, even
though the composite index is slower to recover, as
might be expected in a widespread expansion.
Finally, the anticipated values for the composite
index have proved to be about as accurate as the
actual values. Although no test of significance could
be used to check the accuracy of anticipated values,
their performance between June 1978 and December
1979 has been reasonably consistent with the actual
values. The anticipated values survey has a tendency
to overstate amplitude, which is not unusual in fore
casting. However, the apparent accuracy of the antic
ipated values suggests that the forecast could be greatly
improved by including the anticipated value in the
forecast model. By treating the m onth in progress as
an actual value, knowledge of manufacturing activity
is extended one full m onth, thereby improving the
accuracy of the forecast. Since all of the indexes indi
cate reasonably accurate anticipated values, many of
the problems with forecasting, especially with the
composite index, may be overcome by the inclusion
of anticipated values as a contemporary value in an
analysis similar to confirming current levels.
1979 in Perspective
Turning points in peaks and troughs of business
activity are difficult to identify and generally have
eluded forecasters. Since early 1975, manufacturing
activity was in an expansionary phase that has been
characterized by spurtsof growth between brief pauses,
at least until late 1979. Each pause raised apprehension
among manufacturers at the onset of another recession.
Economic behavior is still sufficiently perplexing that
forecasters who had expected a slowdown in early
1979 (based on available indicators) were required
continually to revise those expectations. The credible
performance of the 4D indexes over the past ten years
provides a foundation for improving the month-tom onth analysis of manufacturing activity.
While not designed specifically to determine
turning points, the 4D indexes can be useful warning
signals because of their currency and corroborative
value. For example, both the composite index and the
FRB index captured the spurts and pauses o f the re
cent expansion. However, the composite index only
once signaled a potential contraction (January 1977),
compared with four by the FRB index (presumably
related to strikes or other temporary disruptions) since
1975. In September 1979, the composite index clearly
showed a peak and subsequent contraction in overall
manufacturing activity. In contrast, the FRB index
indicated only uncertainty, with alternating positive
and negative rates of change each m onth throughout
the year. Although one-half of the eight individual in
dexes in 1979 deviated from the pattern of their cor
responding series, the remaining indexes conformed
to their expected patterns. For example, hours, ship
ments, and backlogs also reached peaks by midyear
and have continued to indicate decline into 1980,
while their comparable series have fluctuated irregu
larly. The inventory index has indicated relative sta
bility, while its comparable series has shown moderate
accumulation. In each of these cases, the indexes have
underestimated the strength of their corresponding
series. Among the conforming indexes, most of the
corresponding series were also in a contraction phase.
Manufacturing employment, delivery time, and neworders indexes indicated decline throughout the second
half of 1979 and only temporarily showed signs of
strengthening in early 1980. The price index, along
with its corresponding series, again has been the ex
ception in that the pace of inflation occasionally has
slackened, but it has never declined.
The discrepancies between the indexes and their
corresponding series can be attributed to inherent
shortcomings in the survey. The 4D survey sample
currently contains a disproportionate number of steel
producers that, along with automotive producers, have
been the leading edge of the recent economic slow
down. Steel inventories have been under extremely
tight controls and appear to be preventing the inven
tory index from exhibiting the expected trend toward
accumulation. Similarly, hours, backlogs, and ship
ments appear to be contracting in the 4D indexes
rather than being stable as expected because of the
steel industry’s experience. However, the correspond
ing series may also be faulty because the length of the
recent expansion and capacity constraints have pro
duced sizable backlogs of orders. Until these backlogs
are sufficiently reduced, production (and, as a result,
hours and shipments) can be sustained at current
levels, even though other areas of the economy are
weakening. Therefore, the composite index, by being
more broadly based than the FRB index and less dom
inated by capacity constraints, may in fact be a more
accurate depiction of the current state of manufactur
ing activity.
Concluding Remarks
After nearly a year of unfulfilled recession fore
casts, the outlook for manufacturing activity was un
certain. The need for current and reliable indicators
of manufacturing activity is acute. The results of this
investigation indicate that the 4D survey is a useful
tool for monitoring the economy. To be sure, the in
dexes must be used with caution, incorporating knowl
edge of the 4D economy with experience in businesscycle analysis. Nevertheless, the information conveyed
by the indexes is a valuable piece in the puzzle of
where the district and national economy is and where
it is likely to go.
Appendix Description of the Models and Empirical Results
Two basic models were constructed to determine whether the amount of information contained
in the 4D index series constituted a significant improvement over the body o f knowledge already avail
able from the historical pattern o f the aggregate series. Since the most inclusive measure of that body
of knowledge can be presented by the past performance of the aggregate series itself, the models took
the following form:
Coincident model
X f - a + b ^ Z + b 2X f l + b 3X { 2+ b4D If + b5D It
b6D I{ 2+ u
Forecast model
where
X = rate of change in aggregate series
Z = control variable for automobile and expected steel strikes in November-December 1970
D I = one of eight diffusion indexes for current time period, t, and for two previous time periods.
Tests were conducted on all the indexes, using a Cochrane-Orcutt iterative technique to minimize
autocorrelation problems. The models test the ability of the indexes to conform to the rate of change of
an aggregate series (coincident model) and to predict the rate of change of the aggregate series in the
next period (forecast model). The null hypothesis that the coefficients of the diffusion indexes are
zero and, therefore, contribute no useful information is rejected if the coefficients are statistically sig
nificant. (Although lags in data availability differ for each aggregate series, only one form of the model
that gave the diffusion index a one-month availability advantage was tested for consistency.) The statis
tical results for the period between January 1970 and January 1979 appear in table A. The comparable
series selected for the comparison were as follows:
Production-oriented
Employment: Employment (U. S.)—manufacturing
Hours: Weekly hours o f product workers—manufacturing
Prices: Producer Price Index—durable-goods manufacturing
Inventory: Inventory stock—durable-goods manufacturing
Sales-oriented
New orders:! Diffusion index for new orders of durable-goods manufacturing
Backlogs: Unfilled orders—durable-goods manufacturing
Delivery tim e:l Vendor performance—percentage of companies reporting slower deliveries
Shipments: Shipments—durable-goods manufacturing
1. New orders and delivery time are not based on the aggregate series. Because the series are themselves diffusion in
dexes, rates of change need not be computed.
Table A Coefficients of Regression Equations
Key: a
= constant
R2 = coefficient of determination
DW = Durbin-Watson statistic
Production-oriented indexes
Employment
•
Coincident model {Xf)
Sales-oriented indexes
Hours
Prices
Inventories
New
orders
Backlogs
Delivery
time
Ship
ments
Composite
index
X t.\
0.140
(1.87)
0.092
(0.96)
0.073
(0.81)
0.538
(5.72)
-0.367
(-4.42)
0.773
(8.39)
0.562
(6.22)
0.053
(0.61)
0.291
(3.07)
Xt-2
0.181
(2.32)
-0.268
(-2.78)
0.230
(2.77)
0.189
(2.14)
-0.114
(-1.46)
-0.079
(-0.88)
-0.171
(-1.99)
0.047
(0.54)
0.154
(1.78)
D It
0.002
(2.29)
0.003
(2.42)
0.000
(1.99)
0.000
(1.41)
0.009
(4.87)
0.001
(5.58)
0.006
(2.10)
0.001
(3.57)
0.001
(5.01)
D It -1
0.000
(0.72)
-0.002
(-0.94)
0.000
(4.97)
-0.000
(-0.00)
-0.002
(-0.80)
-0.001
(-3.68)
0.002
(0.38)
-0.001
(-2.34)
-0.001
(-3.22)
DIt -2
-0.000
(-1.53)
-0.000
(-0.32)
-0.000
(-0.08)
0.000
(1.10)
-0.000
(-0.16)
0.003
(2.46)
-0.008
(-2.90)
0.001
(1.93)
-0.000
(-0.08)
Z
-0.024
(-9.24)
-0.018
(-0.43)
0.006
(3.15)
0.004
(1.52)
0.179
(2.96)
-0.008
(-1.96)
-0.094
(-1.60)
-0.069
(-5.47)
-0.022
(-3.93)
a
-0.010
(-2.36)
-0.004
(-1.48)
-0.018
(-3.97)
-0.010
(-3.27)
0.453
(4.78)
-0.013
(-4.03)
0.045
(1.13)
-0.037
(-2.23)
-0.002
(-0.39)
0.699
0.145
0.710
0.709
0.488
0.824
0.301
0.311
0.579
1.969
R2
2.020
DW
Forecast model (X^+i)
Xf. ]
0.101
(1.33)
2.001
1.991
2.028
2.019
2.028
2.150
1.993
-0.202
(-2.02)
0.234
(2.56)
0.279
(3.27)
0.071
(0.80)
0.108
(1.08)
0.046
(0-51)
0.054
(0.57)
0.059
(0.58)
Xt-2
-0.131
(-1.75)
-0.104
(-1.05)
0.030
(0.36)
0.262
(3.07)
0.067
(0.78)
0.110
(1.06)
0.000
(0.01)
0.008
(0.08)
0.089
(0.94)
DIt
0.003
(2.83)
0.000
(1.45)
0.000
(5.41)
0.000
(1.78)
0.004
(1.72)
0.000
(1.94)
0.008
(2.48)
-0.000
(-0.34)
-0.000
(-0.16)
DIt.x
-0.001
(-0.86)
-0.000
(-0.43)
0.000
(0.94)
0.000
(0.55)
-0.003
(-0.94)
-0.000
(-0.34)
-0.000
(-0.13)
0.000
(1.10)
0.000
(0.33)
DIt -2
0.000
(0.31)
-0.000
(-0.50)
-0.000
(-0.63)
0.000
(1.23)
0.002
(0.90)
0.000
(2.35)
-0.009
(-3.01)
0.000
(0.03)
-0.000
(-1.01)
Z
-0.023
(-9.88)
-0.002
(-0.42)
0.006
(3.47)
0.003
(1.22)
-0.029
(-2.71)
-0.004
(-1.08)
-0.070
(-1.05)
-0.071
(-5.24)
-0.022
(-4.06)
a
-0.012
(-2.09)
-0.002
(-0.70)
-0.017
(-3.31)
-0.015
(-3.22)
0.331
(4.05)
-0.021
(-2.65)
0.127
(2.05)
-0.012
(-0.59)
0.013
(1.18)
Rl
0.688
0.102
0.701
0.704
0.374
0.768
0.269
0.222
0.481
DW
2.090
2.009
1.962
1.975
2.002
1.824
1.978
1.988
2.021
NOTE: Values in parentheses are ^-statistics.
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