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Economic
Review
SEPTEMBER/OCTOBER 1988
FEDERAL RESERVE BANK OF ATLANTA
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President
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I S S N 0732-1813
V O L U M E L X X 1 I I , N O . 5, S E P T E M B E R / O C T O B E R
1988, E C O N O M I C
REVIEW
2
The Effect of the
"Triple Witching Hour"
on Stock Market Volatility
Steven P. Feinstetn and
William N. Goetzmann
20
Forecast Accuracy and the
Performance of Economic Policy:
Is There a Connection?
Does the triple witching hour actually
lead to greater volatility in the S&P 500
index? Has the 1987 rule change affected market volatility?
In making their decisions, how much
weight should policymakers give to
forecasts of future economic conditions?
William Roberds
34
F.Y.I.
R. Mark Rogers
Improving Monthly Models
for Economic Indicators:
The Example of an Improved CPI Model
52
Book Review
Buying into America
William J. Kahley
by Martin and Susan Tolchin
58
Finance, Employment, Construction, General
Statistical Pages
FEDERAL RESERVE BANK O F ATLANTA 3
The Effect of the
"Triple Witching Hour"
on Stock Market Volatility
Steven P. Feinstein and William N. Goetzmann
This paper investigates the "Triple Witching Hour"-the
index options, and stock index futures simultaneously
characterized
by excessive volatility in the stock
The authors are, respectively, an economist in the financial
section of the Atlanta Fed's Research Department and a
doctoral candidate at the Yale School of Organization and
Management. The authors wish to thank Professors Jonathan
Ingersoll, jr., Philip Dybvig, Stephen Ross, and Paul Koch for
their helpful suggestions and insightful
comments.
whether these periods are
market.
The term "triple witching hour" can conjure
up images of broomsticks and brew, perhaps
the scene from Shakespeare's Macbeth in which
a trio of witches recite incantations around a
boiling cauldron. For stock traders, though, the
term represents something far more frightening. To them the "triple witching hour" refers to
the four times each year when stock index
futures, stock index options, and options on
individual stocks expire simultaneously. Typically on triple witching hour days, large blocks of
stock change hands as hedgers, arbitrageurs,
and speculators seek to maximize returns or
minimize losses as they settle the contracts
entered into previously.
Analysts have alleged that the triple witching
hour is a time of great volatility and wide price
swings in the stock market. In mid-1987 the
Chicago Mercantile Exchange and the New York
Futures Exchange were so moved by the concern over triple witching hour volatility that they
changed the rules governing the expiration of
index futures and options. Trading in most
index futures contracts and some index options
now ends one day earlier, with expiration effec-
2
four times during the year when stock options, stock
expire—to determine
tively taking place at the open of trading on the
expiration day instead of at the close. The
impact of triple witching hours and expiration
days in general, though, extends far beyond the
matter of whether the pattern of stock trading is
atypical on certain days of the year. Of interest
also is the fact that many of the new features that
distinguish modern financial markets from markets of the past are integral to the triple witching
hour phenomenon. These new features include
futures and options trading, computerized trading, program trading of large blocks of stocks,
and index arbitrage. Examination of triple witching hour days offers the opportunity to explore
the impact of these innovations.
By looking at triple witching hour days in
general, some insight can also be gained into
fundamental questions about financial markets.
To what extent does the mechanism of exchange—
the market itself—affect asset prices? Are stocks
rendered riskier merely by the existence of
option contracts that are, in effect, "side bets"
on stock performance? Why should the popularity of financial instruments that simply
reallocate claims on firms' earnings change the
inherent risk profile of the market itself?
Information about triple witching hour days
can also be used to test widely held views about
financial asset prices. For example, the efficient
market hypothesis holds that stock prices continuously reflect all available information and
that prices change only when new information
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
ÖSlTTtUl
becomes available. Thus, when stock prices
swing sharply, analysts must wonder if certain
information is driving the movement or whether
the mechanism of trade itself is precipitating
the price swing.
Yet another branch of theory—option and
futures pricing—hinges on the notion that these
derivative instruments are "redundant." That is,
FEDERAL RESERVE BANK O F ATLANTA
an investment in options or futures can be perfectly mimicked with investment strategies involving only stocks and bonds. If in fact futures
and options are redundant investment vehicles
and markets had previously been efficient, the
price behavior of stocks should be the same
now as before the advent of the new markets.
Consequently, price behavior across triple
3
witching hour days should not be unusual since
triple witching hours did not exist before the
new instruments were created. If, on the other
hand, prices do behave differently on triple
witching hour days than on other days, either the
new assets are not truly redundant, markets
previously were not efficient, or markets currently are not efficient.
This article reviews the current research into
the triple witching hour phenomenon and investigates whether the market really is more
volatile on triple witching hour days. This research also presents preliminary results of the
effect of the new settlement procedures on
market volatility.
The New Financial Instruments
Stock Index Futures and Index Arbitrage. In
order to understand triple witching hour day
activity, one should understand the mechanics
of stock index futures and options. Stock index
futures first traded in 1982. They originated on
the same midwestern exchanges that traditionally traded commodity futures and options, and
in many ways are similar to their agricultural precursors (see box on page 5).1 Like a futures contract on coffee or corn, a stock index futures
contract will return profits when the price of the
underlying asset rises and create losses when
the asset price falls.
A stock index futures contract is an instrument that allows an investor to participate in the
stock market without ever actually purchasing
stocks. Moreover, stock index futures enable
investment in large diversified portfolios through
a single transaction rather than the numerous
transactions that are required to form a diversified stock portfolio. In this way, the investor
can save substantially in commission expenses.
Although stock index futures are derivative instruments, that is, instruments whose prices are
contingent on the values of other assets, the
daily transaction volume measured in dollars
for stock index futures now exceeds that of
actual stocks.2
The market for stock index futures created
the opportunity for a new type of investment
strategy, index arbitrage, which involves exploiting the difference between the value of an
4
underlying stock index portfolio and the price of
the corresponding stock index future. Theoretically that difference should never become very
large. If, however, a gap opens up between the
two, the opportunity for a nearly riskless profit
results. To execute the strategy, one would buy
the less expensive instrument—either the portfolio or the index future—and sell the more
expensive one. If the future is less expensive,
one should buy ("take a long position in") the
future and sell ("short") the portfolio of actual
stocks. If the stock portfolio is less expensive,
arbitrage calls for a purchase of the stock portfolio and a short position in the future. (Commissions and the cost of borrowing the necessary
funds must also be considered.) Either action
ensures a certain profit because the two prices
must converge by the time of expiration.
For example, suppose the Standard and
Poor's (S&P) 500 index futures price were $300,
but the actual Standard and Poor's 500 stock
portfolio could be purchased for $250. Seeing
this discrepancy, an arbitrageur would calculate
whether the gap between the future and the
spot prices were enough to cover commissions
and the costs of borrowing necessary funds. If
indeed the gap were large enough, the arbitrageur could buy the actual stocks and take a
short position in the futures. If the price of the
actual stocks fell by the expiration date, a loss
would be incurred on the actual stock investment, but the profit on the futures investment
would more than offset that loss. Suppose, on
the other hand, stock prices rose. In that case
money would be lost on the short futures position, but even more would be realized from the
change in the price of the actual stocks. Again
the investor would reap a guaranteed profit. No
matter what happens to the price of stocks, the
arbitrageur benefits.3
Eventually, the arbitrageur must "unwind" his
position, that is, sell the stock portfolio and exit
the futures contract. In order to retain the arbitrage revenue and clear a profit, unwinding must
take place when the two prices are the same or
closer together than when the arbitrage strategy
was initiated. Convergence may occur before
the contract expiration but must certainly occur
at expiration—at the witching hour.
Unwinding must be done quickly so that the
arbitrageur is not left holding only one risky part
of the arbitrage portfolio without the offsetting
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
A Comparison of Commodity Futures and Stock Index Futures
A commodity future is a contract that obligates
an agent either to buy or sell a given quantity of a
commodity at a prespecified price on a certain
date. For example, taking a "long" position in a
coffee futures contract obligates the agent to buy a
certain large quantity of coffee (37,500 pounds)
when the contract expires. The party taking the
"short" position is obligated to sell the commodity. The price is determined via bidding at the
time the contract is initiated and is referred to as
the futures price. Taking a long position in a coffee
futures contract is very similar to buying coffee
outright, except delivery and payment are postponed until the contract's expiration. Since the
contract conveys ownership of coffee, albeit deferred, coffee futures prices should be strongly
related to the current price of coffee (also known
as the spot price or cash price of coffee). Moreover,
as the expiration date approaches, owning coffee
and "owning" a coffee futures contract become
nearly the same thing, and so the spot price of coffee and the coffee futures price converge. At
expiration, buying a coffee futures contract is the
same as buying actual coffee; the futures price
must equal the spot price at that time.
One might think of a stock index futures contract
as a contract that obligates an agent either to buy
or sell a large portfolio of stocks at a prespecified
price upon expiration of the contract. This simplification helps one to understand what determines stock index futures prices and what causes
those prices to change. If this simplification were
accurate, a stock index future would be just like a
coffee futures contract, with the exception that
half. To accomplish the speedy dispensing of
their stock holdings, arbitrageurs often employ
the Designated Order Turnaround system of the
New York Stock Exchange, a computerized stock
order routing system. Alternatively, arbitrageurs
may place orders with exchange specialists to
execute the orders at the moment the futures
contract expires. In either case, index arbitrage
requires large volumes of stock to be bought
and sold quickly, with many of these transactions occurring on triple witching hour days.
Stock Options. If the unwinding of index
arbitrage positions were the only unusual activity taking place on certain days, those days
might be called witching hour days, not triple
witching hour days. Yet stock options and stock
FEDERAL RESERVE BANK O F ATLANTA
stocks would be bought and sold instead of coffee.
In reality, though, stock futures differ from commodity futures in that a stock portfolio is never
actually delivered. When the contract expires, the
agents exchange money—that is, the contract
"cash settles." If the spot price has risen on net
duri ng the I ife of the contract so that the spot price
upon expiration is greater than the original futures
price, the "short" party pays the "long" party the
difference in cash.
For example, suppose you took a long position
in a stock index futures contract when the futures
price was $100. If, by expiration, the value of the
underlying stock portfolio had risen to $120 you
would receive cash payments totaling $20—the difference between $ 100 and $ 120—over the 1 ife of the
contract. You would have made money because
the stock index value rose above the level the
futures price had been when you entered into the
futures contract. The cash settlement is not made
all at once at expiration, however. Rather, it is
made in part at the end of each trading day on the
basis of the change that transpired that day in the
futures price. On the expiration day you receive or
pay only the difference between the futures price
from the previous day and the spot price at expiration. Thus, in the example in which the original
futures price was $100 and the expiration spot
price was $ 120, the long party would receive payments each day as the futures price rose and perhaps have to make payments to the short party on
those days when the futures price fell. Over the life
of thecontract, though, the net transfer would total
$20 paid by the short party to the long party.
index options expire on those days as well,
which may generate additional volume. The
owner of a stock option has the right, but not the
obligation, to buy or sell a certain stock by a
specified time and at a particular price. (See box
on page 6 for a brief explanation of options.) The
following possible scenario illustrates how
option expirations can lead to increased stock
trading activity.
Acall option owner (someone who has bought
the right to purchase a certain stock) exercises
the option and demands that the option "writer"
(the party who sold the option) sell a share of
stock. The writer first buys the stock at the stock
exchange and then, to fulfill the contractual
agreement, sells it to the option owner at the
5
O p t i o n s Demystified
An option is a contract that affords the buyer the
right, but not the obligation, to buy or sell an asset
for a prespecified price on or before some selected date. The prespecified price, which is written
into the option contract, is called the strike price
or exercise price. The selected date is the expiration date, the last date on which the option owner
can choose to buy or sell the underlying asset.
The option owner can choose not to exercise the
option and thus forfeit the right to buy or sell the
underlying asset. In that case the option expires
unexercised.
The two types of options are call options and
put options. Call options confer the right to buy
assets; put options confer the right to sel 1. One can
think of a call option as a deposit. Suppose a
college fraternity is planning a party for the next
homecoming. To assure an ample supply of root
beer for its party, the fraternity members may wish
to place a deposit at the local grocery store reserving the right to buy a crate of root beer for a given
price on the day of the party. Here, the fraternity is
buying an option, and the grocery store is writing
the option. The underlying asset is the crate of
root beer, and the cash amount to be paid upon
delivery of the root beer is the strike price. The
amount of money paid in advance to the grocery
store is the option price. Should the fraternity
members decide they do not want the root beer,
they may wish to surrender the deposit, not buy the
strike price. The option owner then resells the
stock to capture profit from t h e difference between the price stated in t h e option contract
a n d t h e current market price. The option's expiration d a t e is t h e d e a d l i n e for these maneuvers. Consequently, the existence of stock call
options may generate increased trading activity
on those days.
A scenario involving stock p u t options may
yield similar activity. The owner of a p u t option
has the right t o sell shares of stock at a previously agreed-upon price. If t h e stock price falls
b e l o w t h e strike price, exercise of the option is
profitable. If the p u t owner wishes to exercise
the option on an expiration day b u t d o e s not
already own t h e necessary shares of stock, he
must first buy t h e shares at t h e market price. H e
then can sell t h e m for the higher strike price t o
the party that sold the p u t a n d pocket the profit.
The p u t writer might then wish to close out his
6
root beer, and let the option expire unexercised.
Suppose on the other hand that the price of root
beer increases dramatically before the day of the
party. Maybe an explosion disables the local bottling plant or a root beer tasters' convention is
scheduled for the same day as the party. The
agreement with the grocery store would thus
become more valuable. The grocery store is
bound to sell the root beer to the fraternity for the
previously agreed-upon price even though the
spot price of root beer has risen in the interim. The
fraternity members may exercise the option, buy
the root beer at the strike price, and thus enjoy
their assets at a bargain price. Alternatively, they
may choose to exercise the option, buy the root
beer at the strike price, then sell the root beer on
the open market for the new higher spot price
and retain the profit.
Stock call options are very much like the root
beer deposit in this example. The call option
buyer has the right but not the obligation to buy a
certain stock for the strike price before or on the
expiration date. If the market price of the underlying stock rises above the strike price, the option
owner can exercise theoption, buying the stockfor
the strike price, and then sell the stock for the
higher current market price. The seller of the
option must have the necessary shares of stocks to
sell to the option buyer. If he does not, he must
first buy those shares.
p o s i t i o n a n d sell t h e newly a c q u i r e d stock.
Again, o n e earlier o p t i o n transaction might,
u p o n expiration, generate three separate stock
transactions.
Options on individual stocks have b e e n traded
on U.S. exchanges since 1973. Stock options may
follow different quarterly schedules, b u t in general they expire on t h e third Friday of t h e month.
Four times a year this day coincides with the
expiration of index futures and index options.
Stock Index Options. The third aspect of the
triple witching hour involves the expiration of
stock index options. Since their introduction in
1983, stock index options have m a d e it possible
to buy or sell options on entire stock indexes in
addition to options on individual stocks. Stock
index p u t o p t i o n s have p r o v e d attractive t o
hedgers who own large portfolios that are likely
to rise a n d fall in v a l u e in concert with t h e
market as a whole. By purchasing a stock index
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
put option, investors can protect against losses
caused by a market-wide decline.4 Index options are also popular among speculators who
wish to profit from the vicissitudes of the stock
market as a whole. By investing in stock index
options rather than individual stock options,
speculators and hedgers need not be concerned with the idiosyncratic risks associated
with individual stocks since the value of a stock
index option is based on the value of a large,
diversified portfolio.
Unlike options on individual stocks, stock
index options settle in cash. No stocks change
hands when stock index options are exercised.
The exercising party simply receives a cash payment from the option writer equal to the difference between the strike price and the current
market value of the underlying index. Although
exercisers of index options need not actually
sell or buy stocks, such exercise might provoke
the option writer, instead, to execute a stock
transaction. An option writer is responsible for
the difference between the current stock index
value and the option strike price. If the stock
market has gained or lost much value since the
writing of the option, payment by the option
writer can be substantial.
Call option writers often hold the underlying
stocks in their portfolios so that, should the
option be exercised, they can sell the stocks on
the exchange in order to raise the funds needed
to pay the call option owner.5 At expiration one
can expect any in-the-money options (options
for which immediate exercise is profitable) to
be exercised, sending some option writers
scrambl ing to cover their positions, thereby promoting heavy stock trading on expiration days.
Stock Volatility Effect
The previous section of this article reviewed
how stock index futures, stock options, and
stock index options might bring about frenetic
equity trading on days when each of these instruments expires. This increased trading activity could in turn exacerbate price volatility. A
temporary mismatch between buy and sell
orders will either send the price up or down as
the price equilibrates supply and demand pressures. Only a small price change is necessary to
FEDERAL RESERVE BANK O F ATLANTA
close a slight gap between buy and sell orders,
but a large price change may be necessary when
the gap is wide. When trade orders suddenly
flood the exchange, large gaps are more likely,
and thus large price swings are more likely to
occur. On triple witching hour days the full
expiration effects of stock options, stock index
options, and stock index futures bear on the
markets at the same time. This simultaneity provides one reason to expect higher volatility on
those days. Of course, even if triple witching
hour days are more volatile than other days,
other reasons for the phenomenon could exist.
Reviewing the Evidence. Notwithstanding
the theoretical reasons for triple witching hour
day volatility and the belief by market participants and business journalists that this
volatility exists, the phenomenon is ultimately
an empirical question and one that warrants
close scrutiny of the facts. Several academic
studies have addressed the volatility of the triple witching hour days. Some researchers have
investigated component parts of the triple
witching hour phenomenon, such as the effects
of large transactions on prices, while others
have probed the impact that the stock index
futures market has had on underlying stock
price movements.6
Among the recent research directly investigating triple witching hour days, the paper by
Hans Stoll and Robert E. Whaley (1986a) is the
most comprehensive. They looked for evidence
of unusual volume and price effects on and
around expiration days. Testing the period from
May 1982 through December 1985, the researchers failed to find that stock index future expiration days exhibited higher volatility than nonexpiration days.7 They did conclude, however,
that from July 1983 through December 1985, the
last hour of trading on triple witching hour days
was a frenetic one, exhibiting far greater volume
and volatility than the last hour of trading on
nonexpiration days.
Stoll and Whaley's results were corroborated
in a study by Franklin R. Edwards (1988). Edwards compared hour-by-hour price fluctuations
on triple witching hour days with hour-by-hour
fluctuations from nonexpiration days during the
period from July 1983 through October 1986.
Edwards too found that price volatility was
significantly greater in the last hour of triple
witching days than on ordinary days.
7
Stoll and Whaley, as well as Edwards, arrived
at their conclusions based on the statistical procedure known as an F-test, which compares
stock prices in one sample with those from
another sample. Based on assumptions of certain properties regarding the distribution of
stock returns in both samples, the test determines the likelihood that stock prices were
equally volatile in the two samples. One troubling feature of the F-test, however, is that it
assumes that stock returns are normally distributed; that is, when plotted on a graph, the
distribution would resemble a bell curve. However, an abundance of evidence shows that stock
returns are not normally distributed but instead
are characterized by sporadic extreme observations, either occasional huge losses or huge
gains.8 The recent stock market crash of October
1987 is a graphic reminder that the distribution
of stock returns does not conform to a normal
distribution. Consequently, the F-test, whose
results are easily distorted by extreme occurrences, is not reliable for drawing inferences
about underlying stock return distributions and
thus for identifying trends that are likely to persist in the future.9
test works as follows: if triple witching hour days
are not unusual with regard to volatility, then any
given triple witching hour day will just as likely
fall in the top half as in the lower half of all days
ranked according to volatility. This implication
of the hypothesis is tested by ranking all days in
the sample by volatility and simply counting
how many triple witching hour days ranked in
the top 50 percent and how many ranked in the
bottom 50 percent. From the results of this
tabulation, one can determine whether the
hypothesis about equal volatility and the triple
witching hour effect is reasonable.
The Data. This research examines the daily
returns of the S&P 500 index from January 1983
through June 1988, the period over which stock
index futures and index options have been
traded. The returns are calculated as daily per-
"If triple witching hour days are not
unusual with regard to volatility, then
any given triple witching hour day will
just as likely fall in the top half as in the
lower half of all days ranked according
to volatility.''
Market Volatility and the Triple
Witching Hour: A New Perspective
The primary objective of the research presented in this article is to determine if the triple
witching hour days in the period before 1987
were, in fact, characterized by unusually high
volatility. Unlike past research, this effort uses a
statistical procedure that does not require the
assumption of normally distributed stock returns.
Furthermore, the research presented here benefited from several more triple witching hour days
than were available for earlier studies.
This article also includes an examination of
the first five triple witching hour days since the
1987 rule change. A study of this data can help
determine whether the new expiration procedures succeeded in reducing triple witching
hour day volatility.
The tests used are distribution-free statistical tests, that is, they do not rely on the assumption of normally distributed stock returns. The
8
cent changes in closing prices. The volatility
measure used was the absolute value of the
daily stock return, which reflects the magnitude
of each day's price swing.10
Prior to June 1984, stock index futures and
stock index options expired on the third Thursday of the final month of the quarter. Consequently, the first five expiration days in the
sample used here are Thursdays. Since that
time all triple witching hour days have been the
third Friday of the final month of the quarter.
Before June 1987, the close of trading on the
expiration day marked the end of trading in and
expiration of stock index futures and stock
index options. Since then, with the change in
rules, trading in most index futures contracts
and some index options ends on the Thursday
before the third Friday, but settlement and
expiration take place on the next day.11 The setE C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
tlement price for the index futures and options
is a composite of the opening prices of the
individual stocks in the index. In effect, the contracts governed by the new rule now expire at
the opening of trading on Friday rather than at
the close.
According to the Chicago Mercantile Exchange,
the rationale for changing the expiration of stock
index futures and options on stock index futures
from the close of trading on Friday to the open
was as follows: whereas arbitrageurs would previously unwind positions using market-on-close
orders—to time their stock transactions exactly
with the expiration of the futures or options—
now they must place market-on-open orders.
Although a specialist cannot delay the close of
trading, he may delay the opening of trading in a
particular stock if he observes a large imbalance
"The two tests run on the 1983-87 data
set clearly rejected the hypothesis that
expiration days were equally likely to
have above- as below-median price
swings."
between buy and sell market-on-open orders.
With this extra time he can find parties willing to
absorb some of the surplus orders. Thus, large
price swings might no longer be necessary to equilibrate temporary surges in supply or demand.
Also, because trading in options and futures
now stops on the Thursday prior to expiration,
some market participants may choose to unwind their positions on a day when they can still
buy and sell futures or options. Therefore, the
new expiration rules might have the effect of
spreading both volume and volatility over two
days, whereas they used to be concentrated
on one.
Design of the Tests. This study tests first for
higher-than-usual volatility of the S&P 500 on
the expiration days between January 1983 and
May 1987. The test is based on a comparison of
the price swings on those days with the median
FEDERAL RESERVE BANK O F ATLANTA
price swing from all other days in the January
1983 to May 1987 sample. 12 Most of the expiration days in this sample, however, occurred on
Fridays, and, as documented in Kenneth R.
French's (1980) research, the day of the week
bears on stock price behavior. Therefore, these
expiration days were then compared specifically to the other Fridays in the sample. These
two tests yield similar results.
Even the second test, though, does not completely control for the day-of-the-week effect,
since some of the expirations in the sample
were on Thursdays. Therefore, the two tests were
repeated using only the subsample in which all
expirations occurred on Fridays—the May 1984
to May 1987 subsample. The results are the
same, as will be shown later in this article.
To test whether the results in the sample
period were associated with the introduction of
options on index futures, all of these tests were
repeated using data from the four years immediately prior to their introduction, 1979-82.13 Third
Fridays in March, June, September, and December were designated as "pseudo-expiration"
days, following the same rule in force throughout most of our 1983-87 test period. If indeed
the patterns in the 1983-87 sample resulted
from the introduction of index options, then one
would expect to find no similar pattern in the
1979-82 period.
In the period since the 1987 rule change, the
triple witching hour is in effect spread out over
two days, a Thursday and the following Friday.
If a volatility effect is present, it may be on one
day or the other, or perhaps spread out over the
two days. Consequently, for this recent sample,
expiration Fridays were compared to all other
Fridays, expiration Thursdays were compared
to all other Thursdays, and the two-day price
swings that transpired over expiration ThursdayFriday clusters were compared to those price
swings that transpired over all other ThursdayFriday clusters.
Results. The two tests run on the 1983-87 data
set clearly rejected the hypothesis that expiration days were equally likely to have above- as
below-median price swings. These results are
presented in Table 1. Chart 1 shows the price
swings for each of the 17 expiration days during
those years; the median price swing for all other
days and the median price swing for all other
Fridays are represented by the top and bottom
9
Chart 1.
S&P 500 Daily Percentage Price Swings
(Triple Witching
Hour Days vs. Medians,
1
September 1983
i
i
June 1984
|
January
i
March 1985
i
1983-May
I
1
December 1985
1987)
1
1
1
r
September 1986
The horizontal lines represent median price swings for the period January 1983-May 1987. The top line shows the
median for all days during this period; the bottom line shows the median for Fridays. A box above the median represents a
greater-than-usual price swing for that triple witching hour day. A box below the median indicates a lower-than-usual
price swing for that day. A box between the medians for the different samples represents a lower-than-usual price swing
relative to all days in the sample but a greater-than-usual price swing for Fridays during the sample period. Thus, this chart
shows that on triple witching hour days between March 1983 and March 1987, price swings in the S&P 500 index were
typically greater than on Fridays and on all days in general.
Table 1.
Test of S&P 500 Index Volatility on Triple Witching Hour Days
(January
1983-May
1987)
Sample
Size
Below-Median
Price Swings
Above-Median
Price Swings
Probability*
(Percent)
Expiration days vs. all other days
17
4
13
2.5
Expiration days vs.
nonexpiration Fridays
17
3
14
0.6
Test
*Probability of the occurrence of at least the indicated number of above-median price swings under the assumption that
expiration days are as likely to exhibit above- as below-median price swings.
Source: Figures in all tables and charts were calculated at the Federal Reserve Bank of Atlanta from data obtained from
Data Resources, Inc., Lexington, Mass.
IO
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Chart 4.
S&P 500 Daily Percentage Price Swings
(Triple Witching Hour Days vs. Medians, May 1984-May
1
December 1984
1
r
1987)
r
1
September 1985
1
r
March 1987
June 1986
The horizontal lines represent median price swings for the period May 1984-May 1987. The top line shows the median for
all days during this period; the bottom line shows the median for Fridays. This chart demonstrates that even after controlling for a "day-of- the-week" effect, the daily percentage price swings on triple witching hour days were greater than usual
for other days in the period May 1984-May 1987.
Table 2.
Test of S&P 500 Index Volatility on Triple Witching Hour Days
(May 1984-May
1987)
Sample
Size
Below-Median
Price Swings
Above-Median
Price Swings
Probability*
(Percent)
Expiration days vs. all other days
12
2
10
1.9
Expiration days vs.
nonexpi ration Fridays
12
1
11
0.3
Test
*Probability of the occurrence of at least the indicated number of above-median price swings under the assumption that
expiration days are as likely to exhibit above- as below-median price swings.
FEDERAL RESERVE BANK O F ATLANTA
13
horizontal lines, respectively. Thirteen of the 17
expiration days had price swings above the
median of all other days, if above-median and
below-median price swings were equally likely
on expiration days, the probability of 13 or more
above-median price swings, as occurred in the
sample, would be only 2.5 percent. Comparing
expiration days to nonexpiration Fridays produced a slightly stronger result, 14 above the
median and 3 below. Outcomes with this many
or more above-median observations have a
probability of only 0.6 percent under the hypothesis that triple witching hour days were just
like other Fridays. The test suggests that the
hypothesis is unlikely; one can conclude that
unusual volatility typified triple witching hour
days.
Restricting the sample to the post-March 1984
period when all expirations were on Fridays
yields the same results, which are presented in
Table 2 and illustrated in Chart 2. Triple witching
hour days appeared unusual compared to all
other trading days, as well as to other Fridays. Of
the 12 expiration days in this subperiod, 10
exhibited volatility above the median of all
other days. Outcomes with 10 or more abovemedian price swings out of a possible 12 would
have justa 1.9 percent chance of occurring under
the hypothesis of no unusual volatility on triple
witching hour days. The second test produced
an even stronger result: 11 of the 12 days fell
above the median for other Fridays. The probability of this result occurring under the hypothesis of no unusual volatility on triple
witching hour days is only 0.3 percent. One can
thus conclude that triple witching hour days
were more volatile than ordinary Fridays and
more volatile compared to all other trading days
as well.
These results showed a marked contrast to
similar tests run on the 1979-82 data. "Pseudotriple witching hour" days were created for this
presample by examining the third Friday of the
final month of the quarter. If something were
unusual about these days of the year, apart from
being triple witching hour days after 1982, similar patterns of volatil ity would also be expected
in this earlier period. As shown in Table 3 and
Chart 3, these expectations were not fulfilled.
Exactly half of the pseudo-triple witching hour
days, eight of the sixteen, fell above the median
of all other days' volatility, and, similarly, eight
12
fell above the median of other Fridays. This
result is likely when nothing is unusual about
the 16 pseudo-triple witching hour days. Thus,
the study of the 1979-82 data suggests that nothing peculiar about third Fridays in quarterending months was evident in the period prior
to the introduction of index options.
The Period since the Rule Change. Table 4
and Charts 4, 5, and 6 present the results from
the tests conducted on the period since the
expiration rule change. Of the five expiration
Fridays since the rule change, four fell below
the median of all other Fridays and one fell
above. The probability of this few or fewer
above-median observations would be 18.8 percent if it were in fact the case that expiration
Fridays were no different from all other Fridays.
"With such a small sample, definitive
conclusions cannot be drawn, but it
appears that the rule change may have
reduced the propensity for expiration
Fridays to exhibit unusually high volatility."
With such a small sample, definitive conclusions
cannot be drawn, but it appears that the rule
change may have reduced the propensity for expiration Fridays to exhibit unusually high volatility. Prior to the rule change, ten Fridays fell
above the median and only two below, whereas
since the rule change only one has fallen above
the median and four have fallen below.
The purpose of examining Thursdays and
Thursday-Friday clusters is to test the possibility that the rule change simply shifted volatility
to the Thursday preceding expiration or perhaps spread the excess volatility across two
days. The test of Thursday volatility, however,
could not confirm or reject this possibility. Of
the five Thursdays preceding expiration Fridays, three fell above the median for all other
Thursdays, and two fell below. No conclusions
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
can be drawn from this result, and more observations are needed in order to determine
whether these Thursdays are now more or less
volatile than ordinary Thursdays.
On the other hand, the test of ThursdayFriday clusters does provide evidence against
the notion that the excessive volatility is still
generated by the expirations but is now simply
spread out over two days. All five of the expiration Thursday-Friday cluster two-day price
swings fell below the median of all other
Thursday-Friday clusters, which indicates that
expiration Thursday-Friday clusters are not
likely to display higher-than-usual volatility; if
anything, they are likely to display lower-thanusual volatility. Again, though, one must exercise caution when interpreting these results.
"/TJraders may have practiced extra
caution and restraint in this early
period under the new rule while waiting to see its effects. Also, curbs placed
on computerized trading in the aftermath of the October 1987stock market
crash could have contributed to the
apparent reduction in volatility...."
The sample size of five observations is small,
and a different pattern quite possibly will
emerge with time. Moreover, traders may have
practiced extra caution and restraint in this early
period under the new rule while waiting to see
its effects. Also, curbs placed on computerized
trading in the aftermath of the October 1987
stock market crash could have contributed to
the apparent reduction in volatility on triple
witching hour days.
Conclusion
This study of t h e volatility on triple witching
hour days finds that before the rule change,
volatility on those days was likely t o b e greater
FEDERAL RESERVE BANK O F ATLANTA
than the volatility of ordinary trading days. In
other words, the change in stock market prices
over the course of a triple witching hour day was
likely to be greater than the price changes experienced over most ordinary days.
This result is significant to investors. If the
witching hour effect systematically influences
stock price volatility, this effect should also
influence the pricing of stocks and derivative
instruments. The value of index options, for
instance, depends directly upon expected market volatility. Thus, the witching hour effect, or
its possible disappearance, must be taken into
account by those who wish to price financial
assets.
In a theoretical perspective, the greater
volatility suggests something curious: the structure of the market for derivative assets may
actually influence the valuation of the primary
securities. The possibility that this influence
exists runs counter to the theory that stock
prices continuously reflect only that information
relating to the risk-adjusted expectation of the
future cash flows of a company.
This article described a possible explanation
for higher-than-usual volatility on expiration
days before the rule change, that is, large mismatches between buy and sell orders brought
on by the flood of orders submitted by agents
covering or settling positions. Another possibility is that with higher volume on expiration
days, more new information was brought to the
market—information that could have pushed
prices one way or the other. Yet, these explanations are only possibilities. Though this research sheds Iittle light on the true cause of the
volatility, the study does clarify just what the
empirical effect of the triple witching hour was
before the rule change.
Finally, the early evidence suggests that since
the rule change, expiration Fridays are no longer
likely to exhibit higher-than-usual volatility, and
expiration Thursday-Friday clusters are likely
to exhibit less volatility than other ThursdayFriday clusters. Nonetheless, because of the
limited amount of information available since
the rule change and other potentially influential
events during this period, this result is tentative; the newly emerging evidence could still
contradict this result. For now observers must
wait to see whether the triple witching effect is
still a reality or a thing of the past.
13
Chart 1.
S&P 500 Daily Percentage Price Swings
(Pseudo-expiration
i
I
r
September 1979
i
Days vs. Medians,
I
r
June 1980
i
January
1
1979-December
i
r
March 1981
I
1982)
r
December 1981
t
1
r
September 1982
The horizontal lines represent median price swings for the period January 1979-December 1982. The top line shows the
median for all days during this period; the bottom line shows the median for all Fridays. Since the boxes representing triple
witching hour day price swings are distributed fairly evenly above and below the lines, this chart indicates that before the
introduction of options on index futures, pseudo-expiration days were not likely to be more volatile than typical days.
Table 3.
Test of S&P 500 Index Volatility on Pseudo-expiration Days
(January
1979-December
1982)
Sample
Size
Below-Median
Price Swings
Above-Median
Price Swings
Probability*
(Percent)
vs. all other days
16
8
8
59.8
Pseudo-expiration days
vs. other Fridays
16
8
8
59.8
Test
Pseudo-expiration days
*Probability of the occurrence of at least the indicated number of above-median price swings under the assumption that
expiration days are equally likely to exhibit above- as below-median price swings.
IO
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Chart 4.
S&P 500 Daily Percentage Price Swings
(Expiration
June 1987
Fridays
vs. Medians,
September 1987
May 1987-July
December 1987
1988)
March 1988
June 1988
The horizontal lines represent median price swings for the period May 1987-July 1988. The top line represents the
median for all days during this period; the bottom line represents the median for Fridays. This chart shows that since the
rule change which moved the end of trading on most index futures contracts and some index options to one day earlier,
the propensity for expiration Fridays to exhibit greater-than-usual price swings may have been reduced.
Table 4.
Test of S&P 500 Index Volatility on Expiration Days
since the 1987 Rule Change
Sample
Size
Below-Median
Price Swings
Above-Median
Price Swings
Probability*
(Percent)
5
4
1
18.7
all other Thursdays
5
2
3
50.0
Expiration Thursday-Friday clusters vs.
all other Thursday-Friday clusters
5
5
0
3.1
Test
Expiration Fridays vs.
all other Fridays
Expiration Thursdays vs.
*Probabilities listed for the first and third tests are the probability of the occurrence of at least the indicated number of
below-median price swings under the assumption that expiration days are as likely to exhibit above- as below-median
price swings. The probability listed for the second test is the probability of the occurrence of at least the indicated number of above-median price swings under the assumption that expiration days are as likely to exhibit above- as belowmedian price swings.
FEDERAL RESERVE BANK O F ATLANTA
15
Chart 1.
S&P 500 Daily Percentage Price Swings
(Expiration
June 1987
Thursdays
September 1987
vs. Medians,
May 1987-July
December 1987
March 1988
1988)
June 1988
The horizontal lines represent median percentage price swings for the period May 1987-July 1988, after the rule change.
The top line represents the median for all days during this period; the bottom line represents the median for all Thursdays.
Since the boxes in this chart show no distinct pattern, and since the sample on which the chart is based is such a small
one, these results are not conclusive regarding price swings on expiration Thursdays since the rule change.
IO
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Chart 6.
S&P 500 Percentage Price Swings over Thursday-Friday Clusters
(Expiration
June 1987
Thursday-Friday
September 1987
Clusters
vs. Medians,
December 1987
May
1987-July
March 1988
1988)
June 1988
The horizontal line represents the median price swing for Thursday-Friday clusters during the May 1987-July 1988
period. Since all the boxes fall below the line indicating typical price swings for Thursday-Friday clusters, the results of
this test appear to indicate that expiration Thursday-Friday clusters are not likely to display greater-than-usual price
swings. If anything, they are likely to exhibit lower-than-usual price swings. The sample to this date is small, though, and
another pattern may emerge over time.
FEDERAL RESERVE BANK O F ATLANTA
17
Notes
'The U.S. Commodity Futures Trading Commission
(CFTC), which oversees agricultural commodity trading,
also oversees trading in stock index futures and options
on stock index futures.
2
Galberson (1987).
Stock index arbitrage is not practical for the small or even
moderately sized investor. Execution of the strategy with
the S&P 500 stocks requires a $25 million position in
stocks (Stoll and Whaley, 1986b).
3
4
Suppose a pension fund includes a stock portfolio similar
in composition to the S&P 500, and the fund manager must
ensure that the fund maintains a value above a certain
level, $10,000 for example. One way to achieve this
security is through the purchase of S&P 500 put options
with combined strike prices totaling $10,000. Should the
value of the stock portfol io fall below $ 10,000, the puts can
be exercised, earning for the fund a cash payment equal to
the shortfall between the current market value of the
stocks and the $10,000.
5
A position in a stock index future can serve the same
purpose.
6
See, for example, Kraus and Stoll (1972); Kawaller, Koch,
and Koch ( 1988) ; Edwards ( 1988) ; Finnerty and Park ( 1987) ;
or U.S. Congress (1985).
7
Their sample of nonexpiration days included only Thursdays from the years when stock index futures expired on
Thursdays, and Fridays from the years when expirations
were on Fridays. In this way, they controlled for possible
day-of-the-week effects.
8
See Fama (1965), Mandelbrot (1963), and Blattberg (1974).
9
The F-test is still a useful device, however, primarily for
summarizing comparisons of stock return volatilities from
different samples.
,0
In a nonparametric test like the one employed in this
study, using absolute values of returns gives the same
result as squared returns. Note also that the expectation
of the squared return equals the stock return variance,
should that variance exist.
1
' The instruments that are now governed by the new procedures are S&P 500 futures, options on S&P 500 futures,
some S&P 500 index options, New York Stock Exchange
(NYSE) Composite Index futures, and options on NYSE
Composite Index futures. The old rules still govern some
S&P 500 index options, S&P 100 index options, Major
Market Index futures and options, and Value Line Index
futures and options.
12
,3
May 5, 1987, was chosen as the terminal date for the prerule change period since it is halfway between the expiration of the March 1987 contract, the last to expire underthe
old rules, and the lune 1987 contract, the first to expire
under the new rules.
A four-year sample is roughly the same size as the previously described test samples.
References
Blattberg, Robert C , and Nicholas ). Gonedes. "A Comparison of the Stable and Student Distributions as
Statistical ModelsforStock Prices." Journal of Business 47
(1974): 244-80.
Edwards, Franklin R. "Does Futures Trading Increase Stock
Market Volatility?" Financial Analysts lournal 44 (January/
February 1988): 63-69.
Fama, Eugene F. "The Behavior of Stock Market Prices."Journal of Business 38 (January 1965): 34-109.
Finnerty, Joseph E., and Hun Y. Park. "Stock Index Futures:
Does the Tail Wag the Dog?" Financial Analysts Journal 43
(March/April 1987): 57-61.
French, Kenneth R. "Stock Return and the Weekend Effect."
lournal of Financial Economics 8 (March 1980): 55-69.
Galberson, William. "Futures and Options: How Risk Rattled Wall Street." New York Times, November 1, 1987.
Kawaller, Ira G„ Paul D. Koch, and Timothy W. Koch. "The
Relationship between the S&P 500 Index and S&P 500
Index Futures Prices." Federal Reserve Bank of Atlanta
Economic Review 73 (May/June 1988): 2-10.
Kraus, A., and H.R. Stoll. "Price Impacts of Block Trading in
the NYSE." Journal of Finance 27 (June 1972): 569-88.
18
Mandelbrot, Benoit. "The Variation of Certain Speculative
Prices." Journal of Business 36 (October 1963): 399-419.
McMurray, Scott, and Beatrice A. Garcia. "Wary Traders Brace
for Problems at Double Tri pie-Witching Time." Wall
Street Journal, June 12, 1987.
"SEC Staff Considering a Move to Lessen Stock Swings Tied
to Triple Expirations." Wall Street Journal, May 12, 1986.
Stoll, Hans R„ and Robert E. Whaley. "Expiration Day Effects
of Index Options and Futures." Monograph Series in
Finance and Economics, Monograph 1986-3. Salomon
Brothers Center for the Study of Financial Institutions,
Graduate School of Business Administration, New York
University, 1986a.
and
"Program Trading and Expiration Day Effects." Owens Graduate School of Management, Vanderbilt University Working Paper 86-31,
1986b.
, and
"Program Trading and Expiration Day Effects." Financial Analysts Journal 43
(March-April 1987): 16-28.
U.S. Congress. House. Committee on Agriculture. A Study of
the Effects on the Economy of Trading in Futures and
Options. 98th Cong., 2nd sess., 1985.
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
ATLANTA
THE
The
Preparing
Decade
Hyatt Regency Atlanta
Atlanta, Georgia
December 7-8, 1988
Banks face numerous challenges as this decade of unprecedented change in financial services draws to a
close. To address banking's future, the Atlanta Fed will host a conference on December 7 and 8 that will
bring together an impressive range of speakers from industry and government. Topics to be covered
include:
New Market Strategies for a C h a n g i n g Environment
Problems of Risk in the Financial System
W h a t the Next Session of Congress Promises for the Industry
Developments in Consumer a n d Community Regulations
The lively exchange of information among business, government, and Fed leaders will help you determine your business and banking strategies for the coming decade and beyond.
For more information on the conference, please contact Linda Donaldson, Conference Coordinator, at
(404) 521 -8747. You can also register for the conference using the form below. The deadline for conference
registration is November 18. Hotel arrangements must be made directly with the Hyatt Regency Atlanta at
(404) 577-1234. Discount airfare is available from Osborne Travel at 1 (800) 334-2087.
I
"1
REGISTRATION FORM
I
The B a n k i n g Industry: Preparing for t h e Next D e c a d e
December 7-8, 1988
Hyatt Regency Atlanta
Atlanta, Georgia
FEE $495
• Check/Money Order
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• Visa
Account No
Exp. Date
Signature
Name
Title
Organization
Address
City
State
ZIP
Payment must accompany registration form a n d be received by November 18.1 <»88. Makecheck payable to the Federal Reserve Bank of Atlanta a n d mail
with registration form to Linda Donaldson, Public Information Department, Federal Reserve Bank of Atlanta, 104 Marietta Street, N.W., Atlanta,
Georgia 30303-2713. Late registrations cannot be refunded. For additional information, call U n d a Donaldson, (404) 521-8747. For hotel accommodations, please call the Hyatt Regency Atlanta, (404) 577-1234, and mention Federal Reserve Bank Conference. Discount airfare is available
through Osborne Travel. Call I (800) 334-2087.
F E D E R A L R E S E R V E B A N K O F ATLANTA
19
Forecast Accuracy
and the
Performance of
Economic Policy:
Is There
a Connection?
William Roberds
Economic forecasts are not very accurate,
on average. This article presents the
theoretical arguments both for and against
associating this record of forecast accuracy
with the performance of countercyclical
macroeconomic policy.
22 E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 198
To suggest that economic forecasts are inaccurate is, for many people, to belabor the
obvious. Moreover, the reputation of economic
forecasters fares no better in the eyes of their
peers than in the court of public opinion. For
example, in a recent survey article Victor Zarnowitz (1986) finds that the overall accuracy of
economic forecasting in the United States has
not improved since the 1950s, despite a tremendous increase in the volume of economic
research and a steep drop in the cost of electronic computation. On a similar note, Allan H.
Meltzer (1987a) concludes that "on average,
forecast errors for output growth [that is, growth
in the real gross national product of the United
States! are so large . . . that it is generally not
possible to distinguish consistently between a
boom and a recession either in the current quarter or a year in advance."
While the mediocre record of economic forecasts is apparent, effective remedies to improve
forecasting remain elusive. (For a related discussion, see the article in this issue by Rogers,
"Improving Monthly Models for Economic Indicators," p. 34.) Though calculations by Bryan W.
Brown and Shlomo Maital (1981) support the
idea that economists' forecasts are unbiased—
that is, correct on average—these researchers
suggest that forecasts may be inefficient in the
strict mathematical sense: they may not make
the best use of all available information. Unfortunately, the methods used by Brown and Maital
to measure forecast efficiency do not demonstrate how forecasters can better utilize the
information available to them.
On a less abstract level, surveys byS.K. McNees
(1986), Zarnowitz, and others reveal that forecasters using widely disparate forecasting methodologies enjoy roughly the same level of forecast
accuracy. These surveys also present evidence
that the forecasting techniques used by professional forecasters are generally more accurate than simple extrapolative methods. Such
findings imply that no obvious way is available
to improve the accuracy of economic forecasts.
The author is an economist in the macropolicy section of the
Atlanta Fed's Research Department.
FEDERAL RESERVE BANK O F ATLANTA
In addition, the accuracy of these forecasts is
unlikely to improve in the near future.
This conclusion is hardly a comforting one for
policymakers who use economic forecasts in
their decision making. The uncertainty of economic forecasts raises the possibility that if
policy decisions are based on projections of the
future state of the economy, inappropriate or
counterproductive policy decisions will be
undertaken. Monetarists such as Milton Friedman have long argued that precisely this situation has prevailed in the Federal Reserve
System's conduct of monetary policy.1 Recently
Meltzer (1987a, b), another monetarist, has
taken Friedman's argument one step further by
suggesting that a policy of stabilization based
on inaccurate forecasts contributes to the overall uncertainty in the economy. According to this
view, the uncertainty associated with economic
forecasts does not result from any lack of skill on
the part of economic forecasters. Rather, as will
be explored later in this article, the policy process itself causes such uncertainty to be selfperpetuating.
Are the monetarists correct in attributing the
poor performance of economic forecasts to
policy decisions? Are economic forecasts of any
value in formulating economic policy? As will be
seen in this article, these questions represent
two sides of the same issue—that is, how much
weight should policymakers give to current
economic conditions in making their decisions?
This issue cannot likely be addressed using
purely empirical methods, since economists
cannot run controlled experiments on the economy to measure the effect of different policies.
Another way to analyze the issue is to consider,
as this article does, the various theoretical
arguments both for and against associating forecast accuracy with the performance of countercyclical policy. This article explores (1) why
traditional macroeconomic theories would reject such a linkage; (2) how the rational expectations models of the 1970s would support the
existence of precisely such a linkage, even
though it might be difficult to detect empirically; and (3) how some of the more recent work
with rational expectations models might qualify
the conclusions reached by many researchers in
the 1970s.
21
The Traditional View of the
Relationship between Policy
Performance and Forecast Accuracy
Mainstream economic theory in the 1950s and
1960s provided a natural role for policy in
economic systems. While the Keynesian models of this era were often complex in their
details, at their core always lay a particularly
simple assumption concerning people's reactions to government policy. In these models, the
behavioral rules describing the evolution of the
economy could not change in anticipation of
possible future policy effects. People would
have to wait until policies were enacted before
reacting to them. 2
An example of how this assumption works
should be famil iar to people who have taken an
introductory economics course. Students are
generally taught that an autonomous increase in
government spending will lead to an even larger
increase in private consumption. The ratio of
the increase in consumption to the increase in
government spending, according to this traditional theory, is presented as one over one
minus the marginal propensity to consume out
of income, multiplied by the marginal propensity to consume. So if the marginal propensity to
consume out of income is 90 percent or .9, then a
$1 increase in government spending leads to a
$ 10, that is, $ l/( 1 - .9), increase in income and a
$9, that is, $10 x .9, increase in consumption, as
shown in Chart 1. In this example, the basic
behavioral relationship is summarized by a
value of the marginal propensity to consume
(the slope of the consumption function), which
remains fixed regardless of the direction of
policy.
From the tone of this example, one can
imagine that such theories often led to a strong
role for economic policy. In fact, the strong
behavioral assumptions in these models led
economists to view the problem of setting
economic policy as essentially analogous to
manipulating physical systems. According to
most models of this era, people reacted to
policy changes much as, in the classical models
of Newtonian physics, physical systems react
to outside forces. The problem of smoothing
the economy's fluctuations around a stable
growth path was portrayed as being concep22
tually similar to stabilizing the temperature
inside a building.
A graphic example of such an abstract engineering control problem can help explain the
role of policy in these models. Chart 2 depicts
the time path of an abstract physical system that
would tend naturally to oscillate around some
average value, which is represented by the
black horizontal line in the chart. In an engineering context, the red line in Chart 2 might represent the daily temperature cycle in a building
with no climate controls or the annual runoff of
an undammed stream. In an economic context,
the red line could represent the fluctuations in
the growth rate of aggregate real output (real
GNP) around some trend value. For a physical
system with well-understood characteristics,
the mathematical theory of control allows engineers to manipulate the system so that it stays
close to its average value, or perhaps some
other desirable value. The macroeconomic models of the 1950s and 1960sgenerally implied that
the same approach could be applied to economic systems. By proper application of monetary and fiscal policy, these models held, the
economy could be "fine-tuned," or kept very
close to some desirable time path.
This view of the world has definite implications for the relationship between forecast error
and policy performance, as illustrated in Chart 3.
This chart depicts the same system as Chart 2,
but after the introduction of random influences.
(Without randomness, the system could be
forecast without error.) The wavy red line shows
how such a system might evolve when subject to
random shocks. In an economic context this
path might represent the growth rate of GNP
when the economy is subject to random influences—the weather, movements in world oil
prices, and so on—in the absence of any stabilization policy. The wavy black line in Chart 3
represents the evolution of the same system
after application of a "control" or policy designed to keep the system on average as near as
possible to its desired value, which is again represented by the horizontal black line. Notice
that even this "best" policy cannot keep the system exactly at its desired value; thus, errors in
forecasting the system will appear even under
the theoretically best policy.
In such systems, unpredictable fluctuations
will take place. Random, unforecastable flucE C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
tuations can occur in a system for two reasons.
First, at any given time the system will be subject to further random shocks that cannot be
foreseen. Second, it may not be possible to
measure accurately the current position of the
system given the information available. This
second source of uncertainty is clearly important in a macroeconomic setting, in which
policymakers usually have only imprecise information available on the current state of the
economy.3
Thus, Chart 3 illustrates that for systems subject to uncertainty, forecast errors are unavoidable, even after application of policies or controls
that stabilize the system. For systems such as
the one shown in Chart 3, also, the short-run
errors in forecasting the system will be larger
after the introduction of the stabilizing policy
than before. This difference in the degree of
errors occurs because actions designed to stabilize the system must be undertaken on the
basis of imperfect information about the state of
the system, thus introducing an extra element of
short-term randomness. However, this additional
element of randomness does not mean that a
policy is ineffective in reducing the average size
of the system's fluctuations around some desirable path. These last two points are apparent in
Chart 3. The path of the unstabilized system (the
wavy red line) is much smoother than the path of
the stabilized system (the wavy black line) and
hence more easily forecast in the short run.4
However, the stabilized system stays much
closer on average to the desirable system value
(the horizontal black line) than does the unstabilized system. To make use of a common
analogy in this literature, consider the act of
driving a car on a winding mountain road. Letting go of the steering wheel leads to easily predictable but undesirable consequences. The
consequences of steering are not as predictable
but certainly more desirable.
The foregoing example is useful because it
illustrates one way that the effectiveness of
countercyclical policy is linked to the size of
forecast errors. This example also shows why
most macroeconomic models of the 1950s and
1960s would not suggest that large errors in
forecasting economic aggregates are necessarily caused by poor policy choices. Indeed, these
models would suggest almost the opposite conclusion. That is, one would expect larger shortFEDERAL RESERVE BANK O F ATLANTA
Chart 1.
The Keynesian Consumption Function
C— Consumption
I — Investment
Along the 45° line, the national identity Y = C + I + G
holds (Income = Consumption + Investment + Government Expenditure). If government expenditure increases by
amount AG, then income increases by amount AY from Y1
to Y 2 . If the marginal propensity to consu me out of additional
income is .9 or 90 percent, then the increase in income leads
to an increase in consumption AC = .9 AY.
Mathematically, the Keynesian consumption function requires that
AC = .9AY,
(1)
since .9 = the marginal propensity to consume. At the same
time, the national income identity requires that
AY = AC + A I + AG.
(2)
If Al = 0 and A G = $1, we can substitute these values into
(2) to obtain
AY = A C + 1.
(3)
Substituting (3) into (1) and solving for AY implies that
AC = .9 (AC + 1);
.1 AC = .9;
AC = .9/.1 = $9.
(4)
run forecast errors in well managed economies,
everything else being equal. The larger forecast
errors would result from stabilizing policy actions
undertaken by policymakers using imperfect
information on the condition of the economy.
23
Chart 2.
Evolution over Time of a System without Random Effects
E
<D
t
CO
Average •
Value
Uncontrolled Function
Policy Performance
under Rational Expectations
The view of economic policymaking described
above was not shared by all macroeconomists
during the 1950s and 1960s. In particular, Friedman and other monetarists argued that macroeconomic policy, especially monetary policy,
should be set by using very simple guidelines
that would not attempt to "lean against the
wind" or actively seek to stabilize the course of
the economy. The most famous of these monetarist prescriptions for policy is the k percent
rule suggested by Friedman (1948, 1959). Under
this rule, the money supply should grow by a
certain prespecified percentage (k percent)
each year.
24
Time
One reason that monetarist views found relatively little acceptance during this period was
their lack of strong theoretical underpinning. In
1959 Friedman wrote that "there is little to be
said in theory for the rule that the money supply
should grow at a constant rate. The case for it is
entirely that it would work in practice."5 In the
1970s, however, the monetarist views on setting
policy found new theoretical support from macroeconomic models incorporating the hypothesis
of rational expectations. Briefly stated, this
hypothesis implies that people cannot be fooled
as a matter of course. On average, according to
rational expectations theory, market participants'
expectations about the future must be correct
and must utilize all information available at the
time of the forecast.6 In models incorporating
rational expectations, people are seen as acting
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Chart 3.
Evolution over Time of a System Subject to Random Influences
Average
Value
Time
in anticipation of future policy actions rather
than waiting for such actions to occur, as they do
in the traditional models described earlier in
this article.
The idea of rational expectations, introduced
by John F. Muth (1961), at first seemed a minor
technical innovation in abstract models of the
macroeconomy. In the 1970s, though, rational
expectations theory quickly changed the way
that many economists viewed the impact of
policy. To see the effect of Muth's idea, consider
the consumption example in Chart 1. This example assumes that consumers react to any autonomous increase in income by proportionately
increasing the amount of their consumption.
Thus, elevating consumers' income by increasing the amount of government purchases automatically boosts the amount consumed. But as
FEDERAL RESERVE BANK O F ATLANTA
demonstrated in Robert E. Lucas's research
(1976), this conclusion may change according to
how consumers view such an increase in government payments. If the increase is viewed as
being transitory—for example, lasting less than
one year before offsetting tax increases take
effect—consumption might change less than
proportionately with this increase in income or
perhaps not change at all. The point is that
under rational expectations, reactions to government policy changes are tempered by expectations of future government policy actions, and these expectations are assumed to
be correct on average.
Under the rational expectations hypothesis,
the credibility of the analogy between the problems of economic stabilization and physical
control is clearly strained. As noted by Lucas
25
and many others, people acting in their own
rational self-interest are free to take advantage of any changes in the rules of the marketplace and are not under any obligation to behave
in a way consistent with the statistical record.
Thomas J. Sargent (1986) argues this point with
the following sports metaphor. If the rules of
professional football were changed to allow
teams six downs to make ten yards, not many
teams would be punting on fourth down (even
though statistics would show that, under the old
rules, teams punted frequently in that situation).
Finn E. Kydland and Edward C. Prescott (1977)
were the first to demonstrate the general importance of these observations for evaluating such
"simplistic" policy prescriptions as the k percent rule for monetary policy. They argued that
simple rules offered the potential benefit of
reducing uncertainty concerning future policy
actions. In addition, the researchers pointed out
one area of public policy—patent law—where
such a simple rule for policy works relatively
well. Patents give the inventors of new technology a legal monopoly over their technology for a
fixed number of years. This legal monopoly provides an incentive for people to invest time and
money in new technology but also makes the
patented technology available to the public
after a fixed span of time.
Suppose, however, that a particularly important technology (for example, a cure for heart
disease) were to be patented. Then the temptation would exist to revoke the legal monopoly
of the patent holder to make this technology
widely available at minimal cost. Such a move
would have deleterious consequences for future
medical research, however, because it would
alter the expectations of other medical researchers. These researchers would then rightly
have to consider the possibility that the privilege of legal monopoly would not be extended
to any new technologies they might develop,
and their apprehension would probably result
in less inclination to invest in such technology.
As a result, a fixed time span for patents may
represent the best available choice for policy in
this area.
In the context of monetary policy, Kydland
and Prescott considered the following theoretical example. 7 In a simplified version of this
hypothetical construct, the two choices avail26
able for monetary policy are "sound money,"
which leads to low inflation, and "easy money,"
which leads to high inflation. The private sector
of the economy also has only two choices
(assuming that everyone in the private sector
will make the same decision): (1) to expect high
inflation and raise nominal wages accordingly,
or (2) to expect low inflation and raise nominal
wages only slightly. Individuals in the private
sector would prefer not to be surprised by inflation, so they enjoy a utility level (or a payoff) of
100 when their guess about inflation is correct,
and a utility level of 0 when their guess is wrong.
The government in this example most prefers
the situation where high inflation results when
the private sector expects low inflation and sets
nominal wages accordingly. In this case, the real
wage (wage adjusted for inflation) falls and
employment is increased. This situation is arbitrarily assigned a utility level of 100 for the
government. Less preferable to the monetary
authority is the situation where the private sector expects low inflation and low inflation results, since the real wage does not fall and
employment does not increase. This situation is
assigned a payoff level of 75. Even less preferable for the monetary authority is the situation
in which high inflation is both expected and
realized, since again no employment gains occur
but higher inflation results. Hence this situation
is assigned a utility level of 50. Least preferable
of all is the situation where high inflation is
expected and low inflation results, causing the
real wage to rise and employment levels to fall:
a utility level of 25 is assigned.
The possible outcomes in this example and
the potential rewards to both the private sector
and the government are summarized in Table 1.
Each pair of numbers indicates the utility or
reward going to the government and private
sector, respectively, under one particular outcome or state of the world.
Under the rational expectations hypothesis,
the only sustainable situations are those for
which the private sector's expectations are on
average correct. For example, suppose that the
government in this example has historically
followed a low inflation policy. Private individuals would then have an incentive to expect low
inflation to continue, since their payoff is higher
under this belief than under expectations of
high inflation (100 vs. 0). Suppose, though, that
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
government policy changed to the high inflation
policy. Initially, the result might be a dip in the
real wage and a surge in employment, if the
private sector did not anticipate the shift to a
high inflation policy. The government's utility
level would increase from 75 to 100 as a result of
the increase in employment. Statistical studies
of the data record at this point would probably
show a positive correlation between inflation
and employment, suggesting that high inflation
policies would lead to higher employment. So, if
the policy decision had been based purely on
the data record (that is, the "best available information"), the most likely choice would be to sustain the policy leading to high inflation. However,
rational expectations requires that the private
sector not be systematically misled concerning
the course of policy. In this example, the private
sector's utility level is reduced from 100 to 0
since the wrong course of government policy is
anticipated. Hence, a shift to policies leading to
sustained high inflation would lead to a change
in expectations and a fall in employment. The
government's payoff would fall from 100 to 50,
while the private sector's would rise from 0 to
100.
According to Kydland and Prescott's example, one negative consequence of basing policy
decisions on the statistical record would be
moving the economy from a low inflation state
(the upper left square in Table 1) to a high inflation state (the lower right square in Table 1)
without any offsetting increases in employment
or output. Another negative consequence of formulating policy in this fashion would be that
high inflation policies would tend to be selfsustaining. Suppose that the government, after
maintaining high inflation policies for a number
of years, tried to switch to policies compatible
with lower inflation. Since the private sector
would still be expecting high inflation, the
switch would result in higher real wages and a
decrease in employment. The data on the economy would again indicate a positive correlation
between the inflation rate and employment.
Policymakers basing their decision on the statistical record (and presumably, statistical forecasts) would again be led to the inferior choice
of policy—that is, a policy consistent with high
inflation.
Kydland and Prescott's theoretical analysis is
also useful in considering the relationship beFEDERAL RESERVE BANK O F ATLANTA
Table 1.
Government and
Private Sector Payoffs
under Rational Expectations
Private sector's expectations
Government Policy
Low inflation
High inflation
Low inflation
75,100*
25,0
High inflation
100,0
50,100
*The numbers indicate the payoff to the government and
the private sector, respectively, of each combination of
government policy and private sector expectations
regarding inflation.
tween forecast errors and the performance of
macroeconomic policy. If the notion is accepted
that people's decisions are based on correct
inferences about future policy actions, then the
analogy between physical and economic systems breaks down. According to these researchers' perception, the traditional relationship
between effective policy performance and forecast error is invalid because the relationship is
based on a model of the world that does not
take into account the strategic nature of economic policymaking. Under their view of the
world, the automatic need does not exist for a
policymaker to react to current information
about the economy. Attempts at stabilization
could be counterproductive, since they might
increase the degree of uncertainty concerning
people's expectations for the future course of
policy. As a result, additional short-term error
need not be introduced into the economy by
the actions of policymakers, as it is in the example shown in Chart 3. But if policymakers should
react to new information as it becomes available, some additional short-term forecast error
might be introduced. The difference is that
under Kydland and Prescott's analysis, readjusting policy to new data about the economy
may not always be the best thing to do because
it might adversely affect people's expectations
concerning future policy actions.
The 1970s rational expectations literature
made the issue of the link between forecast
accuracy and policy performance equivalent to
the issue of whether fixed rules or discretionary
27
policies would be more desirable for monetary
and fiscal policy.8 If policy is best set by fixed
rules, then any additional forecast error introduced into the economy by discretionary policy
only increases the aggregate uncertainty in the
economy without any offsetting benefits. But if
discretionary policy represents the best policy
choice, then the traditional logic applies and
larger short-term forecast errors might be desirable as a trade-off for longer-term stability.
Forecast Accuracy and
Policy Performance: Recent Research
While the theoretical contributions of the
1970s rational expectations literature show how
large forecast errors could be associated with
ineffective stabilization policies, the literature
stops short of demonstrating that this association has actually existed during the postwar
period (the only period for which there are reliable statistics on economic forecasts). Beginning with Lucas (1972), a rather large theoretical
and empirical literature focused on the role of
forecast errors in the money supply (which are
presumed to reflect unanticipated, discretionary policy actions on the part of the Federal
Reserve System) in generating random fluctuations in aggregate real output. However,
statistical attempts to measure the contribution
of money forecast errors to errors in forecasting
output have at best led to ambiguous results.9
Dissatisfaction with these findings has led to a
wide divergence of professional opinion on the
topic of macroeconomic fluctuations more generally and stabilization issues in particular.
Some of the more prominent lines of research
on business cycles and their implications for
stabilization policy are surveyed briefly below.10
The first of these is known as the real business
cycle approach." This approach incorporates
the idea of rational expectations but assigns no
causal role to money forecast errors in explaining cyclical fluctuations in real output. Instead,
random fluctuations in real output are assumed
to result only from uncontrollable random
shocks to productive technology. By definition,
this theory allows no role for money in determination of real output. Statistical correlations
between movements in the money supply and
28
in real output are explained as "reverse causation." The reasoning is that the money supply
will naturally expand and contract to accommodate the pace of real economic activity.
The real business cycle approach is a controversial line of research, and recent studies of
the postwar U.S. data record have presented
much empirical evidence both for and against it.
However, the implications of real business cycle
theory for monetary policy are unambiguous.
Since fluctuations in the money supply have by
assumption no effect on real output, this theory
sees countercyclical monetary policy as pointless. On the other hand, the theory implies that
monetary policy cannot contribute to forecast
errors in real economic quantities such as real
GNP.
"¡Tjhe theoretical contributions of the
1970s rational expectations literature
show how large forecast errors could
be associated with ineffective stabilization policies
"
A different way of analyzing macroeconomic
fluctuations has been advocated by Christopher
A. Sims (1982, 1986). His atheoretical approach
advocates a statistical method that does not
derive from any explicit economic theory. According to Sims, the major problem of the
earlier Keynesian models was not any flaw in
their theory but instead the relative unsophistication of their statistical implementation.
Hence the traditional analysis presented earlier
is applicable to policy problems as long as a
statistically valid model of the economic system
is used. This approach also differs from the
earlier Keynesian models in that it accepts the
logical validity of the criticisms of the rational
expectations literature. These criticisms are
viewed as empirically irrelevant, however, because of the essential nature of policy. Since
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
policy is usually being revised in response to
unforeseen circumstances, this approach views
it as unrealistic to focus on changing anticipations of policy actions as a singular, once-andfor-all event. The validity of this last argument
remains controversial and has been challenged
on both theoretical and empirical grounds.12
To summarize, this second view would accept
the traditional relationship between policy performance and forecast error, with the proviso
that stabil ization pol icy be carried out by means
of a statistically valid model of the economy.
Theoretical objections to this analysis may have
logical validity but are seen as having little practical significance.
A third important development in the macroeconomic literature of the 1980s has been the
"The possibility that much of the randomness in the economy could derive
from spurious sources suggests that
stabilizing policy measures would be
desirable to offset the effects of these
fluctuations."
construction of models that explore an interesting implication of the rational expectations
hypothesis. That is, nothing about the rational
expectations hypothesis precludes people from
making decisions on the basis of random,
spurious information (sunspots, hemlines, batting averages, and the like). As long as other
market participants also believe that such information is important in making economic decisions, then taking this information into account
is rational, even if such information has little or
nothing to do with the economy in a fundamental sense. For example, if an analyst thinks that
the stock market will react positively to an
increase in sunspots, the analyst's perceived
best interest would lead to buying and selling
stocks according to this information. If everyone
else in the stock market thinks the same thing,
FEDERAL RESERVE BANK O F ATLANTA
then such beliefs can become self-fulfilling.13
The possibility that much of the randomness
in the economy could derive from spurious
sources suggests that stabil izing pol icy measures
would be desirable to offset the effects of these
fluctuations. Ideally, monetary and fiscal policy
could be designed to offset price changes
induced by market fixation on spurious events
and thereby to reduce the overall randomness
in the economy. Yet, the effect of such policies
on forecast error would I ikely be the opposite of
the traditional effect described earlier in this
article. That is, such policies could reduce the
magnitude of forecast errors by eliminating
much of the perceived (but actually spurious)
uncertainty over prices, especially the prices of
commodities and financial claims traded on
organized exchanges. In practice, however, the
persons implementing such stabilizing policies
would be faced with deciding whether price
movements were reflections of spurious uncertainty or justified as indicative of changes in the
fundamental condition of the economy. Besides,
the literature on "sunspots" has not developed
to the point where much practical guidance has
been offered on this issue. As with the earlier
rational expectations literature, the major contribution of the sunspot literature has been to
demonstrate the possibility that stabilization
policy could be effective in the way outlined
above. That stabilization policy would be effective in this fashion remains to be shown.
Another open question is whether existing
forecasting technologies would be of much use
in filtering out the spurious components of price
movements. The drop in stock prices during late
October 1987, for example, is often seen in the
business press as having little or nothing to do
with any fundamental factor affecting the course
of the economy, either before or after the fact.
Yet most economists' real growth forecasts were
revised substantially downward as a result of
the crash. The Blue Chip Consensus forecast for
real GNP growth in fourth quarter 1987, based
on a survey of about 50 commercial forecasts,
fell from 2.6 percent at an annual rate in early
October 1987 to 1.5 percent. The actual figure for
this period was at last revision reported to be
6.1 percent, which does not inspire confidence
in economists' ability to extract fundamental
information about the economy from price
movements in financial markets.
29
Conclusion
Until the 1970s, most theoretical macroeconomists implicitly viewed the problem of setting
fiscal and monetary policy as analogous to that
of stabilizing a physical system subject to uncertainty. If such an analogy were valid, large errors
in forecasting the course of the economy would
of themselves be no cause for concern, especially over the short term. Under this analogy,
policies producing large short-term forecast
errors could also be those producing the greatest long-term overall stability. Also, even inaccurate forecasts of the future state of the economy would be useful to policymakers as long as
these forecasts made the best use of available information.
A major contribution of the theoretical macroeconomics literature of the 1970s was formalizing the monetarists' longstanding objections to the traditional way of thinking about
economic policy. Using the concept of rational
expectations, researchers were able to construct hypothetical examples in which simple
policy rules perform better than policies that
attempt to react to all currently available information and to forecasts predicated on this information. In such examples, ignoring the urge to
"lean against the wind" typically reduces the
overall uncertainty and the size of errors in
forecasting the economy. In these examples,
also, economic forecasts are seen as having
relatively little value for policymakers, since
policy itself should not be automatically changed
as a result of changes in the economic outlook.
Although the rational expectations literature
of the 1970s was successful in challenging the
then-prevalent Keynesian paradigm, it has not
been successful in producing a new consensus
theory of macroeconomic fluctuations. More
recent developments in macroeconomic theory
have generally attempted to refine and reinterpret the role of rational expectations in explaining fluctuations in the economy. However,
30
various branches of the literature have adopted
widely different approaches, each with disparate implications for the conduct of monetary
and fiscal policy. Consequently no consensus
viewpoint exists on whether better forecasts
lead to better policy, or vice versa. On this issue,
the best that this professional impasse can offer
policymakers is a menu of competing explanations, each with differing recommendations for
policy. Nonetheless, this literature is useful in
that it highlights the dimensions along which
countercyclical policy based on economic forecasts is likely to succeed or fail. These dimensions include the following:
• Policies based on pure statistical extrapolation are likely to be destabilizing if changes in
anticipations of those policies cause changes
in market conditions. However, the postwar
U.S. data record does not unambiguously support the view that changes in policy anticipations can explain a significant proportion of
the fluctuations in real output over this
period.
• The quantitative effects of changes in policy
anticipations may be less important if policy
must be continually changed. In this case
policy based on statistical extrapolation is
more likely to be effective.
• The overall effectiveness of countercyclical
monetary policy may be significantly limited
by the extent to which movements on the real
side of the economy can be attributed to random fluctuations in productivity.
• One potential role for countercyclical policy
might be to counteract the effects of price
movements resulting from the markets' fixation on spurious information. For policy to be
effective in this way, however, policymakers
must be better than the markets at distinguishing between spurious price movements
and those explained by changes in relevant
information about the economy.
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Notes
1
For a succinct and highly readable statement of this view,
see Friedman (1988).
2
The classic works on policy analysis in Keynesian models
are those of Tinbergen (1952,1956). Tinbergen's "theory of
economic policy" was formulated without the benefit of
the branch of mathematics known as control theory, which
is used in some of the examples in this article, because it
was in its infancy in the 1950s. However, the problems
addressed by Tinbergen closely resemble those addressed by control theory. Therefore, discussion of
stabilization policy using control theoretic constructs
seems justified. For an in-depth explanation of the
application of control theory to Keynesian macroeconomic
models, see Chow (1981).
3
In an economic setting, another plausible source of uncertainty is that policy actions themselves may be implemented only with error. Errors in implementing policy
could occur in a number of ways. For example, in many
democracies, the power to set economic policy is ultimately held by the legislative branch, but the day-to-day
implementation of those policies is carried out by the
executive branch. Errors in carrying out policy could result
from coordination problems between the two branches.
Since the effect of such error is similar to that of measurement error, this source of error is not considered in the
discussion above.
4
ln the long run, the stabilized system can theoretically be
forecast more accurately than the unstabilized system. In
practice, however, it is unclear how long the "long run"
might be, whereas the short run cannot get any shorter
than the next data period, for example, the next quarter
for series such as real G N P that are reported on a quarterly
basis. For this reason, the discussion of forecast accuracy
here is restricted to the short run.
5
See Friedman (1959): 98.
FEDERAL RESERVE BANK O F ATLANTA
6
O n e common misconception about rational expectations
is that it is often equated to the idea that people never
make mistakes in forecasting economic aggregates. That
is, "rational expectations" is assumed to mean that people have perfect foresight. In lieu of this unrealistic
assumption, the rational expectations hypothesis postulates that errors made by people in forecasting the
future must be unpredictable in any systematic way.
7
The version of the example used here was adapted by
Backus and Driffill (1985).
8
This article characterizes any policy that reacts to current
information as "discretionary." More subtle definitions of
this term are possible but outside the scope of this
article.
9An interesting study by Sims (1980) and a follow-up study
by Runkle (1987) illustrate the ambiguity of the postwar
U.S. data record on this issue. Sargent (1976) offers a
theoretical explanation of why it would be difficult to
determine empirically the contributions of money forecast errors to fluctuations in real output.
1
°For a more complete survey of developments in the macroeconomics literature since 1975, see Fischer (1988).
1
'See Prescott (1986) and the accompanying articles for an
introduction to this approach to analyzing macroeconomic fluctuations.
l2
1
See Sargent (1984) and Miller and Roberds (1987).
^The idea that financial markets are driven by spurious
information is hardly a new one. One contribution of the
recent literature has been to model formally the effects of
such spurious information in an abstract setting. Another
contribution of this literature has been to show that
volatile fluctuations in output and prices can result even
when there are no random factors influencing the economy. For a survey and extensive discussion of this literature, see Aiyagari (1988).
31
References
Aiyagari, S. Rao. "Economic Fluctuations without Shocks
to Fundamentals; Or, Does the Stock Market Dance to Its
Own Music?" Federal Reserve Bank of Minneapolis Quarterly Review (Winter 1988): 8-24.
Backus, David, and John Driffill. "Rational Expectations and
Policy Credibility Following a Change in Regime." Review
of Economic Studies 52 (1985): 211-21.
Brown, Bryan W„ and Shlomo Maital. "What Do Economists
Know? An Empirical Study of Experts' Expectations."
Econometrica 49 (March 1981): 491-504.
Capitol Publications, Inc. Blue Chip Economic
Indicators.
Various issues.
Chow, Gregory C. Econometric Analysis by Control Methods.
New York: John Wiley & Sons, 1981.
Fischer, Stanley. "Recent Developments in Macroeconomics."
The Economic Journal 98 (|une 1988): 294-339.
Friedman, Milton. "A Monetary and Fiscal Program for
Economic Stability." American Economic Review 38 ()une
1948): 245-64.
. A Program for Monetary Stability. New York:
Fordham University Press, 1959.
. "The Fed Has No Clothes." Wall Street Journal,
April 15, 1988.
Kydland, Finn E., and EdwardC. Prescott. "Rules Ratherthan
Discretion: The Inconsistency of Optimal Plans." journal
of Political Economy 85 (June 1977) : 473-91.
Lucas, Robert E. "Expectations and the Neutrality of Money."
Journal of Economic Theory 4 (April 1972): 103-24.
. "Econometric Policy Evaluation: A Critique." In
The Phillips Curve and Labor Markets, edited by Karl
Brunner and Allen H. Meitzer, vol. 1, 19-46. CarnegieRochester Conference Series on Public Policy. Amsterdam: North-Holland, 1976.
McNees, Stephen K. "Forecasting Accuracy of Alternative
Techniques: A Comparison of U.S. Macroeconomic Forecasts." Journal of Business and Economic Statistics 4
Oanuary 1986): 5-15.
32
Meltzer, Allan H. "Limits of Short-Run Stabilization Policy."
Economic Inquiry 25 (January 1987a): 1-14.
. "On Monetary Stability and Monetary Reform."
Bank of )apan Monetary and Economic Studies 5 (September 1987b): 13-34.
Miller, Preston J., and William Roberds. "The Quantitative
Significance of the Lucas Critique." Federal Reserve Bank
of Minneapolis Staff Report 109, 1987.
Muth, |ohn F. "Rational Expectations and the Theory of Price
Movements." Econometrica 29 duly 1961): 315-35.
Prescott, Edward C. "Theory Ahead of Business Cycle
Measurement." Federal Reserve Bank of Minneapolis
Quarterly Review (Fall 1986) : 9-22.
Runkle, D.E. "Vector Autoregressions and Reality." Journal
of Business and Economic Statistics 5 (October 1987):
437-42.
Sargent, Thomas ). "The Observational Equivalence of
Natural and Unnatural Rate Theories of Macroeconomics."
Journal of Political Economy 84 dune 1976): 631-40.
. "Autoregressions, Expectations, and Advice."
American Economic Review 74 (May 1984): 408-15.
. Rational Expectations and Inflation. New York:
Harper & Row, 1986.
Sims, Christopher A. "Comparison of lnterwar and Postwar
Business Cycles: Monetarism Reconsidered." American
Economic Review 70 (May 1980): 250-57.
. "Policy Analysis with Econometric Models."
Brookings Papers on Economic Activity I (1982): 107-52.
. "Are VAR Models Usable for Policy Analysis?"
Federal Reserve Bank of Minneapolis Quarterly Review
(Winter 1986): 2-16.
Tinbergen, ]an. On the Theory of Economic Policy. Amsterdam: North-Holland, 1952.
. Economic Policy: Principles and Design.
Amsterdam: North-Holland, 1956.
Zarnowitz, Victor. "The Record and Improvability of Economic Forecasting." Economic Forecasts 3 (December
1986): 22-30.
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
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FEDERAL RESERVE BANK O F ATLANTA
I
33
Improving Monthly Models
for Economic Indicators:
The Example of an Improved CPI Model
R. Mark Rogers
Participants in financial markets watch economic activity and inflation closely. Analysts
have incentives to forecast monthly indicators
accurately since interest rates and foreign exchange rates as well as equity prices can move
significantly should economic reports differ
from expectations. Thus, considerable resources
are devoted to forecasting economic indicators.
Forecasters' models typically employ the
most recent data available. Sometimes models
are almost accounting identities, that is, a summation of the indicator's officially defined components, or at least the major components. For
example, the U.S. Commerce Department's
Bureau of Economic Analysis (BEA) calculates
some components of wage and salary information using data from the U.S. Labor Department's Bureau of Labor Statistics (BLS) series
for earnings and for hours worked, which are
released before the BEA forecast in any given
month. Analysts often use percentage changes
in the product of earnings and hours worked to
The author is an assistant economist in the macropolicy
tion of the Atlanta Fed's Research Department.
34
sec-
forecast percentage changes in wages and salaries and hence personal income.
In addition to such accounting models, some
analysts use forecasting models that are more
behavioral; they try to project an indicator's
future performance based on data believed to
influence the indicator, but which are not components of it. For example, mortgage rates
influence housing starts but are not components of the latter data.
Some analysts forecast monthly changes in
the consumer price index (CPI) based on changes
in the producer price index (PPI). Typically, percentage changes in the CPI are forecast from
percentage changes in the PPI, since the latter
are released each month by the BLS about a
week to 10 days before the CPI report. The
assumption behind such models is that there is
predictable movement in prices from the producer level to those at the consumer level even
though the CPI encompasses items like services
that are not in the more commodity- or inputoriented PPI. Also, factors influencing profit
margins differ in the two indexes.
Whether monthly models are basically of the
accounting or behavioral type, they typically
have three attributes: (1) the model specification is simple, (2) the model is constructed with
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
aggregate data instead of separately estimated
components, and (3) the resulting forecasts generally are not very accurate. This article, using
five CPI models for examples, shows that significant improvement can be achieved with relatively simple changes in the specifications of
the "traditional" monthly CPI model and with
only modest disaggregation of the components.1 These five models are:
• a traditional monthly CPI model;
• a first difference model, which is based on
changes in inflation rates combined with a
lagged dependent variable;
• two models that incorporate variations on the
first difference model; and
• a multiequation model, in which different
components of the CPI and PPI are analyzed.
Improvement of monthly CPI forecasting
techniques presents a number of challenges for
analysts. First, the consumer and producer price
indexes are among the most accessible, widely
studied, and often cited indicators available to
financial analysts. Any greater understanding
that economists can gain regarding the performance of these indexes would have implications
for popular economic discourse. The forecasting itself is also challenging since parts of the
CPI are not included in the PPI, making certain
adjustments necessary when performing component analysis of the two indexes.
The research presented in this article moves
step by step away from the traditional forecasting model until a number of different models
have been developed. The accuracy of each of
the resulting forecasting models is analyzed.
One factor to remember when reading about
these different models is that they are designed
to forecast an indicator based on the change in a
second indicator that is reported only seven to
ten days earlier. These forecasting models are
thus designed more to study the impact of
short-term fluctuations in the market than to set
the stage for long-term policy decisions.
The Traditional Monthly CPI Model
When economists forecast changes in the
consumer price index, the most current explanFEDERAL RESERVE BANK O F ATLANTA
atory data are used. Since the CPI numbers
themselves are obtained from the U.S. Census
Bureau's Current Population Surveys, no "prior"
component data series exist from which the CPI
data are derived for the forecast month. Therefore, an accounting-type forecasting model is
not appropriate for CPI projections. However,
PPI data have been released for the forecast
month and, as a result, can be used in the creation of CPI models. These models are generally
represented by the equation:
%ACPIt = a, (%APPIt) + C +
et,
where %ACPI is the monthly percentage change
in the consumer price index, %APPI is the monthly
percentage change in the producer price index
(for finished goods), t is the current time period,
a | is the regression coefficient for % A PPI (based
on a regression using one of two statistical
techniques—ordinary least squares or, as explained below, Cochrane-Orcutt), C is a constant, and et is the error term. Table 1 shows the
regression results for such a model using both
techniques over the period 1980-87. A longer
observation period could be used, but recent
history is more interesting, given the increased
volatility of the data. 2 (The price indexes used in
this study underwent structural changes around
1980 that seem to have made them more volatile and less predictable. If the true nature of
these price series has in fact changed in recent
years, it makes sense to weight more recent
observations more heavily when specifying a
forecasting model.)
From the information provided in Table 1,
Model 1 a appears to be reasonably acceptable
from a statistical perspective. First, as expected,
the coefficient of the percentage change in the
PPI is positive. Also, the t-statistics for the two
independent variables (the percentage change
in the PPI and the constant) are statistically
significant. However, the forecasting accuracy of
the model is not very good. Essentially, for the
regression period, the mean absolute percentage change in the CPI is 0.456 while the mean
absolute error of this model is 0.213, almost half
of the typical percentage change.3 (Mean absolute error is the average of the model's forecast errors, ignoring plus or minus signs—that is,
using absolute values of the misses. Using
absolute values avoids the "my feet are in the
35
oven, my head is in the freezer, so on average I
feel fine" problem that results when the minus
signs are not removed before averaging.) Also,
given that the standard deviation of the mean
absolute error is 0.188, one can conclude that
this model does not produce very accurate
forecasts. This certainly is a reasonable conclusion when the average error is about half that of
the typical percentage change in the CP1. Of
course, the high standard error makes the forecast reliability even lower. The adequacy of the
traditional forecasting model should be seriously questioned.
The forecasting accuracy of the traditional
model can be improved using the CochraneOrcutt estimating procedure, but glaring deficiencies still remain. The Cochrane-Orcutt
procedure is a standard statistical method used
to remove the serial correlation of errors from a
linear regression (prediction) model. Serial correlation of errors refers to the fact that an earlier
period's forecast error contains some statistical
information about the current period's forecast
error. This characteristic, in turn, means that the
forecasting (regression) model has not exploited all available information in its predictions,
since some predictive power or discernible pattern remains in the errors of the model. The
forecast errors of a good forecasting model
should be entirely random; the Cochrane-Orcutt
procedure is one way of obtaining such randomness. As shown in Model lb, the R-squared is
higher, the Durbin-Watson statistic is near 2
(indicating that the model now has taken into
account most serial correlation problems), and
the mean absolute error and its standard error
are noticeably lower. In effect, this version of the
traditional model should forecast more accurately than the version using ordinary least
squares. Though forecasting accuracy is improved, use of the Cochrane-Orcutt technique
has indicated that deficiencies do exist in the
traditional model.
Using the Cochrane-Orcutt estimation technique, the t-statistics for the coefficients of the
independent variable (percentage change in
the PP1) and the constant fell considerably
(shown in Table 1), as is typical when significant
serial correlation exists. Furthermore, the key
explanatory variable in Model lb, the percentage change in the PPI, holds little predictive
value. Its coefficient is negligible and statis36
tically unreliable. While this version of the
traditional model provides better forecasts
(based on the mean absolute error and its standard deviation), the Cochrane-Orcutt procedure
suggests that the primary forecasting variable is
Rho along with the constant. (Rho is a coefficient
or "estimated parameter" in the Cochrane-Orcutt
procedure that indicates the average effect of
one forecast error on the next forecast error.)
Thus, as modeled above, the past history of the
CPI is a better predictor of this month's CPI than
is this month's PPI for finished goods. While past
history, or Rho, provides a better forecast for a
typical percentage change in the CPI than does
the PPI, past history may provide little help in
forecasting turning points in inflation. Theoretically, the PPI would provide help here, but
Model 1 b gives I ittle weight to the PPI relative to
Rho; this conclusion suggests that this model
specification is not appropriate. The next section of this article, though, shows how CPI
forecasting improvements can be made through
incremental changes in the projection model.
Model Improvement through
Changes in Model Specification
The First Difference Model. While most
market economists probably find the traditional model intuitively appealing, they might also
describe an entirely different scenario when
discussing how changes in the PPI pass through
to changes in the CPI. The earlier monthly model
is based on the following thinking: "a percentage change in the PPI for a given month leads to
a given percentage change in the CPI with the coefficient reflecting differences in price margins
between the retail and wholesale levels." Instead, a typical explanation provided by an
analyst might read, "the PPI inflation rate rose
X percentage points from the month before, and
so the CPI inflation rate is expected to jump by
Y percentage points from last month; furthermore, the difference in thechange in these inflation rates reflects differences in price margins
from wholesale to retail levels." In effect, this
latter reasoning suggests a model based on
changes in inflation rates—or first differencescombined with a lagged dependent variable (that
is, values of the CPI percent changes in earlier
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Table 1.
Model 1a: The Traditional Monthly CPI Model
(Ordinary
Least Squares Estimation
Technique)
Dependent Variable: %ACPI
Regression Period: 1/80-12/87
Independent
Variables
Coefficient
Standard
Error
t-statistic
%APPI
0.466450
0.612794E-01
7.61186
Constant
0.302377
0.338458E-01
8.93396
Number of Observations: 96
Mean of Dependent Variable: 0.426180
Standard Error of Regression: 0.290821
Mean Absolute Error: 0.213476
Root Mean Squared Error: 0.284028
R 2 : 0.3813
R2-Adjusted: 0.3748
Durbin-Watson: 1.36665
Standard Deviation of Mean Absolute Error: 0.188343
Model 1 b: The Traditional Monthly CPI Model
(Cochrane-Orcutt
Estimation
Technique)
Dependent Variable: %ACPI
Regression Period: 2/80 -12/87
independent
Variables
Coefficient
Standard
Error
t-statistic
%APPI
0.114030
0.678100E-01
1.68161
Constant
0.368158
0.667769E-01
5.51325
Rho
0.601105
0.819931E-01
7.33116
Number of Observations: 95
Mean of Dependent Variable: 0.414682
Standard Error of Regression: 0.252936
Mean Absolute Error: 0.188996
Root Mean Squared Error: 0.250259
months). The use of lagged values of the CPI is
necessary for the first difference model suggested by the second line of thinking presented
above. Since a jump in the inflation rate of the
CPI is being related to a jump in the PPI inflation
rate, there should be a first difference term for
the dependent variable (%ACPIt - %ACPIt _,).
However, by adding %ACPl t ., to the left-hand
side of the equation, the dependent variable
becomes the more familiar %ACPIt, which when
added to the right-hand (explanatory) side becomes the lagged dependent variable. The suggested model is created to address the question
FEDERAL RESERVE BANK O F ATLANTA
R 2 : 0.4891
R2-Adjusted: 0.4836
Durbin-Watson: 2.15921
Standard Deviation of Mean Absolute Error: 0.164914
of whether acceleration in the CPI is related to
similar movement in the PPI. This model, Model 2, is represented by the equation:
%A CPIt = a] (%A
CPIt.,)
+ a2 (DPPIt)
+ C +
et,
where %ACPIt_ | is a lagged dependent variable and DPPIt is the change in the PPI inflation
rate from period £-1 to period f. In other words,
DPPIt is e q u a l to (%APPI t - %APPlt.,
). If t h e per-
centage change in the PPI was 0.6 percent in
April, and 0.4 percent in May, the DPPI would be
37
Table 2.
Model 2: First Differences Monthly CPI Model
Estimation Technique: Almon, 4 period lags, second order polynomial
Dependent Variable: %ACPI
Regression Period: 1/80-12/87
Independent
Variables
Coefficient
Constant
0.100600
Standard
Error
t-statistic
2.19302
0.458729E-01
Distributed Lag Interpretation, %ACPI
Lag
Coefficient
Standard
Error
t-statistic
1 month
0.4864
0.8685E-01
5.601
2 month
0.2250
0.2510E-01
8.963
3 month
0.5674E-01
0.4752E-01
1.194
4 month
-0.1824E-01
0.4421 E-01
-0.4127
Mean Lag: Not Meaningful
Sum of Lag Coefficients: 0.749887
Standard Error: Not Meaningful
Standard Error: 0.836693E-01
Distributed Lag Interpretation, DPPI
Standard
Error
Lag
Coefficient
t-statistic
Current month
0.1048
0.6486E-01
1 month lag
0.8084E-01
0.5861 E-01
1.379
2 month lag
0.5539E-01
0.6200E-01
0.8933
3 month lag
0.2844E-01
0.4474E-01
0.6358
1.616
Mean Lag: 1.02776
Sum of Lag Coefficients: 0.269466
Standard Error: 1.69606
Standard Error: 0.195351
Number of Observations: 96
Mean of Dependent Variable: 0.426180
Standard Error of Regression: 0.268192
Mean Absolute Error 0.185914
Standard Deviation of Mean Absolute Error 0.169281
R 2 : 0.508627
R2-Adjusted: 0.487028
Durbin-Watson: 1.849057
Durbin-Watson(4): 2.141449
Root Mean Squared Error 0.250835
Note: Mean lags and their standard error are not meaningful when coefficients for the lags change sign.
-0.2 percent. Table 2 contains the regression
results for this model.
In addition to specifying the change in inflation rates, a different estimation technique is
incorporated into this model. Distributed lags
38
are used in the model specification along with
the Almon distributed lag procedure, a standard technique in estimating regression models that involve distributed lags (collections of
the same variable, dated at different times).4
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Almon lags are estimated for both independent variables: (1) the lagged %ACPI and (2) the
first difference variable for %ÀPPI. A number
of lag structures were examined. After some
experimentation with the lags and order of the
polynomial, a lag period of four months with lag
coefficients following a second degree polynomial was judged to be good for forecast requirements. These constraints imply that the
lags follow a curve with only one inflection point,
after which the values taper near to zero in the
final period.
This first difference model provides interesting behavioral insight into the lag structure of
the variables. For the lagged dependent variable, the value for the previous month is the key.
The coefficient for the two months previous is
just under half that of the one month lag while
lags three and four months are of little significance. The lag structure for the first difference
variable DPPI is perhaps even more interesting.
A significant portion of the influence of changes
in %ACPI is in the current period—that for which
the CPI is being reported. This period accounts
for about 40 percent of the PPl's influence on
percentage changes in the CPI, and the one
month lag represents about 30 percent of the
impact. (The 40 percent figure is this lag period's
coefficient divided by the sum of the coefficients for this variable.) Of course, the comparison is meaningful only if the coefficients are
of the same sign. Coefficients for three and four
month lags are of diminishing importance.
Interestingly, although this first difference
model, using Almon lags, has provided insight
into the lagged effects of the PPI on the CPI, it
has not provided a more accurate forecasting
model. The mean absolute error is barely lower
than for Model I b, while the standard deviation
for Model 2's mean absolute error is actually
slightly higher. However, Model 2 provides the
basic structure for an improved, modified singleequation model. Later in this article, Model 2
will also help provide the basis for a multiequation model.
The First Difference Model with a Structural
Shift Variable. As mentioned earlier, the first
difference model was created to determine
whether acceleration in the PPI is related to that
for the CPI. The problem with Model 2's specification is that the factors affecting the passthrough of inflation from the wholesale level to
FEDERAL RESERVE BANK O F ATLANTA
the retail level may change over the business
cycle or in response to both long-term and sudden structural changes, such as large oil price
shocks. While Model 2 attempts to quantify the
pass-through of inflation from the wholesale to
the retail level, it does not address the impact of
structural shifts as indicated by longer-term
changes in the CPI/PPI inflation differential. The
term SHIFTt is used in this article to represent
the CPI inflation rate minus the PPI inflation rate.
Of course, for a given forecast month, this variable is not yet available because of the missing
CPI data. Hence, the variable tested is for the
prior period, SHIFTt.,. As discussed below, distributed lags are used in order to measure the
impact of this differential over several months,
thereby quantifying the impact of a prevailing
trend rather than the noise of one-month percentage changes.
The Almon estimation procedure is employed
with a lag structure of four periods, and the distributed lag coefficients are assumed to follow a
second degree polynomial. The far end is constrained to zero. All three lagged dependent
and independent variables follow this structure. As shown in Table 3a, the regression results
of this model structure suggest that the specification is somewhat lacking, although only
minor changes may be sufficient to achieve
desired model properties.
Several properties of this model are immediately noteworthy. Lagged %ACPI has coefficients thatchange sign, but the primary lag is in
the first month, as expected, and has a positive
sign. The third and fourth month lags have small,
negative coefficients and are not significant. The
variable DPPI (the change in the PPI inflation
rates) has a positive coefficient in the current
month, but it is more than offset by negative
coefficients for the remaining lagged months.
This result is not theoretically satisfying, to say
the least, and is contrary to the Model 2 distributed lag results for this variable, as well as to
theoretical expectations. For the variable SHIFT,
the coefficient changes sign in the last lagged
month, which would be of minor importance if
the coefficients of DPPI were more certain. The
extensive use of Almon lags may have led to
multicollinearity problems. 5 While negative
coefficients for SHIFT maybe plausible, they are
not expected for the key variable, DPPI. At this
point, one can assume that for DPPI, the primary
39
Table 3a.
Model 3a: First Differences With Shift Variable Monthly CPI Model
Estimation Technique: Almon, 4 period lags, second order polynomial
Dependent Variable: %ACPI
Independent
Variables
Coefficient
Constant
0.198789
Standard
Error
0.476192E-01
Regression Period: 1 /80 -12/87
t-statistic
4.17455
Distributed Lag Interpretation, %ACPi
Lag
Coefficient
Standard
Error
t-statistic
4.228
1 month
0.7352
0.1739
2 month
0.2203
0.2292E-01
9.615
3 month
-0.7379E-01
0.8814E-01
-0.8372
4 month
-0.1472
0.8684E-01
-1.696
Mean Lag: Not Meaningful
Sum of Lag Coefficients: 0.734466
Standard Error: Not Meaningful
Standard Error: 0.763839E-01
Distributed Lag Interpretation, DPPI
Lag
Current month
Coefficient
0.2166
Standard
Error
0.6816E-01
t-statistic
3.178
1 month
-0.6361 E-01
0.1348
-0.4718
2 month
-0.1931
0.1675
-1.153
3 month
-0.1719
0.1236
-1.391
Mean Lag: Not Meaningful
Sum of Lag Coefficients: -0.212029
Standard Error: Not Meaningful
Standard Error: 0.449407
Distributed Lag Interpretation, SHIFT
Standard
Error
t-statistic
Lag
Coefficient
1 month
-0.4086
0.1614
-2.531
2 month
-0.1786
0.4074E-01
-4.383
3 month
-0.3380E-01
0.8132E-01
-0.4156
4 month
0.2573E-01
0.7768E-01
0.3312
Mean Lag: Not Meaningful
Sum of Lag Coefficients: -0.595244
Standard Error: Not Meaningful
Standard Error: 0.135807
Number of Observations: 96
Mean of Dependent Variable: 0.426180
Standard Error of Regression: 0.240054
Mean Absolute Error: 0.166281
Standard Deviation of Mean Absolute Error: 0.159465
R 2 : 0.600898
R 2 -Adjusted: 0.573992
Durbin-Watson: 2.009688
Durbin-Watson(4): 2.078819
Root Mean Squared Error: 0.229806
40
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Table 3b.
Model 3b: First Differences With Shift Variable Monthly CPI Model
Estimation Technique: Almon, 4 period lags, second order polynomial
Dependent Variable: %ACPI
Independent
Variables
Coefficient
Regression Period: 2/80 -12/87
Standard
Error
t-statistic
DPPI
0.179168
0.701371E-01
2.55454
Constant
0.188042
0.481077E-01
3.90878
Distributed Lag Interpretation, %ACPI
Lag
Coefficient
Standard
Error
t-statistic
1 month
0.5696
0.8519E-01
6.686
2 month
0.2111
0.2365E-01
8.929
3 month
4 month
-0.3257E-02
0.4449E-01
-0.7321 E-01
-0.7364E-01
0.4186E-01
-1.759
Mean Lag: Not Meaningful
Sum of Lag Coefficients: 0.703822
Standard Error: Not Meaningful
Standard Error: 0.788271 E-01
Distributed Lag Interpretation, SHIFT
Lag
Coefficient
Standard
Error
t-statistic
1 month
-0.2384
0.7172E-01
-3.324
2 month
-0.1382
0.4078E-01
-3.390
3 month
-0.6514E-01
0.4261 E-01
-1.529
4 month
-0.1906E-01
0.3360E-01
-0.5673
Mean Lag: 0.706835
Sum of Lag Coefficients: -0.460815
Standard Error: 1.03390
Standard Error: 0.135934
Number of Observations: 95
Mean of Dependent Variable: 0.414682
Standard Error of Regression: 0.243381
Mean Absolute Error: 0.177663
Standard Deviation of Mean Absolute Error 0.155511
R 2 : 0.547282
R2-Adjusted: 0.521849
Durbin-Watson: 1.926097
Durbin-Watson(4): 2.229887
Root Mean Squared Error: 0.235570
effect is likely in the current period, and by
eliminating the lagged structure for that variable, a "cleaner," more statistically satisfying
model may result. Model 3b, the next model
discussed in this article, is of the same specification as Model 3a except that DPPI does not have
a lagged structure.
The Modified First Difference Model with a
Structural Shift Variable. As shown in Table 3b,
FEDERAL RESERVE BANK O F ATLANTA
dropping the distributed lag structure from
DPPI leads to results more consistent with
theoretical expectations: DPPI has a positive
coefficient while the distributed lag coefficients
of the other two variables are similar to those
estimated in Model 3a. For the lagged dependent variable, the sum of the lag coefficients is
positive and relatively high, suggesting that
lagged %ACPI provides most of the explanatory
41
Table 4a.
Model 4a: The Food Component
Estimation Technique: Almon, 4 period tags, second order polynomial
Dependent Variable: %ACPI a
Regression Period: 1/80-12/87
Independent
Variables
Coefficient
DPPIa
0.168529
0.327195E-01
5.15071
Constant
0.135457
0.516263E-01
2.62381
Standard
Error
t-statistic
Distributed Lag Interpretation, %ACPI a
Lag
Coefficient
Standard
Error
t-statistic
1 month
0.2827
0.7744E-01
3.650
2 month
0.2099
0.3406E-01
6.163
3 month
0.1386
0.4565E-01
3.036
4 month
0.6860E-01
0.3938E-01
1.742
Mean Lag: 0.990127
Sum of Lag Coefficients: 0.699771
Standard Error: 0.252148
Standard Error 0.113543
Distributed Lag Interpretation, SHIFTa
Standard
Error
Lag
Coefficient
1 month
-0.3024
0.4630E-01
-6.530
2 month
-0.7933E-01
0.2921 E-01
-2.716
3 month
0.4541 E-01
0.2928E-01
1.551
4 month
0.7185E-01
0.2219E-01
3.238
t-statistic
Mean Lag: Not Meaningful
Sum of Lag Coefficients: -0.264437
Standard Error: Not Meaningful
Standard Error 0.973780E-01
Number of Observations: 96
Mean of Dependent Variable: 0.346094
Standard Error of Regression: 0.257348
Mean Absolute Error: 0.200752
Standard Deviation of Mean Absolute Error 0.149903
R 2 : 0.479498
R2-Adjusted: 0.450581
Durbin-Watson: 1.650386
Durbin-Watson(4): 1.805803
Root Mean Squared Error: 0.250072
power of the model. The coefficients of the
lagged variable SHIFTt are all negative.
An important aspect of understanding the
economic meaning of this shift variable is that
theCPl/PPl inflation differential is usually negative over the period of estimation, and when
positive, it is not drastically above zero. There42
fore, when the PPI trend inflation rate rises, this
differential usually turns negative or becomes
more negative. A rise in the PPI trend inflation
rate therefore lowers the differential but raises
the CPI forecast because of the negative coefficient. In economic theory, this movement is
plausible because, when the PPI trend inflation
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
«
Table 4b.
Model 4b: The Energy Component
Estimation Technique: Almon, 4 period lags, second order polynomial
Dependent Variable: %ACPI b
Regression Period: 1/80-12/87
Independent
Variables
Coefficient
DPPIb
0.365905
0.387541 E-01
9.44169
Constant
0.381572
0.115530
3.30280
Standard
Error
t-statistic
Distributed Lag Interpretation, %ACPI b
Lag
Coefficient
Standard
Error
1 month
0.3879
0.6050E-01
2 month
0.1526E-01
0.3766E-01
t-statistic
6.413
0.4053
3 month
-0.1736
0.4443E-01
-3.908
4 month
-0.1787
0.3516E-01
-5.083
Mean Lag: Not Meaningful
Sum of Lag Coefficients: 0.508764E-01
Standard Error: Not Meaningful
Standard Error: 0.125537
Distributed Lag Interpretation, SHIFTb
Lag
Coefficient
Standard
Error
t-statistic
1 month
-0.4276
0.5875E-01
-7.279
2 month
-0.2457
0.4103E-01
-5.989
3 month
-0.1138
0.3956E-01
-2.877
4 month
-0.3191E-01
0.2878E-01
-1.109
Mean Lag: 0.694785
Sum of Lag Coefficients: -0.819095
Standard Error: 0.428846
Standard Error: 0.136758
Number of Observations: 96
Mean of Dependent Variable: 0.165543
Standard Error of Regression: 0.990602
Mean Absolute Error: 0.730886
Standard Déviation of Mean Absolute Erron 0.614524
R 2 : 0.643226
R 2 -Adjusted: 0.623405
Durbin-Watson: 1.873166
Durbin-Watson(4): 1.901712
Root Mean Squared Error 0.952816
rate rises relative to the CPI trend inflation rate,
there is cost-push pressure to raise prices at the
consumer level. Profit margins are reduced,
meaning that, overall, as producer costs accelerate, greater pressure builds to raise consumer
prices. In terms of interpreting the model solution, the negative coefficients of the shift variFEDERAL RESERVE BANK O F ATLANTA
able may be difficult to understand until one
realizes that the differential is typically negative
or quickly becomes negative as PPI inflation
accelerates.
While Model 3b is more appealing theoretically than Model 3a, the summary statistics are
not quite as good. Fortunately, these differ43
Table 4c.
Model 4c: The Commodities Less Food and Energy Component
Estimation Technique: Almon, 4 period lags, second order polynomial
Dependent Variable: %ACPI c
Regression Period: 1/80-12/87
Independent
Variables
Coefficient
DPPIC
0.149648
0.607590E-01
2.46297
Constant
0.788119E-01
0.414196E-01
1.90277
Standard
Error
t-statistic
Distributed Lag Interpretation, %ACPI c
Lag
Coefficient
Standard
Error
t-statistic
1 month
0.6276
0.1113
5.640
2 month
0.2387
0.2804E-01
8.511
3 month
0.4397E-02
0.5623E-01
-0.7819E-01
4 month
-0.7516E-01
0.5369E-01
-1.400
Mean Lag: Not Meaningful
Sum of Lag Coefficients: 0.795532
Standard Error: Not Meaningful
Standard Error: 0.934713E-01
Distributed Lag Interpretation, SHIFTC
Lag
Coefficient
Standard
Error
t-statistic
1 month
-0.2268
2 month
-0.1598
0.3482E-01
-4.588
3 month
-0.9960E-01
0.4499E-01
-2.214
4 month
-0.4635E-01
0.3845E-01
-1.205
0.7638E-01
-2.970
Mean Lag: 0.935148
Sum of Lag Coefficients: -0.532541
Standard Error: 0.755781
Standard Error: 0.116078
Number of Observations: 96
Mean of Dependent Variable: 0.373705
Standard Error of Regression: 0.207770
Mean Absolute Error: 0.154781
Standard Deviation of Mean Absolute Error: 0.130596
R 2 : 0.524526
R 2 -Adjusted: 0.498111
Durbin-Watson: 1.945978
Durbin-Watson(4): 1.819621
Root Mean Squared Error: 0.202072
ences are slight and Model 3b is still an improvement over Model 2. Compared to the trad itional
model, the forecasts of this specification are
more accurate. More importantly, Model 3b provides the structural form for the multiequation
model, as explained in the next section of this
article.
44
The Multiequation Model
The rationale for estimating percentage
changes in the CPI with an aggregated component model is that individual components of the
CPI inherently may have different coefficients
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
»
Table 4d.
Model 4d: The Services Less Energy Component
Estimation Technique: Almon, 4 period lags, second order polynomial
Dependent Variable: %ACPI d
Regression Period: 1/80-12/87
Independent
Variables
DPPI
Constant
Coefficient
-0.136744
0.234071
Standard
Error
0.103591
0.719659E-01
t-statistic
-1.32004
3.25253
Distributed Lag Interpretation, %ACPI d
Lag
Coefficient
Standard
Error
t-statistic
1 month
0.3745
0.1062
3.528
2 month
0.2074
0.3003E-01
6.907
3 month
0.8923E-01
0.5640E-01
1.582
4 month
0.2010E-01
0.5282E-01
0.3806
Mean Lag: 0.645398
Sum of Lag Coefficients: 0.691248
Standard Error: 0.368663
Standard Error: 0.100085
Distributed Lag Interpretation, SHIFT
Lag
Coefficient
1 month
0.5962E-01
Standard
Error
0.1249
t-statistic
0.4774
2 month
-0.1505
0.6294E-01
-2.391
3 month
-0.2305
0.6644E-01
-3.469
4 month
-0.1803
0.5445E-01
-3.311
Mean Lag: Not Meaningful
Sum of Lag Coefficients: -0.501669
Standard Error: Not Meaningful
Standard Error: 0.209795
Number of Observations: 96
Mean of Dependent Variable: 0.563920
Standard Error of Regression: 0.371341
Mean Absolute Error: 0.239351
Standard Deviation of Mean Absolute Error: 0.270907
R 2 : 0.429800
R 2 -Adjusted: 0.398122
Durbin-Watson: 2.114584
Durbin-Watson(4): 2.240218
Root Mean Squared Error: 0.360427
even though the basic structure for each is the
same (based on Model 3b). Thus, the aggregated forecast may be more accurate than the
single-equation Model 3b.
In order to create a reasonable component
model, this article bases each component on a
CPI dependent variable with a corresponding
FEDERAL RESERVE BANK O F ATLANTA
PPI independent variable. The four CPI components chosen are (1) food, (2) energy, (3) commodities less food and energy, and (4) services
less energy. The relative importance (weights)
figures for these components sum to 100 or the
CPI total. For the first three CPI components, the
PPI components that basically correspond are
45
Table 5.
Summary Statistics for February 1980-December 1987
R2
Model
R 2 -Adjusted
Mean
Absolute
Error
Standard
Deviation
of Mean
Absolute Error
Root Mean
Squared Error
Model 1 a
Traditional with OLS
0.3813
0.3748
0.213476
0.188343
0.284028
Model 1b
Traditional with
Cochrane-Orcutt
0.4891
0.4836
0.188996
0.164914
0.250259
Model 2
Almon with Lagged
Dependent Variable
and First Differences
in PPI Inflation
0.508627
0.487028
0.185914
0.169281
0.250835
Model 3a
Almon for Model 2
with the Addition of
Structural Shift Variable
(%ACPI - %APPI)
0.600898
0.573992
0.166281
0.159465
0.229806
Model 3 b
Model 3a Using Almon
Except for the First
Difference Variable
0.547282
0.521849
0.177663
0.155511
0.235570
Model 4
Four Equation Composite
Based on Model 3b
Specification
0.613312*
N.A.
0.166072
0.142370
0.218256
Correlation
coefficient squared between actual and forecast data.
(1) finished consumer foods, (2) finished energy
goods, and (3) finished consumer goods excluding energy. No PPI series corresponds to the
services-1 ess-energy CPI component since the
producer price index has no services components. Since this last CPI component has had
a large weight in the CPI (growing from about
one-third in 1970 to just over one-half in 1987),
the use of a multiequation model is even more
compelling if only to ferret out the services
component.
The first three CPI component series—food,
energy, and commodities-less-energy—are modeled exactly after the structure of Model 3b.
Each component model's explanatory variables
are the appropriate lagged dependent variable
and respective PPI components for DPP/,- and
SHIFT,-, where / is the appropriate component
46
(food, energy, or commodities less food and
energy). For the services-less-energy component, the same structure is used except that the
DPPIj
a n d SHIFT)
variables are b a s e d on
the
overall CPI and PPI series. Of course, the lagged
dependent variable is the lagged services-lessenergy series. For all four equations the previously discussed Almon procedure is used on
the lagged dependent variable and on SHIFT/.
As in Model 3b, DPP/, does not have Almon lags.
The regression results for these four equations
are found in Tables 4a-4d.
For the first three components, the coefficients (including the signs) and t-statistics are
similar to those for Model 3b. As expected, the
magnitude of various coefficients varies, as does
the relative importance of the right-hand variables. For example, the coefficients for DPP/,- for
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
*
the food and the commodities less food and
energy components are relatively low compared to that for the energy component. Hence
for these two components, the lagged dependent variables (as indicated by the sum of the
lag coefficients) represent the primary explanatory variable, whereas for the energy component the DPPIj variable is relatively more important. Overall, the summary statistics indicate
that these three component models are satisfactory.
For the services-less-energy component, the
coefficient sign for DPPI is negative instead of
positive as the general model relating the PP1 to
the CPI leads one to expect. However, the
lagged dependent variable provides considerable explanatory power in conjunction with the
structural shift variable. The lower R2 and R2adjusted for this component model probably
result largely from the fact that overall PPI numbers had to be used for DPP/, and SHIFT) since
no services components exist in the PPI. While
the R2 and Readjusted are still reasonably
good, improvements can be made and a different model structure may be more appropriate.6 However, to maintain consistency, the
model structure in Table 4d is retained and is
used in the overall aggregate forecast for the
CPI.
At this point, the four component forecasts
must be aggregated in order to compare forecast accuracy with the single-equation models.
Aggregation is based on the standard Bureau of
Labor Statistics method as explained by Chester V. McKenzie (1961).
The summary statistics for Model 4 are shown
in Table 5 along with the statistics for the other
models. As indicated by the absolute and root
mean squared errors, the multiequation model
forecasts more accurately than the models discussed previously. One might argue that the
composite model is only slightly better than
Model 3b. Yet, with component forecasts, judgment can be better used to determine if the
overall forecast should be lowered or raised.
Generally, outside information can more easily
be brought to bear on the components of the
price indexes than on the entire index. Forecasters might have access to information on the
persistence of a disturbance to one of the
underlying components of a series. For example, a bad agricultural harvest might have severe
FEDERAL RESERVE BANK O F ATLANTA
effects on the food component of a price index
for several quarters but might not be expected
to have any permanent effect. This knowledge
should therefore lead to an adjustment to that
component forecast, rather than to a judgmental adjustment of the entire index.
Although forecasters can use judgment with
single-equation models, such tinkering is somewhat cruder. With the single-equation models,
one cannot determine which underlying independent componentvariable may be causing an
unusual forecast.
Summary Comparison:
In-Sample and Out-of-Sample
Significant improvement in the forecast accuracy of monthly CPI models can be made
through improved model specification and disaggregation. Changes in specification lowered
the root mean squared error over the period
February 1980 through December 1987 from 0.284
for the traditional model to 0.236 for the improved first difference Model 3b. Using a fourequation model with the same specification as
Model 3b further lowered the root mean squared
error to 0.218. Thus, relatively simple changes in
model specification and use of components led
to significantly improved forecasts for percentage changes in the monthly consumer price
index. However, these numbers reflect an insample comparison. A more rigorous comparison would use out-of-sample forecasts. An
out-of-sample comparison is simply a comparison of forecast values to actual values for a
time period following the period of estimation.
An in-sample comparison is a comparison of
forecast errors in the same period as the estimation period.
To compare out-of-sample accuracy, identically specified models were re-estimated for
the period July 1974 through December 1979.
Some producer price series did not start until
1974, and lagged variable needs required that
the estimation period begin later in the year.
Forecasts were then estimated for the out-ofsample, or ex-post, period January 1980 through
December 1987. These forecast comparisons
are shown in Table 6. Overall errors are higher
simply because the forecasts are out-of-sample—
47
Table 6.
Out-of-Sample Forecast Comparison for the Regression Period
July 1974-December 1979
Summary Statistics for the Forecast Period January 1980-December 1987
Correlation
Coefficient
Squared*
Mean
Absolute
Error
Standard
Deviation
of Mean
Absolute Error
Root Mean
Squared Error
Model 1 a
Traditional with OLS
0.387242
0.276911
0.183773
0.331814
Model 1b
Traditional with
Cochrane-Orcutt
0.466024
0.223763
0.175890
0.284050
Model 2
Almon with Lagged
Dependent Variable
and First Differences
in PPI inflation
0.438596
0.215923
0.185510
0.284039
Model 3a
Almon for Model 2
with the Addition of
Structural Shift Variable
(%ACPI - %APPI)
0.483423
0.197599
0.177658
0.265102
Model 3b
Model 3a Using Almon
Except for the First
Difference Variable
0.510342
0.189737
0.174690
0.257292
Model 4
Four Equation Composite
Based on Model 3b
Specification
0.530937
0.197948
0.162639
0.255655
Model
Correlation coefficient squared between actual and forecast data. R2 and R2-adjusted are not available over the out-ofsample period.
forecast data are almost always lower when fitted to the same period for which the model is
estimated. Yet, with in-sample forecasts, accuracy typically improves as more explanatory
variables are added. This effect must be discounted, and an out-of-sample comparison is
probably the best method of doing so.
As shown in Table 6, the progression of
improvement is nearly identical with the in-
48
sample results except that Model 3b is slightly
more accurate than Model 3a, the reverse of
results shown in Table 5. The multiequation
forecast accuracy is still essentially identical to
the best single-equation model. Overall, the
improvement in forecast accuracy is quite dramatic. For example, regarding the mean absolute
error, the accuracy of Model 4 is about 30 percent greater than that of Model la.7
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Methodology Suggestions
This attempt to improve the monthly CPI model
reflects certain methodological considerations
regarding the development of monthly economic
models in general. First, the decision must be
made whether to use an accounting or behavioral
forecasting model or perhaps a combination of
the two. The primary factor determ ining the choice
is whether component data are available prior to
an indicator's release, if they are available, use of
an accounting model is more appropriate.
Future CPI models could benefit from revised
model specification and estimation technique.
Use of simple percentage change or "log-log"
models does not automatically ensure a better
model specification.1 Specification should follow
actual accounting processes in the definition of an
indicator; alternatively, in behavioral models,
closer attention ought to be paid to duplicating
the way independent and dependent variables
are related in actual practice. When considering
the estimation technique, at the minimum the
serial correlation problem should be addressed
since models with significant serial correlation can
result in missed turning points, a critical shortcoming, despite such models' accuracy on average
FEDERAL RESERVE BANK O F ATLANTA
over long time periods. Of course, estimation
techniques other than ordinary least squares or
Cochrane-Orcutt should be considered.
The decision to use a multiequation model
involves several considerations, some complex,
others straightforward. One issue concerns whether the dependent variable can be separated into
major components according to differing behavioral factors. A second consideration is whether
independent variables are available for corresponding dependent variable components, or at
least a significant portion of them. Relative accuracy is a third consideration. Finally, even if pure
econometric multiequation forecasts have no
greater accuracy, component analysis may provide
insight into forecasting error for judgmental
adjustment.
Note
1
A "log-log" model is one in which the dependent and
independent variables are logarithmic transformations
of specific data series. Such a model structure is often
assumed for generating various types of elasticities.
49
Notes
available, however, not all these effects can be estimated
with any degree of accuracy. One way of overcoming this
problem is to place constraints on the way that the variables being used to forecast a series are brought into the
model. The Almon lag scheme is one such set of constraints.
'The "traditional" model is generally used by money market
analysts for relatively quick forecasts. This model specification was also used by Bechter and Pickett (1973).
2
The inclusion of earlier observations generally increased
forecasting accuracy without affecting the relative accuracy
of competing models discussed in this paper.
3
Mean absolute percentage change is used instead of mean
percentage change since the former does not take into
account declines which offset some increases in the CPI
before the average is calculated.
4
This technique is used to help solve the problem of "overparameterization" that is always present in forecasting
economic time series. "Overparameterization" simply
means that reality is complicated, and so the ideal forecasting model would allow for many different effects of
various variables (observed at different time intervals) on
the variable to be forecast. Since relatively few data are
''Multicollinearity occurs when two or more variables (such
as the lagged variables) are highly—but not perfectlycorrelated with each other, making it difficultto distinguish
their individual influences on changes in the dependent
variable.
6
An alternative approach might be an ARIMA model, but
that is outside the scope of this paper.
7
This figure is the percentage improvement if the Model 4
number is the numerator. If, instead, Model I a s number is
divided by Model 4's, the improvement is roughly
40 percent.
References
Bechter, Dan M., and Margaret S. Pickett. "The Wholesale
and Consumer Price Indexes: What's the Connection?"
Federal Reserve Bank of Kansas City Monthly Review
(June 1973): 3-9.
Guthrie, Robert S. "The Relationship Between Wholesale
and Consumer Prices." Southern Economic Journal 47
(April 1981): 1046-55.
50
Judge, George G„ et al. The Theory and Practice of Econometrics. New York: John Wiley and Sons, 1980.
McKenzie, Chester V. "Technical Note: Relative Importance
of CPI Components." Monthly Labor Review 84 (November 1961): 1233-36.
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
JUST RELEASED!
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write to the Federal Reserve Bank of Atlanta, Public Information Department,
104 Marietta St., NW, Atlanta, Georgia 30303-2713.
51
Book Review
Buying into America
by Martin and Susan Tolchin
New York: Times Books, 1988.
400 pages. $19.95.
Foreign investment in the United States is
becoming more widespread and significant in
value each day. Not only financial assets 1 ike U.S.
government securities and corporate stocks
and bonds but also real assets such as office
buildings, manufacturing plants, forestland, and
beachfront properties are coming under increased foreign control. In their latest book,
Buying into America, Martin and Susan Tolchin
document and describe the growing scope of
these activities, note how defenders of the
trend justify this surge of foreign investment,
and review the long-term costs that such investments may entail for the nation.
This book has received much publicity, largely because of its wealth of information on a popular topic. Americans are uncertain about the
long-run consequences of foreign investment
and appear to be growing more apprehensive
as the pace of such activity accelerates. One of
the authors' stated purposes in writing the book
was to heighten awareness and concern over the
potentially costly impacts of foreign investment
they see for Americans.
Martin and Susan Tolchin form a politically
savvy and veteran writing team. He is a correspondent in the Washington bureau of The New
York Times, and she is a professor of public
administration in the School of Governmentand
52
Business Administration at George Washington
University. They seem to have a faculty for writing about topics that touch a public nerve and
are in vogue in the politically charged public
policy arena. Titles of their previous books—
Dismantling America: The Rush to Deregulate;
Clout: Womanpower and Politics; and To the
Victor: Political Patronage from the Clubhouse
to the White House—demonstrate this bent.
In Buying into America: How Foreign Money Is
Changing the Face of Our Nation, the husbandwife team concludes that foreign investment in
the United States definitely is not without its
costs. Tolchin and Tolch in argue that the influx of
foreign money into the U.S. economy poses a
severe threat to the ability of the United States
to control its fate and defend its position as a
premier industrial power. Opening paragraphs
in the book refer to "mayors, governors and
cabinet officers . . . circling the globe in quest of
foreign funds, with the intensity of third-world
ministers trying to stave off the financial collapse of their shaky governments." Moreover,
the authors assert, "under pressure to bring
home the bacon, politicians have paid scant
attention to the long-run economic, political,
and social effects of their country's deepening
dependence on foreign money." The Tolchins
are, of course, partly right. By adopting a xenoE C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
phobic tone, however, they divert too much attention away from important macroeconomic
issues with greater influences on American
society—for example, the low U.S. saving rate
and large government budget deficits.
The story told by the Tolchins can be summarized as follows: The United States—government and citizens alike—is spending too much,
and foreign lenders are financing the consumption spree. In the process, foreigners are acquiring U.S. financial and real assets so quickly and
in such large amounts that they have accumulated significant economic and political
clout here. Unless something is done soon to
slow or reverse our dependence on foreign
money, which has made the United States the
world's biggest debtor, Americans are in extreme danger of mortgaging their future and
reverting to colonial status. In a nutshell, they
argue, America is spending its way into the
poorhouse by borrowing for consumption rather
than using the funds to expand or develop
industrial capacity.
Worse yet, in Tolchin and Tolchin's view, the
United States seems to be giving away the proverbial farm with open-door, laissez-faire national trade and investment policies, and state
governments are needlessly providing extravagant subsidies to lure foreigners here. Meanwhile, the authors portray foreign businesses
and governments—both separately and together—as accelerating this nation's economic and
political demise by means of sinister and subversive plots. The writers note, for example, that
foreign corporations may try to buy into critical
defense-related manufacturing activities to
acquire technology; sometimes, foreign governments encourage such activities and may even
subsidize unfaircompetitive practices. National
security can, of course, be compromised because of such actions. The authors also point to a
moral issue: the openness of the U.S. financial
system has encouraged, albeit incidentally, capital flight to the United States from dictators and
criminals seeking a safe repository for their illgotten gains.
The Tolchins argue persuasively that the
significant and fast-growing net-debtor status of
the United States should generate concern
among its citizens. Reliance on foreign money to
finance a consumption binge certainly entails
long-run costs in the form of an interest and
FEDERAL RESERVE BANK O F ATLANTA
debt repayment burden that threatens to grow
even heavier over the next few years. The
authors also assert—with some validity—that
competition among states for foreign direct
investment has led to excessive subsidies in the
form of cheap land, tax holidays, job training,
grants, and other breaks. Such competition
among state and local governments can be
wasteful and cause a cleavage between national
and local interests. In instances when the investment would have been made without the
inducement, the long-run profitability or benefit to the United States is suboptimal.
While some foreign investment incentives
seem questionable, the Tolchins fail to recognize that the jobs, tax revenues, and other
benefits that result generally warrant such lures.
Moreover, foreign investment may promote
economic growth and enhance the ability of
domestic industries to compete, thus adding
jobs to the host economy. Such investment
often brings innovative management techniques
and better technology along with new plants or
equipment. Local government leaders are keenly
aware of these and other benefits; investment
may boost employment, improve the community's economic diversity, and support its
property and payroll tax base even when investment incentives are provided.
Among the Tolchins' other concerns, national
security issues are legitimate. Fortunately, safeguards have already been developed; for example, defense contracts cannot be awarded to
firms that are more than 25 percent foreignowned, and investments in a few sensitive
industries are prohibited. As a practical matter,
though, determining which industries qualify for
safeguard treatment is difficult, suggesting that
industrial restrictions should be used sparingly
lest certain industries' competitive edge be
dulled by insulation and protection.
The Tolchins' other objections to foreign
investment stem from their belief that foreigners' interest in the United States derives from a
desire to avoid protective tariffs, to acquire new
technology, to gain a foothold in the large and
affluent U.S. market, or simply to make money.
Yet these motivations are not as sinister as the
light in which this book casts them; indeed, a
prevailing view among economists holds that
world resource allocation is improved to the
extent that foreign investment is market-driven
53
and lured by the profitability criterion. Once
here, foreign investors should want the U.S.
economy to operate as profitably and efficiently
as possible. What's more, foreigners' efforts to
avoid protective tariffs, acquire new technology,
and increase market shares are common to virtually all businesses, regardless of their geographical bases, political motivations, or the
areas into which expansion is projected.
In Buying into America, the Tolchins achieve
success in publicizing what they regard as the
potential dangers of foreign investment in the
United States, thus meeting one of their major
goals. Unfortunately, they are not as successful
in reaching another major purpose—accurately
describing how foreign money is changing the
face of our nation. This objective is not met
because the Tolchins overstate the dangers of
foreign investment and give short shrift to its
benefits. This shortcoming of Buying into America
is particularly reflected in the shotgun approach
taken regarding interactions between the United
States and the rest of the world. The authors
bring up such diverse incidents as clandestine
Moscow-directed economic warfare and thirdworld-initiated money laundering along with
open and aboveboard international economic
transactions such as foreign corporations' establishment of manufacturing plants in the
United States and foreigners' portfolio purchases of U.S. stocks and bonds. Moreover,
Tolchin and Tolchin essentially lump these disparate interactions together as all being costly
to the United States.
The unbalanced treatment of the issues in
Buying into America is, perhaps, motivated by
the authors' strong desire to call attention to the
dangers of foreign investment, but they still go
too far. Imbalance is also created by confusing
consequences with causation: investment often
is a response to a problem rather than the root
of a problem. For example, if the United States
is indeed on a consumption spree, as the
Tolchins assert, the condemnation of foreigners
who are willing to finance this consumption is
neither a practical nor a charitable stance.
In assessing the merits of foreign investment,
two fundamental issues need to be addressed.
First, an analyst should determine whether
foreign investment in the United States is creating wealth or simply redistributing existing
wealth. The Tolchins vaguely address this ques54
tion with loose assertions that long-run "costs"
such as profits, interest, dividend, and principal
repatriation may outweigh short-term employment gains created by foreign investment.
However, Buying into America definitely does
not attempt to analyze the costs and benefits of
foreign investment in a methodologically sound
and empirical framework.
A typical example of the type of no-win situation that the Tolchins depict concerns foreign
investment in the real estate market. They write
that "foreign investors often contribute to soaring real estate prices,'' but in the same paragraph they state that "foreign investors conduct
distress sales that take the bottom out of U.S.
real estate markets." The authors also downplay
the impact of jobs created by foreign investments while suggesting, incorrectly, that foreigners dominate entire industries; as measured
by employment, U.S. affiliates of foreign manufacturers constituted under 8 percent of the
work force in 1986 and exceeded 10 percent in
only a few industries.
The second important issue to be considered
in evaluating foreign investment is whether our
relatively receptive and open economic environment is preferred to one with more government intervention in markets involving international activities. The Tolchins' philosophical
answer, like protectionists' on the narrower
trade issue, is clear: they recommend much
greater government intervention via information gathering and control of activities. The
Tolchins seem either not to recognize or to
ignore the fact that increased government involvement could also generate substantial
costs for our economic system in the form of
inefficiencies.
Beyond the direct costs associated with increased U.S. government surveillance and intervention, potentially large costs to the private
sector might also ensue from the adoption of
measures that restrict capital flows. Official U.S.
balance of payments statistics for 1987 show
that the flow of foreign direct investment (for
property, plant, and equipment) to the United
States was $41.5 billion during 1987, but U.S.
companies did even more direct investment
abroad ($49.3 billion). Moreover, the stock of
U.S. direct investment abroad is much larger
than what foreigners own here as a result of large
U.S. outflows during the past several decades.
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
Foreign Investment: The Tolchins' Myths, Costs, a n d R e c o m m e n d a t i o n s
Recitation of the Tolchins' nine "myths" of foreign investment is daunting, and their list of major potential economic and political costs of foreign investment is frightening. So, too, for that matter, are many of
their recommendations.
For the Tolchins, the perpetuation of the following myths is the real problem because they engender
a paralyzing resistance to changes in public policy:
• foreign capital will help America rebuild its industrial capacity;
• foreign investment is separate from trade policy;
• investment policy exists apart from foreign policy;
• foreign investments are a sign of America's economic health;
• money is neutral, not political, and investors are interested in profit, not power;
• U.S. policymakers and the American people have enough information on which to base intelligent decisions;
• no changes are needed in the current U.S. laissez-faire policy toward foreign investment;
• foreign investment and free trade are the same thing; and
• foreign investment helps American business.
The writers suggest that each of the following cost items entails a significant offset to the limited jobcreating benefits of foreign investment:
• U.S. political and economic independence is lost;
• foreign corporations eliminate U.S. industrial competition with peremptory strikes;
• states give foreign investors extravagant concessions;
• foreign corporations are antagonistic to U.S. workers' unionization rights; and
• foreign investment will eventually worsen U.S. budget and trade deficits.
The Tolchin solution to the challenge of foreign investment is a much-expanded role for the U.S.
government in monitoring and controlling international economic transactions. Government must:
• shield citizens from the negative impact of foreign investment and assert some control over its
future direction;
• require a policy of full disclosure of foreigners' investments in this nation;
• identify the benefits of foreign investment and reinforce state efforts to maximize those benefits;
• study the points at which foreign investment weakens U.S. national security and take measures to limit
those investments;
• take a hard look at foreign investors as employers and identify their shortcomings along with their
strengths;
• control foreign investment when it is inimical to American business or American interests;
• recognize the U.S. bargaining position and negotiate from strength; and
• demand a level playing field and reciprocity abroad.
The most severe shortcoming of this book,
though, is that it is anecdotal (and repetitive at
that) rather than analytical. How foreign money
is changing the face of our nation is an important
topic for research. Careful analysis, however, is
required to determine the ways in which this
change ¡staking place. Moreover, important disFEDERAL RESERVE BANK O F ATLANTA
tinctions should b e m a d e when discussing the
topic of foreign investment. Taking these distinctions into account and employing an analytical framework would p r o d u c e a much m o r e
useful, and less polemical, product.
Specifically, it is crucial from an economic
perspective to distinguish foreign portfolio
55
investment, which may be an accommodating
consequence of U.S. budget and trade deficits,
from foreign direct investment in real assets. An
appropriate discussion would reflect on the
causes and consequences of direct versus portfolio investment and include some quantitative
estimation of the macroeconomic impacts of the
different types of capital flows. Clearly, the
employment effect of a foreign auto manufacturer locating an assembly plant in this country
is much different from that of a foreign pension
fund's purchase of the federal government's
latest bond issue. Furthermore, neither of these
activities bears much resemblance to nefarious
international transactions in terms of political or
social effects.
56
The intrusion of foreign money into an economy is not a new issue. In light of this fact, an
appropriate stance on the part of the United
States and other countries receiving foreign
investment would be to realize that the world is
increasingly interdependent and that, while
interdependency has its costs, its benefits are
often much better.
William J. Kahley
The reviewer is an economist in the regional section of the
Atlanta Fed's Research Department.
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
FUNCTIONAL COST ANALYSIS
r
1987 Functional Cost Analysis Reports
Now Available
E a c h year the Federal Reserve System collects and analyzes data on the
costs of various bank activities and services from a sample of institutions across
the nation. A compilation of the 1987 results, which includes average expense
data as well as income and productivity measures for the industry and for
specific deposit-size categories, is now available to the public. For more information on the 1987 Functional Cost Analysis report, call Peggy Simons at (404)
521-8823.
To order a report, please send a letter and a $50 check, payable to the Federal
Reserve Bank of Atlanta, to:
Federal Reserve Bank of Atlanta
Research Department
104 Marietta St., NW
Atlanta, Georgia 30303-2713
ATTN: Peggy Simons
1986 Functional Cost Analysis Reports are still available at a reduced cost of $10.
FEDERAL RESERVE BANK O F ATLANTA
57
FINANCE
n
n
n
r
i
JUL
1988
$ millions
luuntiei i. id i DdHK uepUSHS
Demand
NOW
Savings
Time
Bank D e p o s i t s
JUN
1988
ANN
MAY
1988
1,010,030 i,Bua,q-ub i
380,672
372,675
358,816
172,739
173,784
170,359
524,239
522,873
513,614
794,253
787,501
780,451
JUL
1987
JUN
1987
MAY
1987
I
CHG ( * )
i . b y i . a « / 1,6/8,133 1 ,650,331
369,381
359,026
351,237
155,256
155,818
152,850
510,573
511,724
509,119
691,733
687,310
678,900
IbT
+ 7
+ 3
+11
+ 3
+15
219,005
41,635
24,005
58,503
99,751
218,949
42,668
24,251
58,560
98,453
215,993
41,032
23,722
57,959
97,547
201,
40,767
21,785
57,326
84,934
201,102
40,994
22,025
57,745
84,135
199,066
40,350
21,759
57,643
83,016
+ 9
+ 2
+10
+ 2
+17
commercial KanK u e p o s i t s
Demand
NOW
Savings
Time
4,099
2,609
4,840
11,474
22,3/2
4,245
2,587
4,895
11,228
21,914
3,999
2,523
4,813
11,057
20,405
4,041
2,146
4,606
9,978
20,352
4,102
2,148
4,623
9,885
19,954
4,025
2,102
4,579
9,700
+10
+ 1
+22
+ 5
+15
Lommerciai uanK u e p o s i t s
Demand
NOW
Savings
Time
Hb,bb8
16,163
10,734
27,266
34,264
86,648
16,506
10,946
27,413
33,581
85,312
16,081
10,728
21,137
33,055
78,403
15,824
9,860
26,933
27,327
78,387
16,009
10,015
27,061
26,900
77,652
15,825
9,917
26,908
26,523
+10
+ 2
+ 9
+ 1
+25
Commercial Bank D e p o s i t s
Demand
NOW
Savings
Time
35^986
9,013
3,419
9,370
15,993
35,884
9,194
3,407
9,328
15,689
3 2 , ' 54
8,655
3,310
9,189
15,584
32,403
8,568
3,088
8,873
13,246
32,265
8,545
3,097
8,972
13,040
31,827
8,303
3,077
8,963
12,730
+11
+ 5
+11
+ 6
+21
Commercial Bank Deposits
Demand
NOW
Savings
Time
28,089
5,008
2,389
8,090
13,070
28,145
5,123
2,403
8,033
13,069
28,08™
5,002
2,392
8,032
13,069
^7718T
4,961
2,218
7,898
12,475
27,309
4,963
2,250
7,955
12,488
27,404
4,931
2,221
7,980
12,626
+
+
+
+
+
Commercial Bank D e p o s i t s
Demand
NOW
Savings
Time
15,175
2,429
1,568
2,990
8,520
15,200
2,433
1,546
2,991
8,571
15,054
2,350
1,536
2,972
8,472
2,338
1,400
3,028
7,426
14,145
2,357
1,398
3,066
7,507
14,034
2,338
1,400
3,102
7,416
+ 4
+11
- 1
+15
Bank Deposits
30,711
5,032
3,294
5,947
16,430
30,700
5,167
3,362
5,900
16,315
30,370
4,945
3,233
5,816
16,310
28,779
5,035
3,073
5,988
14,482
28,644
5,018
3,117
6,068
14,315
28,189
4,928
3,042
6,039
14,021
Demand
NOW
Savings
Time
Demand
NOW
Savings
Time
NOTES:
'
3
1
8
2
5
+ 7
- 0
+ 7
- 1
+13
A l l d e p o s i t data are extracted from the Federal Reserve Report o f T r a n s a c t i o n Accounts, other Deposits
and Vault Cash (FR2900), and are reported for the average of the week ending the f i r s t Monday o f the
month.
Most recent d a t a , reported i n s t i t u t i o n s with over $30 m i l l i o n i n d e p o s i t s and $3.2 m i l l i o n o f
reserve requirements as o f December 1987, represents 95 percent o f d e p o s i t s in the s i x - s t a t e area. The
major d i f f e r e n c e s between t h i s report and the " c a l l r e p o r t " are s i z e , the treatment o f interbank
d e p o s i t s , and the treatment o f f l o a t .
The total d e p o s i t data generated from the Report o f T r a n s a c t i o n
Accounts eliminates interbank d e p o s i t s by r e p o r t i n g the net o f d e p o s i t s "due t o " and "due from" other
depository i n s t i t u t i o n s .
The Report o f Transaction Accounts s u b t r a c t s cash in process o f c o l l e c t i o n
from demand d e p o s i t s , w h i l e the c a l l report does not.
The Southeast data represent the total o f the
s i x s t a t e s . Subcategories were chosen on a s e l e c t i v e b a s i s and do not add to t o t a l .
P = preliminary.
* = Most recent month v s . year-ago month.
58
ECONOMIC REVIEW, SEPTEMBER/OCTOBER 1988
FINANCE
r ir ir i r
$ millions
Commercial Bank D e p o s i t s
Demand
NOW
Savings
Time
AUG
1988
JUL
1988
JUN
1988
1,817,963 I1,818,838 1,809,405
360,572
380,672
372,675
171,527
173,784
172,739
522,596
524,239
522,873
806,054
794,253
787,501
AUG
1987
JUL
1987
JUN
1987
ANN
I
CHG (*)
1,677,766 1,694,997 1¡,678,133
359,026
354,979
369,381
153,372
155,256
155,818
511,724
508,633
510,573
697,147
691,733
687,310
+ 8
+ 2
+12
+ 3
+16
Commercial Bank Deposits
Demand
NOW
Savings
Time
219,940
40,694
23,837
58,405
101,189
219,005
41,635
24,005
58,503
99,751
218,949
42,668
24,251
58,560
98,453
200,839
39,435
21,384
57,385
86,512
201,150
40,767
21,785
57,326
84,934
201,102
40,994
22,025
57,745
84,135
+10
+ 3
+11
+ 2
+17
Commercial Bank D e p o s i t s
Demand
NOW
Savings
Time
22,606
4,003
2,619
4,828
11,628
22,486
4,099
2,609
4,840
11,474
22,372
4,245
2,587
4,895
11,228
20,200
3,923
2,131
4,582
9,926
20,405
4,041
2,146
4,606
9,978
20,352
4,102
2,148
4,623
9,885
+12
+ 2
+23
+ 5
+17
Commercial Bank Deposits
Demand
NOW
Savings
Time
86,737
15,562
10,596
27,175
34,787
86,558
16,163
10,734
27,266
34,264
86,648
16,506
10,946
27,413
33,581
78,889
15,134
9,681
27,232
28,373
78,403
15,824
9,860
26,933
27,327
78,387
16,009
10,015
27,061
26,900
+10
+ 3
+ 9
- 0
+23
Commercial Bank D e p o s i t s
Demand
NOW
Savings
Time
36,614
8,879
3,407
9,344
16,563
35,986
9,013
3,419
9,370
15,993
35,884
9,194
3,407
9,328
15,689
32,157
8,481
3,021
8,790
13,328
32,403
8,568
3,088
8,873
13,246
32,265
8,545
3,097
8,972
13,040
+14
+ 5
+13
+ 6
+24
Commercial Bank D e p o s i t s
Demand
NOW
Savings
Time
28,087
4,953
2,391
8,087
13,125
28,089
5,008
2,389
8,090
13,070
28,145
5,123
2,403
8,033
13,069
26,986
4,738
2,176
7,887
12,556
27,187
4,961
2,218
7,898
12,475
27,309
4,963
2,250
7,955
12,488
+ 4
+ 5
+10
+ 3
+ 5
Commercial Bank Deposits
Demand
NOW
Savings
Time
15,168
2,320
1,560
2,972
8,578
15,175
2,429
1,568
2,990
8,520
15,200
2,433
1,546
2,991
8,571
14,014
2,279
1,400
3,013
7,553
13,973
2,338
1,400
3,028
7,426
14,145
2,357
1,398
3,066
7,507
+ 8
+ 2
+11
- 1
+14
Commercial Bank Deposits
Demand
NOW
Savings
Time
30,728
4,868
3,256
5,994
16,508
30,711
5,032
3,294
5,947
16,430
30,700
5,167
3,362
5,900
16,315
28,593
4,880
2,975
5,881
14,776
28,779
5,035
3,073
5,988
14,482
28,644
5,018
3,117
6,068
14,315
+ 7
- 0
+ 9
+ 2
+12
NOTES:
A l l d e p o s i t data are extracted from the Federal Reserve Report of T r a n s a c t i o n Accounts, other Deposits
and Vault Cash (FR2900), and are reported f o r the average o f the week ending the f i r s t Monday o f the
month.
Most recent data, reported i n s t i t u t i o n s with over $30 m i l l i o n in d e p o s i t s and $3.2 m i l l i o n o f
reserve requirements as o f December 1987, represents 95 percent o f d e p o s i t s in the s i x - s t a t e area. The
major d i f f e r e n c e s between t h i s report and the " c a l l r e p o r t " are s i z e , the treatment o f interbank
d e p o s i t s , and the treatment of f l o a t .
The total d e p o s i t data generated from the Report o f Transaction
Accounts e l i m i n a t e s interbank d e p o s i t s by r e p o r t i n g the net o f d e p o s i t s "due t o " and "due from" other
depository i n s t i t u t i o n s .
The Report o f T r a n s a c t i o n Accounts s u b t r a c t s cash i n process o f c o l l e c t i o n
from demand d e p o s i t s , while the c a l l report does n o t .
The Southeast data represent the total o f the
s i x s t a t e s . Subcategories were chosen on a s e l e c t i v e b a s i s and do not add to t o t a l . P = p r e l i m i n a r y .
* = Most recent month v s . year-ago month.
FEDERAL RESERVE BANK O F ATLANTA
59
EMPLOYMENT
MAY
1988
C i v i l i a n Labor Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
Unemployment Rate - % SA
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. E a r n . - $
C i v i l i a n Labor Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
APR
1988
MAY
1987
ANN
X
CHG
120,775
114,222
6,553
120,264
113,905
6,359
5.6
5.3
6.1
40.9
415
41.0
415
40.9
404
0
+ 3
16,542
15,536
1,006
16,307
15,363
1,025
16,327
15,227
1,131
+ 1
+ 2
-11
119,695
112,377
7,318
+ 1
+ 2
-10
Unemployment Rate - % SA
6.4
6.4
6.6
M f g . Avg. Wkly. Hours
Mfg. Avg. Wkly. E a r n . - $
41,1
369
41.2
367
41.1
361
0
+ 2
1,862
1,720
126
1,846
1,720
126
1,894
1,752
142
- 2
- 2
-11
L i v i n a n Laoor horce - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
Unemployment Rate - % SA
7.4
7.4
8.1
M f g . Avg. Wkly. Hours
Mfg. Avg. Wkly. E a r n . - $
41.0
368
.41.0
288
41.2
359
- 0
+ 3
6,104
5,816
288
6,035
5,731
304
5,879
5,581
313
+ 4
+ 4
- 8
Unemployment Rate - % SA
5.0
5.3
5.0
M f g . Avg. Wkly. Hours
Mfg. Avg. Wkly. E a r n . - $
40.9
339
40.7
335
40.8
331
+ 0
+ 2
3,144
2,953
191
3,089
2,907
182
3,089
2,937
151
+ 2
+ 1
+26
C i v i l i a n Labor Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
C i v i l i a n Labor Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
Unemployment Rate - t SA
6.2
6.2
5.7
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. E a r n . - $
41.1
356
41.3
357
41.7
352
- 1
+ 1
1,908
1,687
202
1,889
1,687
203
1,982
1,734
248
- 4
- 3
-19
Unemployment Rate - % SA
10.8
10.7
12.7
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. E a r n . - $
42.0
467
42.6
467
41.7
458
+ 1
+ 2
1,156
1,075
81
1,153
1,069
1,156
1,041
115
0
+ 3
-30
40.0
313
39.9
312
39.9
301
+ 0
+ 4
2,378
2,259
120
2,374
2,249
125
2,328
2,181
147
+ 2
+ 4
-18
Unemployment Rate - % SA
5.4
5.8
6.5
Mfg. Avg. Wkly. Hours
M f g . Avg. Wkly. E a r n . - $
41.6
369
41.8
370
41.3
364
C i v i l i a n Labor Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
C i v i l i a n Labor Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
Unemployment Rate - % SA
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. E a r n . - $
C i v i l i a n Labor Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
NOTES:
7.2
10.1
+ 1
+ 1
MAY
1988
ANN
Ï
CHG
MAY
1987
Nonfarm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . 8 Real E s t .
T r a n s . , Com. 8 Pub. U t i l .
105,969
19,445
5,290
25,235
17,696
25,358
6,651
5,561
105,744
19,370
5,083
24,383
17,658
25,231
6,627
5,510
102,268
18,926
5,012
24,248
17,303
24,170
6,539
5,358
+
+
+
+
+
+
+
+
4
3
5
4
2
5
2
1
Nonfarm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . 8 Real E s t .
T r a n s . , Com. 8 Pub. U t i l .
13,864
2,385
789
3,457
2,428
3,122
822
764
13,842
2,382
784
3,447
2,426
3,122
822
763
13,467
2,349
777
3,355
2,361
2,979
803
746
+
+
+
+
+
+
+
+
3
2
2
3
3
4
2
2
Nonfarm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . & Real E s t .
T r a n s . , Com. 8 Pub. U t i l .
1,527
374
75
337
305
282
70
73
1,520
372
73
334
304
282
70
72
1,505
367
74
332
302
274
71
73
+
+
+
+
+
+
-
1
2
1
2
1
3
1
0
Nonfarm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . 8 Real E s t .
T r a n s . , Com. 8 Pub. U t i l .
5,094
541
349
1,389
782
1,393
369
262
5,096
541
346
1,391
778
1,398
370
262
4,835
528
338
1,309
738
1,300
359
255
+
+
+
+
+
+
+
+
5
2
2
3
6
7
3
3
Nonfarm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . 8 Real E s t .
T r a n s . , Com. 8 Pub. U t i l .
2,793
570
149
693
490
550
156
177
2,788
570
149
690
489
549
156
176
2,764
571
150
691
481
535
155
174
Nonfarm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . 8 Real E s t .
T r a n s . , Com. 8 Pub. U t i l .
1,498
168
82
363
313
329
85
104
1,496
168
• 82
361
314
328
85
104
1,487
163
82
361
317
320
85
103
+ 1
+ 3
0
+ 1
- 1
+ 3
0
+ 1
Nonfarm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . 8 Real E s t .
T r a n s . , Com. 8 Pub. U t i l .
888
233
34
190
199
143
39
43
886
233
33
187
200
143
39
43
865
227
34
186
193
139
38
42
+ 3
+ 3
0
+ 2
+ 3
+ 3
+ 3
+ 2
Non farm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . 8 Real E s t .
T r a n s . , Com. 8 Pub. U t i l ,
2,064
499
100
485
340
425
102
106
2,057
498
100
481
341
422
102
106
2,011
496
98
475
330
411
96
99
All l a b o r f o r c e data are from Bureau o f Labor S t a t i s t i c s r e p o r t s s u p p l i e d by s t a t e a g e n c i e s .
s e a s o n a l l y a d j u s t e d . The S o u t h e a s t data r e p r e s e n t the t o t a l o f the s i x s t a t e s .
60
APR
1988
+
+
+
+
+
+
+
+
3
1
2
2
3
3
6
7
Only the unemployment rate data are
E C O N O M I C REVIEW, S E P T E M B E R / O C T O B E R 1988
EMPLOYMENT
JUN
1988
^ l v i T I T n u S o r F o r c e -t^ious.
Total Employed - t h o u s .
Total Unemployed - thous.
116,209
6,819
MAY
1988
114,222
6,553
JUN
1987
119,517
113,498
7,655
U
+ 2
-11
Unemployment Rate - % SA
5.3
5.6
6.1
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. Earn. - $
41.1
418
40.9
415
41.1
406
0
+ 3
iirff?^ T 6 7 ? 0 8
15,536
15,248
1,006
1,190
+ 2
-18
T T v T T i a n Labor Force - t n o u s .
Total Employed - t h o u s .
Total Unemployed - thous.
15,544
1,047
Unemployment Rate - % SA
6.1
6.4
6.6
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. Earn. - $
41.6
373
41.1
369
41.4
362
+ 0
+ 3
1,736
128
1,862
1,720
126
1,770
143
- 2
Unemployment Rate - % SA
7.0
7.4
7.8
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. Earn. - $
41.4
370
41.0
368
41.6
363
- 0
+ 2
5,104
5,816
5,883
5,570
313
+ 5
TivTTian LaborForee - T ^ o u s T "
Total Employed - thous.
Total Unemployed - t h o u s .
C i v i l i a n tabor Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
5,8
Unemployment Rate - % SA
4.6
5.0
5.0
41.0
343
40.9
339
41.1
333
~37Ï47
2,948
199
"17141
2,953
191
37101
2,924
177
Unemployment Rate - % SA
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. Earn. - $
C T V T n a n L a b o r Force - t h o u s .
Total Employed - t h o u s .
Total Unemployed - t h o u s .
-10
- 0
+ 3
+ 1 ^
+ 1
+12
6.1
6.2
5.0
41.4
357
41.1
356
42.3
358
- 2
- 0
1,925
1,699
202
17908
1,687
202
17990
1,739
281
- 2
-28
Unemployment Rate - % SA
10.2
10.8
10.9
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. Earn. - $
42.9
474
42.0
467
41.5
450
+ 3
+ 5
lTifl)
1,059
90
1,156
1,075
81
1,044
123
- Z
+ 1
-27
C m n ^LaDor^^ce^tnous.
Total Employed - t h o u s .
Total Unemployed - t h o u s .
Unemployment Rate - % SA
7.4
7.2
10.0
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. Earn. - $
40.7
319
40.0
313
40.2
304
+ 1
+ 5
2,365
2,237
127
2,378
2,259
120
2,354
2,201
153
+ 1
+ 5
-17
5.4
5.4
6.7
41.9
374
41.6
369
41.7
367
C i v i l i a n Labor Force - t h o u s .
Total Employed - thous.
Total Unemployed - t h o u s .
Unemployment Rate - % SA
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. Earn. - $
NOTES:
^onTarm^mpfo^
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . & Real E s t .
T r a n s . , Com. & Pub. U t i l .
N^farmfcrnpTo ym e n t - t p u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . & Real E s t .
T r a n s . , Com. & Pub. U t i l .
^^^numts^fo^Knz^xMusT^
Mfg. Avg. Wkly. Hours
Mfg. Avg. Wkly. Earn. - $
c T v T f i a n L a b o r Force - t h o u s .
Total Employed - thous.
Total Unemployed - thous.
ANN
ANN
%
CHG
+ 1
+ 2
HAY
1988
JUN
1987
19,642
5,495
25,529
17,363
25,593
6,729
5,615
19,445
5,290
25,235
17,696
25,358
6,651
5,561
102,910
19,091
5,176
24,518
17,051
24,341
6,616
5,398
+
+
+
+
+
+
+
3
6
4
2
5
2
4
137878^
2,393
796
3,457
2,401
3,135
827
768
iT^fSi
2,385
789
3,457
2,428
3,122
822
764
2,363
785
3,368
2,311
2,995
812
751
+
+
+
+
+
+
+
+
3
1
1
3
4
5
2
2
370
76
334
295
277
71
73
+
+
+
+
+
+
2
2
1
2
5
2
0
0
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . & Real E s t .
T r a n s . , Com. & Pub. U t i l .
378
77
339
310
283
71
73
374
75
337
305
282
70
73
Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . & Real E s t .
T r a n s . , Com. & Pub. U t i l .
5,083
540
351
1,384
771
1,395
371
262
5,094
541
349
1,389
782
1,393
369
262
Nonfarm Employment - t h o u s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . & Real E s t .
T r a n s . , Com. 8 Pub. U t i l .
T780r
570
151
695
487
556
157
177
¿7/93
570
149
693
490
550
156
177
Nonfa r m E m p T o ^ n e n t ^ T t e u s T
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . & Real E s t .
T r a n s . , Com. & Pub. U t i l .
i749ö
169
82
364
311
329
85
105
i,4§8
168
82
363
313
329
85
104
Nonra r m EmpTo5menF^tnoij s .
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . S Real E s t .
T r a n s . , Com. & Pub. U t i l .
884
235
35
191
189
145
39
43
233
34
190
199
143
39
43
Nonfarm Employment - thous.
Manufacturing
Construction
Trade
Government
Services
F i n . , I n s . & Real E s t .
T r a n s . , Com. » Pub. U t i l
502
102
481
333
428
104
107
A l l labor force data are from Bureau o f Labor S t a t i s t i c s r e p o r t s s u p p l i e d by s t a t e agencies.
s e a s o n a l l y adjusted. The Southeast data represent the total o f the s i x s t a t e s .
FEDERAL RESERVE BANK O F ATLANTA
JUN
1988
m
100
485
425
102
W P
530
342
1,309
726
1,307
363
256
27780
572
152
696
478
541
157
175
t
CHG
+
+
+
+
+
+
+
+
+
0
0
0
2
3
0
+ 1
+ 1
164 + 3
81 + 1
364 + 0
309 - 1
320 + 3
86 - 1
104 + 1
229
35
187
184
139
39
42
+ 3
0
+ 2
+ 3
+ 4
0
+ 2
498 + 1
9 9 + 2
2
319 + 3
412 + 3
97 + 6
99 + 7
Only the unemployment rate data are
61
CONSTRUCTION
tt2I
MAY
1988
APR
1988
MAY
1987
ANN
%
CHG
Nonresidential B u i l d i n g Permits
Total N o n r e s i d e n t i a l
Industrial Bldgs.
Offices
Stores
Hospitals
Schools
5 Mil.
50,437
7,143
13,045
13,400
2,335
1,081
50,200
7,232
12,985
13,363
2,229
1,092
47,289
8,250
13,840
12,095
2,449
1,192
+ 7
-13
- 6
+11
- 5
- 9
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
SOUTHEAST i
^ H M H I H
Nonresidential B u i l d i n g Permits
Total N o n r e s i d e n t i a l
Industrial Bldgs.
Offices
Stores
Hospitals
Schools
5 Mil.
7,751
809
1,869
2,426
507
261
7,780
814
1,911
2,417
495
264
7,787
1,058
1,868
2,412
416
146
- 0
- 24
+ 0
+ 1
+ 22
+ 79
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
Nonresidential B u i l d i n g Permits - $ M i l .
Total N o n r e s i d e n t i a l
493
Industrial Bldgs.
23
Offices
158
Stores
175
Hospitals
16
Schools
24
511
22
161
189
16
22
+
+
14
66
7
5
23
14
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
12-month cumulative rate
Nonresidential B u i l d i n g Permits
Total Nonresidential
Industrial Bldgs.
Offices
Stores
Hospitals
Schools
67
169
MAY
1988
Mil.
3,723
361
815
1,062
193
95
3,702
369
821
1,057
182
96
3,752
399
841
1,154
288
34
- 1
- 10
- 3
- 8
- 33
+179
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
Nonresidential B u i l d i n g Permits - $ M i l .
Total Nonresidential
1,905
Industrial Bldgs.
244
Offices
545
Stores
588
Hospitals
123
Schools
101
1,950
245
580
578
123
103
1,787
310
446
548
20
42
+ 7
- 21
+ 22
+ 7
+515
+140
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
N o n r e s i d e n t i a l B u i l d i n g Permits - $ M i l .
Total Nonresidential
358
Industrial Bldgs.
15
Offices
61
Stores
157
Hospitals
106
Schools
9
368
16
62
161
106
12
445
37
91
135
34
36
- 20
- 58
- 33
+ 16
+212
- 75
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
N o n r e s i d e n t i a l B u i l d i n g Permits
Total Nonresidential
Industrial Bldgs.
Offices
Stores
Hospitals
Schools
$ Mil.
'19
29
52
61
17
13
224
28
56
63
16
12
233
22
55
81
24
8
+
+
6
32
5
25
29
63
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
N o n r e s i d e n t i a l B u i l d i n g Permits - $ M i l .
Total N o n r e s i d e n t i a l
1,054
Industrial Bldgs.
136
Offices
239
Stores
384
Hospitals
53
Schools
19
1,024
133
231
368
52
19
1,001
223
267
308
37
6
+ 5
- 39
- 10
+ 25
+ 43
+217
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
NOTES:
Data s u p p l i e d
Nonresidential
six states.
APR
1988
MAY
1987
ANN
%
CHG
93,196
96,230
- 2
1007.2
462.0
1069.4
589.2
- 7
-22
140,102
143,530
- 2
15,651
15,557
15,765
- 1
201.9
102.8
202.4
100.9
205.4
120.6
- ?
-15
23,374
23,336
23,513
- 1
995.6
606
601
667
- 9
9.9
3.4
10.0
3.3
11.0
6.1
-10
-44
1,099
1,112
1,237
-11
8,968
8,944
8,772
+ 2
113.8
71.4
115.4
70.9
108.3
78.3
+ 5
- 9
12,691
12,647
12,523
+ 1
3,665
3,610
3,613
+ 1
45.5
18.8
46.8
18.3
49.5
19.8
- 8
- 5
5,570
5,560
5,400
+ 3
389
396
361
+ 8
6.1
0.5
6.4
0.5
7.3
1.7
-16
-71
747
764
884
-15
285
286
320
-11
4.7
1.4
4.8
1.1
5.3
1.7
-11
-18
504
510
553
- 9
.,738
1,719
1,915
- 9
21.9
7.2
22.0
6.8
23.6
13.0
- 7
-45
1,763
2,743
2,916
- 5
by the U.S. Bureau of the Census, Housing U n i t s Authorized By B u i l d i n g Permits and P u b l i c C o n t r a c t s , C-40.
data exclude the c o s t o f c o n s t r u c t i o n for p u b l i c l y owned b u i l d i n g s . The Southeast data represent the total o f the
62
ECONOMIC REVIEW, SEPTEMBER/OCTOBER 1988
CONSTRUCTION
JUN
1988
ANN
%
CHG
HAY
1988
JUN
1987
Nonresidential B u i l d i n g Permits - 5 Mi 1. .
Total Nonresidential
50,612
Industrial Bldgs.
7,323
Offices
12,773
Stores
13,679
Hospitals
2,315
Schools
1,079
50,437
7,143
13,045
13,400
2,335
1,081
47,747
8,127
14,071
12,230
2,531
1,204
6
10
- 9
+ 12
8
10
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
Nonresidential B u i l d i n g Permits - ? Mi 1.
Total N o n r e s i d e n t i a l
7,743
777
Industrial Bldgs.
Offices
1,891
Stores
2,469
484
Hospitals
Schools
237
7,751
809
1,869
2,426
507
261
7,836
1,050
1,833
2,428
436
. 174
-
1
- 26
+ 3
+
2
+ 11
+ 36
Residential Bull " ng T e r m i t s
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
Nonresidential B u i l d i n g Permits - ! ™iT.
Total Nonresidential
508
Industrial Bldgs.
22
Offices
175
177
Stores
Hospitals
14
Schools
18
493
23
158
175
16
24
566
59
166
185
14
26
3,689
333
816
1,062
173
97
3,723
361
815
1,062
193
95
NonresidehtTaTBuiTciing p e r S n t s - ^m .
Total Nonresidential
1,916
Industrial Bldgs.
252
•Offices
551
Stores
613
124
Hospitals
Schools
83
12-month cumulative rate
Nonresidential BuTT3irig "Permits Total Nonresidential
Industrial Bldgs.
Offices
Stores
Hospitals
Schools
+
-
10
63
5
4
0
- 31
R e s i d e n t i a l B u i l d i n g Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
3,776
426
814
1,167
302
36
- 2
- 22
+ 0
- 9
- 43
+169
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
1,905
244
545
588
123
101
1,792
276
440
548
20
60
+ 7
9
+ 25
+ 12
+520
+ 38
Residentìà ITJù11 cfl ng Permits
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
+
-
Total Nonresidential
Industrial Bldgs.
Offices
Stores
Hospital s
Schools
354
22
60
146
105
9
358
15
61
157
106
9
453
37
92
143
34
35
- 22
- 40
- 35
+ 2
+209
- 74
esidential m n l d i n g
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
Total Nonresidential
Industrial Bldgs.
Offices
Stores
Hospitals
Schools
219
23
54
64
19
13
219
29
52
61
17
13
237
31
55
75
24
9
+
8
26
2
15
21
44
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
Total Nonresidential
Industrial Bldgs.
Offices
Stores
Hospital s
Schools
1,057
124
235
407
50
17
1,054
136
239
384
53
19
1,012
221
267
311
42
8
+ 4
- 44
- 12
+ 31
+ 19
+113
ResTdentTaT
Value - $ M i l .
R e s i d e n t i a l Permits - Thous.
Single-family units
Multifamily units
Total B u i l d i n g Permits
Value - $ M i l .
NOTES:
Data supplied
Nonresidential
six states.
ANN
%
CHG
JUN
1988
HAY
1988
JUN
1987
94,377
93,892
96,733
- 2
992.1
462.5
995.6
460.6
1068.8
572.1
- 7
-19
141,697
141,036
144,481
- 2
15,692
15,651
15,882
- 1
200.5
106.0
201.9
102.8
207.1
115.0
- 3
- 8
23,406
23,374
23,567
- 1
588
606
685
-14
9.9
2.8
9.9
3.4
10.9
6.5
- 9
-57
1,097
1,099
1,252
-12
9,008
8,968
8,934
+ 1
112.7
74.9
113.8
71.4
111.1
73.4
+ 1
+ 2
12,698
12,691
12,710
- 0
3,682
3,665
3,555
+ 4
45.3
18.6
45.5
18.8
48.9
19.8
- 7
- 6
5,599
5,570
5,346
+ 5
385
389
480
-20
5.9
0.9
6.1
0.5
7.4
1.7
-20
-47
739
747
894
-17
293
285
320
- 8
4.7
1.8
4.7
1.4
5.3
1.5
-11
+20
512
504
558
- 8
1,735
1,738
1,907
- 9
22.1
7.1
21.9
7.2
23.4
12.1
- 6
-41
2,762
2,763
2,807
- 2
by the U . S . Bureau o f the Census, Housing U n i t s Authorized By B u i l d i n g Permits and Public C o n t r a c t s , C-40.
data exclude the cost o f c o n s t r u c t i o n for p u b l i c l y owned b u i l d i n g s . The Southeast data represent the total of the
FEDERAL RESERVE BANK O F ATLANTA
63
GENERAL
LATEST
DATA
Personal Income
{$ b i l . - SAAR)
Plane P a s s . A r r . ( t h o u s . )
Petroleum Prod, ( t h o u s . )
Consumer P r i c e Index
1967=100
Kilowatt Hours - m i l s .
SOUTHEAST
Personal Income
($ b i l . - SAAR)
Plane P a s s . A r r . ( t h o u s . )
Petroleum Prod, ( t h o u s . )
Consumer Price Index
1967=100
JCilowatt Hours - m i l s .
Personal Income
($ b i l . - SAAR)
Plane P a s s . A r r . ( t h o u s . )
Petroleum Prod, ( t h o u s . )
Consumer P r i c e Index
1967=100
Kilowatt. Hours - m i l s .
PREV
PERIOD
Ql
3,884.3
3,855.2
3,652.5
+ 6
MAY
N.A.
8,444.0
N.A.
8,181.0
N.A.
8,237.0
+ 3
JUNE
HAY
353.5
352.0
192.0
340.1
+ 4
+ 1
Ql
480.0
475.1
447.2
+ 7
MAY
MAY
5,920.4
1,310.0
6,083.3
1,319.0
6,059.1
1,426.0
- 2
- 8
MAY
N.A.
31.3
N.A.
30.7
N.A.
31.4
- 0
Ql
50.2
49.8
47.1
+ 7
175.0
57.0
158.9
56.0
182.0
56.0
- 4
+ 2
N.A.
N.A.
N.A.
4.5
- 2
177.4
+ 9
MAY
MAY
Personal Income
($ b i l . - SAAR)
Plane Pass. A r r . ( t h o u s . )
Petroleum Prod, ( t h o u s . )
Consumer Price Index
1977=100
MIAMI
Kilowatt Hours - m i l s .
Personal Income
($ b i l . - SAAR)
ANN
CURR
PERIOD
193.0
190.9
YEAR
AGO
MAY
MAY
t
+ 3
- 9
+ 4
+ 3
Ql
91.6
91.1
85.3
+ 7
- 7
Plane P a s s . A r r . ( t h o u s . )
Petroleum Prod, ( t h o u s . )
Consumer P r i c e Index
1967=100
Kilowatt Hours - m i l s .
MAY
2,137.8
N.A.
2,095.9
N.A.
2,301.8
N.A.
MAY
5.4
N.A.
5.0
N.A.
5.6
- 4
Personal Income
($ b i l . - SAAR)
Ql
52.5
51.9
50.4
+ 4
MAY
MAY
343.6
1,157.0
340.9
1,166.0
346.7
1,268.0
- 1
- 9
MAY
N.A.
4.5
Ql
27.8
27.3
26.6
+ 5
MAY
MAY
42.0
75.0
39.1
75.0
48.1
79.0
+13
- 5
N.A.
N.A.
2.0
N.A.
2.1
0
Plane P a s s . A r r . ( t h o u s . )
Petroleum Prod, ( t h o u s . )
Consumer P r i c e Index
1967=100
Kilowatt Hours - m i l s ,
Personal Income
($ b i l . - SAAR)
Plane P a s s . A r r . ( t h o u s . )
Petroleum Prod, ( t h o u s . )
Consumer Price Index
1967=100
Kilowatt Hours - m i l s .
Personal Income
($ b i l . - SAAR)
Plane P a s s . A r r . ( t h o u s . )
Petroleum Prod, ( t h o u s . )
Consumer P r i c e Index
1967=100
Kilowatt Hours - m i l s .
NOTES:
MAY
Ql
MAY
MAY
2.1
JULY
1988
CHG
N.A.
4,7
64.9
64.1
60.4
+ 7
363.1
N.A.
334.8
N.A.
392.9
N.A.
8
N.A.
5.3
N.A.
5.5
N.A.
5.2
2
JUNE
1988
JULY
1987
ANN
Ï
CHG
Agriculture
P r i c e s R e c ' d by Farmers
Index (1977=100)
142
B r o i l e r Placements ( t h o u s . )
92,563
C a l f P r i c e s ($ per cwt.)
85.00
B r o i l e r P r i c e s (t per l b . )
42.10
Soybean P r i c e s ($ per bu.)
8.87
B r o i l e r Feed Cost ($ per ton) (Q3)248
137
94,804
86.70
36.70
8.56
(Q2)181
129
90,647
80.70
8.10
5.20
(Q3)193
+10
+ 2
+ 5
+50
+71
+28
Agriculture
P r i c e s R e c ' d by Farmers
Index (1977=100)
136
B r o i l e r Placements ( t h o u s . )
39,638
C a l f P r i c e s ($ per cwt.)
85.28
B r o i l e r P r i c e s ( i per l b . )
42.15
Soybean P r i c e s ($ per bu.)
9.06
_ B r o j l e r Feed Cost ($ per ton) (03)226
126
40,539
82.89
34.86
8.62
(Q2)163
94
37,388
80.63
26.21
5.34
(Q3)18!
+61
+70
+25
14,681
74.40
35.00
8.85
724
13,024
79.00
25.00
5.29
+31
+ 9
+ 5
+60
+79
+ 1/
2,426
96.70
35.10
8.85
158
2,830
2,430
84.20
26.60
5.29
185
+10
- 1
+14
+60
+79
+17
+ 5
Agriculture
Farm Cash Receipts - $ m i l .
Dates: JAN., MAY
946
B r o i l e r Placements ( t h o u s . )
14,177
C a l f P r i c e s ($ per cwt.)
82.70
B r o i l e r P r i c e s (£ per l b . )
40.00
Soybean P r i c e s ($ per bu.)
9.45
. B r o i l e r Feed Cost ($ per ton)
Agricul ture
Farm Cash Receipts - $ m i l .
Dates: JAN., MAY
B r o i l e r Placements ( t h o u s . )
C a l f P r i c e s ($ per cwt.)
B r o i l e r P r i c e s (t per l b . )
Soybean P r i c e s ($ per bu.)
,Bro.i 1 er Feed Cost ($ per_tonl.
3,113
2,409
95.80
42.40
9.45
216
Agriculture
Farm Cash Receipts - $ m i l .
Dates: JAN., MAY
1,034
B r o i l e r Placements ( t h o u s . )
15,780
C a l f P r i c e s ($ per cwt.)
76.90
B r o i l e r P r i c e s ( i per l b . )
42.50
Soybean P r i c e s ($ per bu.)
9.02
B r o i l e r Feed Cost
15,939
75.90
34.00
7.64
'.58
986
14,951
76.70
25.50
5.15
185
Agriculture
Farm Cash Receipts - $ mil
Dates: JAN., MAY
B r o i l e r Placements ( t h o u s . )
C a l f P r i c e s ($ per cwt.)
B r o i l e r P r i c e s ( i per l b . )
Soybean P r i c e s ($ per bu.]
Feed. Cos t ( 5
:1er Fi
N.A.
86.00
N.A.
'8.59
185
362
N.A.
86.00
N.A.
5.46
165
+ 6
692
7,272
82.90
44.70
8.84
266
7,493
84.50
36.20
8.87
185
6,982
80.20
29.30
5.37
165
+ 4
+ 3
+53
+65
+61
741
N.A.
82.50
N.A.
8.96
261
N.A.
80.50
N.A.
9.27
197
665
N.A.
79.30
N.A.
5.38
208
+11
Agriculture
Farm Cash Receipts - $ m i l .
Dates: JAN., MAY
B r o i l e r Placements ( t h o u s . )
C a l f P r i c e s ($ per cwt.)
B r o i l e r P r i c e s ( i per l b . )
Soybean P r i c e s ($ per bu.)
Der
ton
wMäi~ 1 e r F e e d i l * 4
'
Agriculture
Farm Cash Receipts - $ m i l .
Dates: JAN., MAY
B r o i l e r Placements ( t h o u s . )
C a l f P r i c e s ($ per cwt.)
B r o i l e r P r i c e s ( i per l b . )
Soybean P r i c e s ($ per bu.)
B r o i l e r Feed Cost ($ per ton)
470
N.A.
91.00
N.A.
+67
+75
+17
+30
+63
+61
+ 4
+67
+25
Personal Income data s u p p l i e d by U . S . Department o f Commerce. Taxable Sales are reported as a 12-month cumulative t o t a l .
Plane
Passenger A r r i v a l s are c o l l e c t e d from 26 a i r p o r t s .
Petroleum Production data s u p p l i e d by U.S. Bureau of Mines.
Consumer P r i c e
Index data supplied by Bureau o f Labor S t a t i s t i c s .
A g r i c u l t u r e data supplied by U.S. Department o f A g r i c u l t u r e .
Farm Cash
Receipts data are reported as cumulative f o r the calendar year through the month shown.
B r o i l e r placements are an average
weekly r a t e .
The Southeast data represent the total o f the s i x s t a t e s .
N.A. = not a v a i l a b l e .
The annual percent change
c a l c u l a t i o n i s based on most recent data over p r i o r y e a r . R = r e v i s e d .
64
ECONOMIC REVIEW, SEPTEMBER/OCTOBER 1988
mm*
Economic
Review
Federal Reserve Bank of Atlanta
104 Marietta St, N.W.
Atlanta, Georgia 30303-2713
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