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Economic Review
Federal Reserve Bank of Dallas
March 1988

1

Effects of the Lower Dollar
on U.S. Manufacturing:
Industry and State Comparisons
W. Michael Cox and John K. Hill

Just as the strong dollar slowed the rate of growth in U.S.
manufacturing during the first half of the 1980s, the fall in
the dollar that began in early 1985 should stimulate U.S.
manufacturing during the rest of the decade. The production responses from individual manufacturing industries
are likely to vary, however, because the dollar has effectively fallen more for some industries than for others. In addition, some industries are generally more sensitive to
changes in exchange rates. Because the composition of
manufacturing varies regionally, the manufacturing sectors
of some states also are likely to prosper more than others
from dollar declines.

10

The Effect of Monetary Policy
on Long-Term Interest Rates:
Further Evidence from an
Efficient-Markets Approach
Kenneth

f. Robinson

Many analysts contend that an expansionary monetary
policy lowers interest rates. Through this so-called liquidity
effect, policy actions are thought to generate movements in
real economic variables. In this article, evidence from an
efficient-markets approach to interest rate determination
finds no support for such an effect on long-term interest
rates. In fact, an expansionary monetary policy is four:d to
be associated with an increase in long-term rates. The evidence indicates that this relationship appears to have
emerged in the decade of the 1980s.

This publication was digitized and made available by the Federal Reserve Bank of Dallas' Historical Library (FedHistory@dal.frb.org)

Economic Review
Federal Reserve Bank of Dallas
March 1988
President and Chief Executive Officer
Robert H. Boykin
First Vice President and Chief Operating Officer
William H. Wallace
Senior Vice President and Director of Research
Harvey Rosenblum
Vice President and Associate Director of Research
Gerald P. O'Driscoll, Jr.
Assistant Vice President and Assistant Director of Research
W. Michael Cox
Assistant Vice President and Senior Economist
Leroy O . Laney

Economists
National/International
Robert T. Clai r
John K. Hill
Joseph H. Haslag
Cara S. Lown
Kenneth J. Robinson
Regional/Energy
Stephen P. A. Brown
William C. Gruben
Hilary H. Smith
William T. Long 11\
Jeffery W. Gunther
Keith R. Phillips

Editorial
Virginia M. Rogers
Elizabeth R. Turpin
Graphics and Typesetting
Graphic Arts Department

The Economic Review is published by the Federal Reserve
Bank of Dallas and will be issued six times in 1988 (January,
March, May, July, September, and November). The views
exp ressed are those of the authors and do not necessarily
reflect the positions of the Federal Reserve Bank of Dallas
or the Federal Reserve System.
Subscriptions are availab le free of charge. Please send
requests for single-copy and multiple-copy subscriptions,
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Department, Federal Reserve 8ank of Dallas, Station K,
Dallas, Texas 75222 (214/651-6289).
Articles may be reprinted on the condition that the
sou rce is credited and that the Research Department is
provided with a copy of the publication containing th e
reprinted material.

Effects of the Lower Dollar
on U.S. Manufacturing:
Industry and State Comparisons
W. Michael Cox

John K. Hill

Assistant Vice President
and Assistant Director of Research

Senior Economist

Federal Reserve Bank of Dallas

Federal Reserve Bank of Dallas

Growth in U.s. manufacturing production slowed significantly during the first half of the 1980s. After rising at an
average annual rate of 2.7 percent during 1969-79, manufacturing output increased at a rate of only 1.8 percent
during 1979-85. Much of this slowdown has commonly
been attributed to changes in the foreign exchange value
of the dollar. For the period September 1980 - March 1985,
the inflation-adjusted dollar rose 37 percent against a
trade-weighted average of the currencies of U.S. trading
partners.' By making U.s . goods more expensive relative to
foreign goods, the appreciation of the dollar thus served to
reduce world demand for U.S. manufactured products.
Beginning in early 1985, however, the dollar reversed its
course. By the middle of 1987, the dollar had declined 23
percent on a broad basis, giving up nearly two-thirds of its
earlier gains. In view of the present persistence of the u.s.
trade deficit, further declines in the dollar are also possible.
Just as the strong dollar has served to slow the rate of
growth in U.S. manufacturing during the first half of the
1980s, the lower dollar should provide a sti mulus to U.s.
manufacturing during the second half of the decade. The
Economic Review-March 1988

benefits, however, likely will not be distributed evenly across
individual industries and states. Because the dollar has depreciated at different rates against the various currencies
and because industries differ in their exposure to trade with
a particular country, the dollar in effect has fallen more for
some industries than for others. Substantial differences also
exist across industries in the sensitivity of production to
changes in exchange rates. For these reasons, the fall in the
dollar likely will elicit a variety of production responses from
U.S. industries. And in view of the geographic concentration of manufacturing production, important regional
imbalances probably will be associated with the lower
dollar.
The purpose of this article is to identify those industries
and states likely to benefit the most from recent declines in
the dollar. First, the amount of dollar depreciation that has
occurred since early 1985 was calculated for each two-digit
Th e authors would like to thank Franklin D. Berger lor computational assistance and Stephen P. A. Brown lor helplul conversations during th e lormative
stages 01this resea rch.

Figure 1

Depreciation in Real Value of the Dollar,
by Country Group, March 1985-June 1987
PERCENT
60

50
40

30
20
10
01----

-10

-20

-19.9

-30L---------------------------------------------~
SOURCE: Federal Reserve Bank of D all as.

Standard Industrial Classification manufacturing industry.
Information on trade exposure and product substitutability
was then used to determine the sensitivity of industry production to changes in exchange rates. Finally, the results
were combined to form estimates- by industry and
state-of the effects of the lower dollar on U.S. manufacturing production.
The research for this study supports the following conclusions. The industries projected to benefit substantially from
the lower dollar include transportation equipment, instruments, electronic equipment, nonelectrical machinery, and
chemicals. For these industries, recent movements in the
dollar have been particularly favorable, and their production
is relatively sensitive to exchange rate changes. In contrast,
the production of lumber and wood products, pulp and
paper, textiles, and processed food is not expected to respond significantly.
The findings for this study also indicate substantial regional disparities in the projected benefits of the lower dollar. An above-average response is expected for much of the
Northeast, Upper Midwest, and West. But below-average
gains are projected for the Southern Atlantic, South Central,
and Northern Plains states.
2

Dollar depreciation by industry
The first step in gauging the response of production in a
given industry to an exchange depreciation is to measure
the size of the depreciation that has occurred for that industry. In this section, industry-specific measures are described for the amount by which the dollar has depreciated
since early 1985. This was developed by weighting
inflation-adjusted movements in the exchange rates of u.s.
trading partners by their shares of U.s . trade in the particular
industry group.
The rece nt decline in the foreign exchange value of the
dollar has not been uniform against individual countries (see
Figure 1).2 For example, the dollar has fallen by roughly
one-half against the Japanese yen and the major European
currencies. But relative to the Canadian dollar and the currencies of the Pacific Newly Industrialized Countries
(PACNIC), the dollar has experienced very little real depreciation. And relative to the currencies of many other
Western Hemisphere countries, notably Mexico, the real
dollar has, in fact, appreciated significantly.
Because industries differ in their exposure to trade with a
particular country, the disparities in dollar depreciation visa-vis countries imply a difference in the amount of depreciFederal Reserve Bank of Dallas

Table 1

DEPRECIATION IN REAL VALUE OF THE DOllAR,
BY MANUFACTURING INDUSTRY, MARCH 1985-JUNE 1987
Decomposition o f doll ar deprec iation, by country group
Indu stry

Japan

Europe

Ca nada

PACNIC W . Hem.

Other

Total

Food and kindred
products

5.94 + 14.28 + 0.64 + 0.30 - 5.20 + 1.63 = 17.58

Tobacco

4.55 + 23 .07 + .11 +

.53 - 1 .54 + 1.98 = 28.76

Textiles

7.60 + 15.27 + .47 +

.71 - 2.31 + 1.45

Apparel

1.50 + 5.98 + .12 +1 .89 -2.17 +1.01

Lumber and wood

8.24 + 4.59 + 3.09 +

.53 - 1.23 +

.37 = 15 .62

Furniture and
fixtures

2.35 + 14.58 + 1.50 +

.89 - 1 .83 +

.45 = 17.97

Paper

3.85 +10 .27 + 3.36 +

.18 - 2.01 +

.41

Printing and
publishing

5.51 + 16.30 + 2.20 +

.30 - 1.14 +

.76 = 23 .96

Chemicals

7.01 + 19.81 + .85 +

.33 - 2.71 + 1.05 = 26.35

Petroleum and
coal products

2.30 +12 .01 + .97 +

.24 -7.67 + 1 .14 =

Rubber and plastics

8.45 + 12.15 + 1.40 +

.65 -2 .39 +

.52 =20.77

.70 + 12.11 + .10 + 1.72 - 2.61 +

.27 =12 .26

Leather

= 23.16
= 8.33

= 16.08

8.98

Stone, clay, and
glass

7.65 + 18.53 + 1.33 +

.38 - 2.49 +

.29 = 25.70

Primary metals

7.38 + 15.12 + 1.91 +

.15 - 2.54 +

.93 = 22.90

Fabricated metals

8.72 +12 .06 + 1.72 +

.59 - 1.69 +

.50 = 21 .91

Nonelectrical
machinery

11.72 +18 .68 + .86 +

.38 - 1 .66 +

.68 = 30.67

Electronic equipment

17.23 + 8.20 + .51 +

.77 - 2.49 +

.56 = 24.80

Transportation
equipment

16.48 +11 .36 + 2.16 +

.12 - 1.02 +

.43 = 29 .52

Instruments

16.48 +18.53 + .59 +

.33 - 1.17 +

.62 = 35.39

Miscellaneous
manufacturing

4.92 +12 .99 + 1.10 +

.83 - 1.48 + 1.45

= 19.81

SOURCE: Federal Reserve Bank of Dall as.

ation that has effectively occurred for individual industries.
Shown in Table 1 are the calculations for industry-specific
measures of the real dollar depreciation sin ce March 1985.
The final results are presented in the last column of the table. The middle columns express the final numbers as
weighted sums of the country-specific rates of depreciation.
The weights for a particular industry reflect the shares of
1986 U.S. bilateral trade in that industry accounted for by
the various country groups. By reading down the middle
Economic Review - March 1988

columns, it is possible to determine the industry categories
most heavily exposed to trade with a particular country
group. For example, electric and electronic equipment is
the industry category most heavily exposed to trade with
Japan, while apparel is most closely tied to trade with the
PACNIC.
The results in Table 1 reveal Significant differences in the
degree to which individual U.S. industries have been favored
by the fall in the dollar. U.S. producers of instruments,
3

nonelectrical machinery, and transportation equipment
have enjoyed an effective exchange depreciation of 30-35
percent. This reflects the prominence of Japan and Europe
in U.S. trade in these products. For apparel and leather
products, on the other hand, the real dollar has fallen by less
than 15 percent because these industries are tied much
more closely to countries of the PACNIC and the Western
Hemisphere.

Responsiveness of domestic production
to changes in exchange rates
In assessing the effects of an exchange depreCiation on
production in a given industry, it is necessary to know not
only the amount of depreciation that has occurred for that
industry, but also how responsive production is to changes
in exchange rates. In the language of the economist, it is
necessary to know the exchange rate elasticity of supply. In
the present study, the development qf these elasticities was
based on a simple model of two-way trade in differentiated
products. The model was first used to express the exchange
rate elasticity of a given industry in terms of a number of
basic underlying parameters. Then numerical values obtained for some parameters were used to form an estimate
of the elasticity for each U.S . two-digit SIC manufacturing
industry.
Consider a particular industry group consisting of two related, but imperfectly substitutable, products. The production of the two goods is assumed to be internationally
specialized. One of the goods, identified by the subscript
H, is produced under competitive conditions by firms in the
home country. The other good, identified by the subscript
F, is competitively produced by firms in the rest of the
world, collectively expressed as the foreign country. Each
country consumes both goods. Equilibrium requires that
prices adjust to equate world demand with world supply in
each market.
The model described above can be represented formally
by the following two equations:

(1 )

OH(P,P"/E}

+ O"H(PE,P"} =

SH(P} and

(2)

OF(P,P"/E}

+ O"F(PE,P"} =

S"F(P"},

where
0= demand
5 = supply, and
" = a foreign variable.

Country demands are written solely in terms of relative
prices, with income and scale effects ignored. The four important relative prices are expressed as follows. The home4

country relative price of good H is denoted P. The
foreign-country relative price of good F is denoted po. With
E representing the real foreign exchange value of the home
currency, the home-country relative price of good F can be
expressed as P"IE, and the foreign -country relative price of
good H as PE. With this notation, home-country demands
can be written in terms of P and P"IE and foreign-country
demands in terms of PE and PO. Because their production is
specialized, the world supply of good H depends only on P,
and the world supply of good F only on PO.
The model presented above can be used to explain how
an autonomous depreciation of the home currency serves
to raise production of the home good. The direct effect of
a decline in E is an increase in the relative price of good F in
the home country and a decrease in the relative price of
good H in the foreign country. Consumers worldwide are
encouraged to substitute away from good F and towards
good H. This, in itself, creates an excess of demand over
supply in the market for good H. Of course, the market for
good F is also thrown out of equilibrium, with supply there
exceeding demand. For that market to clear, P" must fall.
It cannot, however, fall by the full amount of the exchange
depreciation . Thus, when account is taken of both the original decline in E and the compensating decline in po, the net
result is a higher relative price of good F in the home country and, therefore, a greater demand for the home good in
the home country. It is also the case that the own-price effect in the foreign demand for good H must outweigh the
cross-price effect, causing foreign demand for the home
good to be higher than before. Thus, after accounting for
the required adjustment in po, a decline in E is still seen to
generate an excess of demand over supply in the market for
the home good. This ensures that the home-country price
of good H will rise. And this brings forth an increase in the.
supply of the home good.
The process of adjustment to an exchange depreciation
clearly is a complicated one. By using equations 1 and 2,
however, it is possible to uncover the basic economic parameters that jOintly determine the eventual response in
home production to a change in E. After a number of simplifying assumptions were made, the following relationships
were obtained that involved movements in home production and in the exchange rate: 3
(3)

%LlSH = s(%LlP} and
(txl'/

(4) %LlP =

+ TO') + (~)
(s

(txT" - t*xT)

+I'/) + (TO' + PO')

( -%M),

where
Federal Reserve Bank of Dallas

(5)
(6)

T == tx(1 - t*M)
T*

+ tM(1

- tx) and

== t*x(1 - tM) + t*M(1 - t*x) ·

Equation 3 expresses the percentage change in home production in terms of the percentage change in the relative
price of the home good . The term e is the price elasticity
of supply. Equations 4-6 link the percentage change in the
relative price of the home good to the percentage change
in the real exchange rate. The exchange rate elasticity of
supply can be obtained by combining equations 3 and 4.
This elasticity is seen to depend on several economic parameters: (1) the price elasticity of supply; (2) the price
elasticity of demand for the product group, 1'/; (3) the
elasticity of substitution between goods within the group,
u; (4) domestic and foreign trade exposure as measured by
ratios of exports to production, tx and t*x; and (5) domestic
and foreign trade exposure as measured by ratios of imports
to group consumption, tM and t*M'
In numerically evaluating the exchange rate elasticity for
each U.S. manufacturing industry, this study has focused on
five of the key underlying parameters: the four measures
of trade exposure and the degree of substitutability between
domestic and foreign products. Values for tx and 4A were
calculated for each industry using 1986 data on U.S. exports
and imports and 1985 data on value of shipments. These
same data were also used to calculate t"x and t*M.4 The values used for u were taken from the Michigan Model of
World Production and Trade. s
For the price elasticities of supply and demand, e and 1'/,
the default value of unity was used. This assumption need
not seriously distort the relative positions of various industries as determined by their responsiveness to changes in
exchange rates. But it clearly reduces the confidence in the
absolute values obtained for the exchange rate elasticities. For this reason, the central purpose of the present
analysis has been simply to rank individual industries and
states by the changes in production expected as a result of
the lower dollar.
The numerical values of the key parameters and exchange
rate elasticities of supply are shown in Table 2. Over the
relevant range of parameter values, the exchange rate
elasticities vary directly both with the measures of trade exposure and the substitution elasticity. Thus, the industries
found to be the most sensitive to exchange rate movements
are miscellaneous manufacturing (including jewelry, toys,
and sporting equipment), leather and leather products,
transportation equipment, and apparel. These industries are
highly exposed to trade, either through exports or imports,
and their products are highly substitutable for foreign prodEconomic Review - March 1988

ucts within the same product group. Industries such as
printing and publishing, food processing, textiles, and tobacco manufacturing are considered relatively insensitive to
the exchange rate movements, primarily because of low
trade exposure.
Response of U.S. manufacturing
to the lower dollar

In this section, the previous results are combined to provide
a complete account of the effects of the lower dollar on U.S.
manufacturing. Individual industries are ranked according
to their expected production responses. The industry figures are then averaged for each of the fifty states, using
weights that reflect the importance of each industry in a
given state's manufacturing sector. These results are used
to identify the states with manufacturing sectors that are
likely to respond the most significantly to the lower dollar.
Industry comparisons. For an estimate of the effects of
the lower dollar on production in individual manufacturing
industries, the industry-specific rates of dollar depreciation
(see Table 1) were multiplied by the exchange rate elasticities of supply (see Table 2). The results' are shown in Figure 2. As a rule, the production of durable goods is
projected to respond more significantly to recent movements in the dollar than is the production of nondurables.
The largest percentage gains are shown for transportation
equipment and instruments. Each of these industries has
enjoyed a real exchange rate depreciation of 30 percent or
more. And production in each industry is highly sensitive
to exchange rate movements. Other industries with production gains above the mean are miscellaneous manufacturing, electric and electronic equipment, nonelectrical
machinery, and chemicals.
The industries expected to respond the least significantly
to a lower dollar include lumber and wood products, pulp
and paper, textiles, and food processing. For these industries, the recent movements in the dollar have been only
moderately favorable, and their production is relatively insensitive to changes in exchange rates.
Although the data in Figure 2 should enhance a general
understanding of the future health of U.S. manufacturing, it
is of more limited value in explaining recent movements in
industrial production or in generating near-term forecasts.
For one thing, the estimates presume a complete, long-run
response of production to the lower dollar. Temporary labor shortages or constraints on plant capacity play no role
in the analysis. In addition, the estimates reveal only the
effects of recent movements in the dollar. For some industries, these effects will be dominated by other external factors. The petroleum products industry, for example, is likely
5

Table 2
CALCULATION OF EXCHANGE RATE ELASTICITIES OF SUPPLY
M easures of trade exposure (percent)
Exchange rate
elas ti city
(Tim es 10)

0.62

Indu stry

'X

'M

"X

"M

Substitution
parameter

Food and kindred
products

3.5

5.4

1.8

1 .1

1 .13

Tobacco

7.7

.6

.2

2.8

1.13

.79

1 .0

1 .15

.70

.5

4.27

3.28

1.7

1.76

1.34

Textiles

3.0

7.8

2.6

Apparel

2.0

25.3

7.8

Lumber and wood

5.4

10.0

3.3

Furniture and
fixtures
Paper

1.4

12.3

3.9

.4

3.10

1 .67

4.7

8.5

2.8

1 .5

1 .58

1 .09

1.1

1.4

.5

.4

3.01

.41

11 .0

7.5

2.5

3.8

2.61

2.16

Petroleum and
coal products

2.3

6.6

2.2

.7

2.36

1 .01

Rubber and
plastics

4.0

7.8

2.6

1 .3

3.21

1 .63

Leather
Stone, clay, and
glass

5.9

52 .8

15.1

1 .0

2.36

3.65

3.1

9.2

3.0

1 .0

2.26

1.27

Primary metals

4.3

17.9

5.7

1 .2

1.44

1.44

Fabricated metals

3.4

6.3

2.1

1.1

3.67

1 .55

Nonelectrical
machinery

15.4

17.9

5.9

5.0

1.02

1 .90

Electronic equipment

10.9

20.6

6.6

3.2

2.11

2.54

Transportation
equipment

11.7

23 .3

7.4

3.4

3.59

3.57

Instruments

14.5

17.4

5.7

4.7

1 .98

2.58

Miscellaneous
manufacturing

29 .0

41.4

13.0

8.0

1 .98

4.03

Printing and
publishing
Chemicals

SOURCE : Federal Reserve Bank of Dallas.

to be affected more by the recent plunge in oil prices than
by the lower dollar. As another example, domestic textile
manufacturers are not likely to be significantly affected by
the fall in the dollar, but they may derive substantial benefits
from a more comprehensive and better enforced system of
import quotas.
State comparisons, To calculate the effects of the lower
dollar on the general level of manufacturing production in
6

a particular state, the individual industry responses were
averaged. Used in the calculations were weights that reflect
the importance of the various industries to the state's
manufacturing sector. 6 This was carried out for each of the
fifty states, using 1982 Census data on value added by industry and state (see Figure 3). In the figure, states showing
a high response have percentage gains in manufacturing
output estimated to be at least 110 percent of the national
Federal Reserve Bank of Dallas

Figure 2

Response of u.s. Manufacturing
to the Lower Dollar
TRANSPORTATION EQUIPMENT
INSTRUMENTS
MISCELLANEOUS MANUFACTURING
ELECTRONIC EQUIPMENT
NONELECTRICAL MACHINERY
CHEMICALS
LEATHER
FABRICATED METALS
RUBBER AND PLASTICS
PRIMARY METALS
STONE, CLAY, AND GLASS
FURNITURE AND FIXTURES
APPAREL
_TQBACCO
_
LUMBER AND WOOD
_
PAPER
_TEXTILES
_
FOOD AND KINDRED PRODUCTS
_ PRINTING AND PUBLISHING
_ PETROLEUM AND COAL PRODUCTS
SOURCE : Federal Reserve Bank of Dall as.

average. States with a medium-high response show gains
of 100-110 percent of the national average. States showing
a medium-low response reflect gains of 90-100 percent of
the national average. Finally, states with a low response
show estimated gains in output below 90 percent of the
national average.
Significant regional disparities exist in the projected effects
of the lower dollar. These differences stem directly from
variations in the mix of industries in each state's manufacturing sector. States projected to benefit the most have a
large contingent of industries most favored by the fall in the
dollar. Thus, Michigan prospe rs because of the prominen ce
of transportation equipment in its manufacturing,
Massachusetts rates high because of its electronic equipment and nonelectrical machine ry industries, and California
benefits from being an importa nt producer of electronic
equipment and transportation equipment.
Conversely, states expected to benefit the least have a
heavy representation of industries least favored by recent
movements in the dollar. The Southern Atlantic states, for
example, are relatively well endowed with tobacco, textile,
and apparel manufacturing. But these industries are not
expected to benefit significantly from the drop in the dollar.
States in the Northern Plain s not only have very little manuEconomic Review - March 1988

facturing, but what they do have is largely food processing
- an industry that is relatively insensitive to exchange rate
movements.
Interpretation of the results in Figure 3 should be made
with caution. The analYSis here has distinguished states only
on the basis of the gains expected in their manufacturing
sectors. The analysis has ignored other trade-sensitive
sectors- such as agriculture and mining- that are also likely
to be affected by the decline in the dollar.7 The conclusions
reached here in examining state manufacturing need not
carryover to broader measures of state product.

Summary and conclusions
Declines in the value of the dollar, both recent and prospective, will provide a stimulus to U.S. manufacturing on
through this decade. The production responses from individual industries will not, however, be uniform. There are
two reasons for this. First, recent movements in the dollar
have enhanced the competitive position of some industries
more than others; second, industries differ in their sensitivity
to exchange rate changes. When these two factors are
co mbined- effective dollar depreciation and exchange rate
sensitivity- the effects of the lower dollar appear more sig7

Figure 3

Response of State Manufacturing
to the Lower Dollar

\

J

_

HIGH _

MEDIUM-HIGH

",'i,,":':

MEDIUM-LOW ~LOW

SOURCE: Federa l Rese rve Bank of Dallas.

nificant for durable goods manufacturing than for nondurable manufacturing.
Because the industry composition of manufacturing varies
across the fifty states, the effects of the lowe r dollar are also
likely to be uneven across geographic regions. Based on this
study, manufacturing production in much of the Northeast,
Upper Midwest, and West is prOjected to rise at a rate that
exceeds the national average. The findings also show,
however, that below-average gains in production are
projected for most of the Southern Atlantic, South Central,
and Northern Plains states.

1.

8

This is based on the RX-101 Dollar Index, which is maintained and published by the Federal Reserve Bank of Dallas (see W . Michael Cox, "A
Comprehensive New Rea l Dollar Exchange Rate Index," Econo mic Re-

view, Federal Reserve Bank of Dallas, March 1987, 1-14). Exchange rate
changes cited in subsequent sections of the paper are based upon indexes more specifically designed to reflect conditions in U.S. manufacturing.
2.

The rates of dollar dep reciation indicated for various country groups are
weighted averages of the inflation-adju sted movements in t he dollar
Vi5-~-vi5 the currencies of individual member countries. The definition
of co untry groups, as used in this study, can be found in Cox, "A Comprehensive New Real Dollar Exchange Rate Index," 11 . The weight for
the ith member of the jth group is the ratio of total u.s. bilateral trade
in manufactu red produ cts with country i during 1986 to total U.s. bilateral trade in manufactured products with all cou ntries in group i
during 1986. Inflation adju stments were made using co nsumer price
ind exes. Fo r a defense of this choice of price index, see Cox, "A Comprehensive New Real Dollar Exchange Rate Index," 3.

3. Details of the derivation are available from th e autho rs upon requ est.
The following assumptions were used to simplify the solution:

Federal Reserve Bank of Dallas

1

1

•

The specific value used fo r r was developed in the fo llowing way. In
1980, the United States accounted for 25 percent of world manufacturing output (see International Monetary Fund, International Financial Statistics: Supp lement on Output Statistics, Supplement Series no. 8
[Washington, D.C., 19841), and the U.S. trade deficit in manufactured
products was small in relation to tota l U.S. production of manufactured
goods. Thus, the United States also accounted for roughly 25 percent
of world manufacturing demand in 1980. Since that time, the u.s. sha re
of world manufactu ring output has fallen, but the U.S. trade deficit in
manufactured goods has widened considerably. It is reasonable, then,
to assume that the United States co ntinu es to account for about onefourth of the world's demand for manufactured products. The implied
va lue for r is 3.

Goods Hand F are equally substitutable for goods outside the industry group . The sum of the own-price and cross-price
elasticities of demand are then the same for each good and can
be interpreted as the price elasticity of group demand.

•

The utility functions of consumers and production functions of
producers are weakly separable in the product group. This implies
that the cross-price elasticities of demand for good s Hand F can
be expressed in terms of the share of group expenditures allo cated
to imports and an elasticity of substitution which holds constant
the value of a subutility or subproduct function.

•

The home and foreign countries have th e sa me price elasticity of
group demand, the same price elasticity of supply, and the same
elasticity of product substitution.

4. For any given manufacturing industry, let SH = u.s. production, X =
u.S. exports, M = u.s. imports, and r = the ratio of foreign to U.S. demand. Then the measures of foreign trade exposure can be written as
t'x = M/[rSH

+ (1 + r)(M -

t'M = X/[rSH

+ r(M -

5.

See Clinton R. Shiells, Robert M. Stern, and Alan V. Deardorff, "Estimates
of the Elasticities of Substitution between Imports and Home Goods for
the United States: Weltwirtschaltliches Archiv (Review 01 World Economics), Band 122, Heft 3 (1986): 515.

6.

This, ~f course, abstracts from the likely possibility that new investment
will not be distributed in the same way as the initial distribution of

X)] and

manufacturing output.
X)].

In computing t' x and t'M' industry-specific data were used for SH' X, and
M, as noted above. Because of data limitations, however, the term r was

For an analysis of the relationship between the va lue of th e dollar and
the pri ce of oil, see Stephen P. A. Ilrown and Keith R. Phillips, "Exchange
Rates and World Oil Pri ces," Economic Review, Federa l Reserve Bank of

assumed to be the same fo r each manufacturing industry.

Dallas, March 1986, 1-10.

Economic Review - March 1988

7.

9

The Effect of Monetary Policy
on Long-Term Interest Rates:
Further Evidence 'rom am
Efficient-Markets Approacb
Kenneth

J.

Robinson

Economist

Federal Reserve Bank of Dallas

The degree to which monetary policy affects interest rates
has important macroeconomic implications. In the transmission mechanism of many large-scale econometric
models, a key feature is the so-called liquidity effect- the
te mporary decline in both short-term and long-term interest
rates brought about by an expansionary monetary policy.1
In addition, the belief that the central bank can affect interest rates through its policy actions may result in widespread
requests to guide monetary policy toward the aim of influencing interest rates.
Early work on the effect of monetary policy on interest
rates centered on reduced-form estimates of the impact of
past money growth on interest rates. A significant liquidity
effect was discovered, with the response time varying from
four to nine months up to two years .2 Subsequent research,
using the same reduced-form approach and covering
mostly the period of the 1970s and early 1980s, finds either
a rapidly vanishing liquidity effect or no significant movement at all in short-term interest rates in response to mon10

etary stimulus. Market participants' increasing sensitivity to
the inflationary consequences of an expansionary monetary
policy is cited as a prime factor behind the absence of a
liquidity effect during this period. 3
The view of a liquidity effect as a key element in the
transmission mechanism of monetary policy has been subject to criticism on both theoretical and empirical grounds.
The theoretical critique basically acknowledges the possibility of a liquidity effect but also hypothesizes that monetary expansion could ultimately lead to higher interest
rates. 4 The rise in interest rates comes about through the
expansionary effect that increases in the money supply exert on both income and prices. An acceleration in both of
these variables would tend to increase money demand,
leading to upward pressures on interest rates. Further, inTh e autho r would like to thank W. Michael Cox and Cara S. Lown for th e use
o f th eir data on th e holding-period rate of return fo r governm ent bonds.
He wo uld also like to th ank Jeffery W. Gunther for helpful co mments and
discussions.

Federal Reserve Bank of Dallas

creases in the money supply could influence inflationary
expectations and ultimately lead to higher interest rates.
On empirical grounds, criticism of previous research on the
search for a liquidity effect focuses on the reduced-form
approach itself. Regressing changes in interest rates on distributed lags of past money growth is an ad hoc procedure
devoid of any theoretical structure. Interpretation of the
parameter estimates from such a procedure is often difficult
because of the lack of a formal framework in which to analyze the results.
In an effort to remedy these shortcomings, additional
research has made use of a rational expectations-efficient
markets framework in which to analyze the effects of monetary growth on interest rates. With such an approach,
monetary policy is found to have no significant influence
on either short-term or long-term interest rates in a period
covering up to the mid-1970s. 5 Moreover, in the estimation
procedure, it can be shown that failure to assume that expectations are formed rationally can give rise to misleading
results.6
This article makes use of a model incorporating market
efficiency to update the impact of monetary policy on
long-term interest rates, using monthly data over the
1959-86 period? The empirical results indicate the absence
of a liquidity effect over the entire sample period. In fact,
contrary to previous results, an expansionary monetary
policy is found to be associated with a rise in long-term interest rates . Estimates for various subperiods reveal that in
the decade of the 1980s, in particular, this relationship appears to have emerged. These results cast doubt on the
ability of an expansionary monetary policy to lower longterm interest rates significantly. Repeated demands on
the central bank for an easy monetary policy, with the aim
of keeping interest rates low, should be viewed with
skepticism.
The analysis here proceeds as follows. The model is described in the next section. The third section contains an
explanation of the estimation technique, and the fourth
section offers an interpretation of the empirical results. The
last section sets forth the conclusions.
The rational expectations-efficient markets model
The theory of rational expectations, or what is known in finance as the efficient-markets theory, states that prices of
financial assets should reflect all available information.
More formally, in a rational expectations framework the
market's subjective probability distribution of any variable
is identical to the objective probability distribution of that
variable, conditional on all available information.
Economic Review - March 1988

Market efficiency is especially appealing because it implies
that no unexploited profit opportunities exist in securities
markets. That is, at the current price, market participants
cannot expect to earn a higher than normal rate of return
by investing in any particular security. In essence, market
efficiency implies an arbitrage condition. When arbitrageurs
who are willing to speculate perceive unexploited profit opportunities, they buy or sell securities to the point where the
efficient-markets condition holds .
In order to give this efficient-markets condition empirical
content, a model of the equilibrium return for a security
must be specified. In the model used in this study, the
market is assumed to equate expected one-period holding
returns across securities. The risk or liquidity premium in
this model is assumed to be constant over time.o For longterm bonds, then, the equilibrium holding-period rate of
return (HPRR[) from period t - 1 to t includes interest
payments plus capital gains and is given as

(1)

HPRR t = Em(l-lPRRt I cP t- 1)

= rt - 1 + (j,

where

HPRR t = the equilibrium, or expected, holding-period
rate of return;
Em = expectation assessed by the market;
HPRR t = the actual holding-period rate of return;
cPt - l = information set available at time t - 1;
rt- 1 = the one-period (short-term) interest rate at
time t - 1, which equals the yield to maturity
or the expected one-period, short-term
return; and
(j = (constant) liquidity or risk premium.
Market efficiency, then, implies that

(2) Em(HPRR t - HPRR t I cPt - 1) = Em(HPRRt - rt - 1 - (j I cPt- 1)
=0.
According to equation 2, no unexploited profit opportunities exist. Market participants cannot expect to earn a
higher than normal rate of return by investing in a long-term
bond. 9 From this efficient-markets or arbitrage condition,
it follows that unanticipated changes in interest rates
(HPRR[ - rH ) should be uncorrelated with any past available information. In an efficient-markets framework, it is
only when new information hits the market that ex post
rates of return would differ from ex ante rates. More
formally, it might be hypothesized that

(3)

(HPRR t - rt - 1) = (j

+ f3(Xt - xt) ,

where X[ is a vector of variables relevant to the pricing of
long-term bonds and X~ is the market's anticipations of
these variables.
11

In the search for a liquidity effect, one choice to include
in the X, vector is money growth. Following the liquidity
preference approach to money demand, other factors might
also be important in affecting the price of long-term bonds,
and thus their rate of return, including income growth and
inflation.'o Incorporating these three variables into equation
3 yields the following:
(4.1)

(HPRR t - rt- 1) = /30

+ /31 (MGt -

e
MG t )

+ 8t

and
(4.2)

(HPRR t - rt- 1)

= /30 + /31 (MGt -

+ /32(YG t + /33(JNFt -

MGt)

YGt)
fNFt)

+ 6t ,

where MG is money growth; YG is the growth rate of national income; fNF is the rate of inflation; MGe, YGe, and
fNFe are the market's anticipations of MG, YG, and fNF,
respectively; and 6, is a white-noise error term.
If a liquidity effect is present, then the coefficient on unanticipated money growth should be positive. That is, if an
unexpected increase in the money supply lowers long-term
interest rates, the holding-period rate of return increases.
From a liquidity preference view, the coefficients on unanticipated income growth and unanticipated inflation should
be negative. Unanticipated increases in income growth and
inflation are hypothesized to raise interest rates and, thus,
lower holding-period rates of return.
One note of caution is in order regarding the estimation
procedure used in this study. The efficient-markets approach does not guarantee that the independent variables
of equations 4.1 and 4.2 are exogenous. In this framework,
causation is hypothesized to run only from unanticipated
variables affecting interest rate movements. The efficientmarkets theory does not, however, rule out the possibility
that interest rate movements may affect money growth.
The fact that simultaneity problems in an efficient-markets
model cannot be ruled out could result in inconsistent
parameter estimates."
Estimation procedure

Data for HPR~-in this study, the holding-period rate of return on long-term U.S. Government bonds- were obtained
from the series on the market value of government debt
that has been calculated by Cox and Lown.12 This measure
includes both interest payments and capital gains or losses.
The variable
is the three-month U.S. Treasury bill rate.
Expectations of money growth, growth in income, and inflation are assumed to be rational forecasts obtained from
linear forecast equations. Economic theory may not be a

r,_,

12

Table 1
VARIABLES SIGNIFICANT
IN FORECAST EQUATIONS USED
IN FORMING EXPECTATIONS
Equation,
variabl e

F statistic

M1 C equation
M1C
M2C
fPC
fNF
TB fLL
MDEF

17.39

4.63

14.86

0.10

12.07

2.230*
2.790*
2.970*

fNF equation
fNF
TBILL
MDEF

0.03
2.430*
16.070*
5.280*
2.170*

fPC equation
M2C
fPC
fNF

L-B

17.31

5.320*
1.934**
2.017* *
2.190*
5.410*
2.230*

M2C equation
M1C
M2C
TBfLL
MDEF

B-G

0.77

37.960*
2.000**
2.110*

• Significa nt at the 1-perc ent level.
• • Significant at the 5-perc ent level.
NOTE : B-G = Breusc h·Godfrey test statistic.
L-B = Ljung-Box tes t statistic.

very useful guide in deciding exactly what information economic agents use in the formation of expectations of these
variables. Therefore, multivariate forecast equations are
derived, making use of the Granger concept of predictive
content.'3 Monthly data for the 1959-86 period are employed for the following variab les:
M1G = growth rate (first difference in logs) of M1,
M2G = growth rate (first difference in logs) of M2,
fPG = growth rate (first difference in logs) of
industrial production- as a proxy for growth
in national income, and
fNF = growth rate (first difference in logs) of the
consumer price index.
Both the M1 and M2 monetary aggregates are used in an
effort to determine how sensitive the results are to different
measures of the money supply.
Federal Reserve Bank of Dallas

Table 2

ESTIMATED EFFECTS OF UNANTICIPATED VARIABLE CHANGES
ON LONG-TERM INTEREST RATES, 1959-1986
Coe ffi cients of
Con sta nt

(M1C - M1C e )

.0534**
(.0238)

-18.4125 **
(8.1484)

.5332*
(.0224)

-15.8687
(8.2158)

(M 2C - M2C e )

(lPC - IPC " )

- 7.9383 *
(2.8162)

.5340**
(.0231)

-44.2181 *
(11 .5678)

.0533*
(.0219)

-40.7341 *
(11 .5936)

-6.9362 *
(2.7285)

(lNF - INFe)

-20.7271
(11 .9177)

-21 .6227
(11.6020)

B·C

.03

.03

.05

.03

.06

.03

.08

.06

• Significa nt a t the 1· pe rce nt leve l.
.. Significant a t the 5· pe rcent level.
NOTE : B·C = Bre usc h·C odfrey tes t sta tisti c.
Figures in pare ntheses a re sta nd a rd e rrors.

In deriving the forecast equations, each of the four variables just listed was regressed on its past values, plus lagged
values of the other three variables, plus lags of each of the
following variables:

URA TE = unemployment rate,
TBILL = three-month Treasury bill rate,
FEDG = growth rate of real Federal Government
expenditures, and
MDEF = growth rate of Federal Government
interest-bearing debt in the hands of
the public.
Both FEDG and MDEF were interpolated to monthly data by
using the Chow-Lin procedure. 14 These additional variables
are included because they are often found to be important
determinants of Federal Reserve behavior and, thus, would
be primary candidates for inclusion in the information set
used by economic agents.15
In order to reduce the residuals of the forecast equations
to white noise, it was necessary to use 12 lags of each variable. In each forecast equation, a variable was retained only
if its 12 lags were jointly significant. The variables that were
found to be statistically significant appear in Table 1. The
respective F values reported correspond to the value of the
F statistic used to test the hypothesis that, jointly, the coefficients on the 12 lags of a particular variable are insignificantly different from zero. Since the Durbin-Watson
statistic is invalid because of the presence of lagged dependent variables, the Breusch and Godfrey (B-G) test staEconomic Review - March 1988

tistic is reported to detect the presence of serial correlation.
Further, the Ljung-Box (L-B) statistic, which tests the hypothesis that the residuals are white noise, is included.
Both the B-G and L-B statistics indicate that the forecast
equations contain white-noise residuals.16
In addition to requiring white-noise residuals, the forecast
equations should also be stable. Chow tests were conducted on each equation for instability, both at the midpoint of the time series and in October 1979. The latter
point was chosen because the change in Federal Reserve
operating procedures undertaken at that time might have
altered the manner in which agents formed expectations.17
These Chow tests indicated no structural change at the
midpoint of the data but did indicate that a change occurred in 1979 for the M1G, M2G, and INF equations. To
account for this instability, therefore, the forecast equations
for the three variables were estimated separately for the
periods before and after October 1979.
The parameters of equations 4.1 and 4.2 were estimated
with the two-step procedure that entails using the residuals
from the forecast equations as independent variables.18 The
procedure results in consistent parameter estimates but
implicitly assumes no uncertainty in the estimates of the
coefficients of the forecast equations. The implication is
that any potential measurement error in the independent
variables of equations 4.1 and 4.2 would be ignored, possibly resulting in incorrect estimates of the standard errors.
To resolve this problem, the method developed by Murphy
13

Table 3
ESTIMATED EFFECTS OF UNANTICIPATED VARIABLE CHANGES
ON LONG-TERM INTEREST RATES IN SELECTED PERIODS
Coeffi cients of
Constant

(M1C - M1C e )

(M2C- M2C e )

(/PC - IPC e )

(I N F - IN Fe )

R2

B-G

.080

0.484

.040

0.236

.002

0.637

.030

0.366

.004

0.012

.020

0.024

.004

0.002

.020

0.012

.110

1.130

.200

0.066

.130

0.456

.230

0.224

1959- 69
- .0022
(.0102)

4.2541
(4.4716)

- .0029
(.0102)

4.5760
(4.5951)

- 1.2540
(1 .5230)

-.0020
(.0102)

4.8568
(8.6353)

-.0028
(.0103)

5.8250
(8.7300)

- 1 .0761
(1 .5170)

- 12.7423
(8 .3297)

- 12.8697
(7.4702)

1970- 79
.0164
(.0176)

-4 .6886
(6.5322)

.0160
(.0177)

-4.9625
(6.5655)

-3 .2296
(2. 3339)

.0167
(.0176)

-7.4715
(10.5466)

.0163
(.0177)

-5 .5130
(10.6783)

- 2.9925
(2 .3467)

- 1 .1787
(10.1598)

-1 .5841
(10 .1189)

1980- 86
.1855 *
(.0672)

-104.5332 *
(32 .8280)

.1703 *
(.0647)

-77 .1513 *
(32 .0610)

-25.2810 *
(11 .9236)

.1855 *
(.0665)

-105 .2110 *
(30.3457)

.1715 *
(.0635)

-90.4101 *
(30.5373)

- 23 .3364*
(10 .1185)

-51 .0911
(41 .1511)

- 58.4017
(36.1082)

• Signifi ca nt at the 1-perce nt level.
NOTE : B-C = Breusch-Codf rey test statisti c.
Figures in parentheses are standard errors.

and Topel was used to obtain the asymptotically correct
covariance matrix. 19

Empirical results
Estimates of the parameters of equations 4_1 and 42 are
found in Tables 2 and 3. Table 2 shows parameter estimates
for the models, corrected for serial correlation, over the entire sample period, 1959-86. The results indicate that no
liquidity effect is present regardless of which monetary ag14

gregate is used . In fact, the coefficients on unanticipated
money growth are Significantly negative in all but one of the
models estimated in Table 2. Unexpected money growth
has a significant posi'tive correlation with long-term interest
rates. The broader monetary aggregate appears to have a
stronger influence on interest rate movements than M1
does, perhaps because the range of financial assets included
in M2 is more extensive. Unanticipated industrial production growth has its hypothesized sign, while unanti cFederal Reserve Bank of Dallas

ipated inflation is not a significant factor affecting interest
rates. The low values of the coefficients of determination
are not surprising in light of the fact that the dependent
variable, in effect, is a forecast error.
In an effort to determine whether the response of interest
rates to changes in monetary policy has varied over the
years, equations 4.1 and 4.2 were estimated for various periods. The results of estimating the model for the decades
of the 1960s and 1970s and for 1980-86 are found in Table
3. For the relatively tranquil 1960s, the model performs
poorly; none of the variables are significant. In the 1970s the
coefficient on unanticipated money growth becomes negative but is not statistically significant. In both the 1960s and
the 1970s, the coefficients on unanti cipated industrial production growth and unanticipated inflation have their hypothesized signs but are insignificantly different from zero.
It is in the 1980s that unanti cipated money growth has a
significant effect on interest rates, but one in the opposite
direction from that implied by the liquidity effect. Unexpected increases in the growth rate of money result in
increases in long-term interest rates.
One possible explanation for this response pattern of interest rates is that following the inflation-ridden decade of
the 1970s, market participants became more responsive to
monetary policy. This is in line with Mishkin's point about
the possible short-run effects of an expansionary monetary
policy on nominal interest rates. "More importantly for
short-run effects on interest rates, increases in the money
stock could also influence anticipations of inflation. Higher
expected inflation resulting from money stock increases
would, through a Fisherian ... relation, increase nominal
interest rates.,,20
The results from the analysis here indicate that the belief
that an easy monetary policy is capable of lowering longterm interest rates should be viewed with skepticism. Repeated calls for the central bank to "ease up," with the aim
of encouraging a fall in interest rates, are likely to be met
with disappointment. Moreover, in structural macro models, the traditional mechani~m that emphasizes the effects
of monetary policy on long-term interest rates and the cost
of capital can be questioned. Changes in the cost of capital
are hypothesized to alter the spending plans of both businesses and consumers. The evidence provided in this study
indicates that these hypothesized real effects from monetary expansion might not be forthcoming.

Conclusion
A key feature in the transmission mechanism of many
large-scale econometric models is the decline in long-term
interest rates associated with an expansionary monetary
Economic Review - March 1988

policy. The existence of this so-called liquidity effect has
been challenged recently. Earlier reduced-form work found
a significant liquidity effect, but subsequent research finds
a rapidly vanishing liquidity effect or no significant interest
rate response at all.
Using a rational expectations-efficient markets framework,
the analysis here has extended previous research and discovered an effect on long-term interest rates opposite to
that associated with a liquidity effect. In particular, it appears to be the case that market participants, after being
battered by the mac roeco nomi c turbulence of the 1970s,
have become more responsive to the possibly adverse
effects an expansionary monetary policy might exert on
the economy.

1.

See Frederic S. Mishkin, "Efficient-Markets Theory: Implications for
Monetary Policy," Brookings Papers on Economic Activity, 1978, no. 3:
707-52, for a discussion of the transmission mechanism.

2. See Phillip Cagan and Arthur Gandolfi, "The Lag in Monetary Policy as
Implied by the Time Pattern of Monetary Effects on In terest Rates,"
American Economic Review 59 (May 1969, Papers and 'Proceedings,

1968): 277-84; William E. Gibson, "Interest Rates and Monetary Policy,"
Journal 01 Political Economy 78 (May/Jun e 1970): 431-55; and Phillip

Cagan, The Channels 01 Mon etary Effects on Interest Rates (New York:
National Bureau of Economic Research, 1972).

3. See Michael Melvin, "The Vanishing Liquidity Effect of Money on Interest: AnalysiS and Implications for Policy," Economic Inquiry 21 (April
1983): 188-202; and William Reichenstein, "The Impact of Money on
Short-Term Interest Rates," Economic Inquiry 25 Oanuary 1987): 67-82.
4.

Milton Friedman, "The Role of Monetary Policy," American Economic
Review 58 (March 1968): 1-17.

5. Frederic S. Mishkin, "Monetary Policy and Long-Term Interest Rates: An
Efficient Markets Approach," Journal 01 Monetary Economics 7 Oanuary
1981): 29-55; and Frederic S. Mishkin, A Rational Expectations Approach
to Macroeconomelrics: Testing Policy Ineffectiveness and Efficient Markets Models (Chicago: University of Chicago Press, 1983).
6.

See Mishkin, "Efficient-Markets Theory," for a discussion of the consequences resulting from a failure to incorporate market efficiency.

7.

For an analysis of the effects on short-term interest rates in an
efficient-ma rkets model, see Reichenstein, "The Impact of Money on
Short-Term Interest Rates," and Kenneth J. Robinson and Eugenie D.
Short, "Estimati ng the Impact of Monetary Policy on Short-Term Interest
Rates in a Rational Expectations-Efficient Markets Model: Further
Evidence," Federal Reserve Bank of Dallas Research Paper no. 8801
(Dallas, February 1988).

8.

This approach was also used in Eugene F. Fama and G. William Schwert,
"Asset Returns and Inflation," Journal 01 Financial Economics 5 (November 1977): 115-46, and in Mishkin, "Monetary Policy and Long-Term
Interest Rates."

9. This efficient-markets model is consistent with the expectations hypothesis of the term structure. This hypothesis states that the long-term

15

interest rate at time I, R~, is an average of expected future short-term
rates plus a liquidity premium:

If expectations of future short-term rates are formed rationally, the expectations hypothesis of the term structure yields the same implications
as equation 2. See Franco Modigliani and Robert j. Shiller, "Inflation,
Rational Expectations and the Term Structure of Interest Rates,"
Economica 40 (February 1973): 12-43.

10. See David E. W. Laidler, The D emand lor Money: Th eories, Evidence, and
Problems, 3d ed. (New York: Harper & Row, 1985).

15. See james Barth, Robin Sickles, and Philip Wiest, "Assessing the Impact
of Varying Economic Conditions on Federal Reserve Behavior," Journal
01 Macroeconomics 4 (Winter 1982): 47-70, and the references cited
therein for a review of the reaction function literature.
16. T. S. Breusch, "Testing for Autocorrelation in Dynami c Linear Models,"
Australian Economic Papers 17 (December 1978): 334-55; L. G. Godfrey,
"Testing for Higher Order Serial Correlation in Regression Equations
When the Regressors Include Lagged Dependent Variables," Econom etrica 46 (November 1978): 1303-10; and G. M . Ljung and G. E. P.

Box, "O n a M easure of Lack of Fit in Time Series Mod els," Biometrika 65,
no. 2 (1978): 297-303. The B-G statistic is used because it can also test
for higher-order serial correlation. While not discussed here, tests conducted for second-, third-, and fourth-order autocorrelation indicate an

11. The money supply process may be endogenous, as pointed out in
Robert G. King and Charles I. Plosser, "Money, Credit, and Prices in a Real
Business Cycle," American Economic Review 74 Uune 1984): 363-80.
Current unanticipated money growth is, by construction, uncorrelated
with previously unexpected changes in interest rates. The assumption

absence of higher-ord er autocorrelated error terms. The Ljung-Box statistic reported is a modification of the Box-Pierce "Q" statistic, which
tests for white noise. This variation on the Q statistic is used because
Ljung and Box find, in a series of Monte Carlo experiments, that it more
closely approximates the chi-square distribution in small samples.

here is that unanticipated movements in interest rates this period do
not affect current unanticipated money growth. No evidence is presented regarding the exogeneity of the independent variables. If
endogeneity is a problem, the coefficient estimates can then be viewed
as providing information about the correlation of unanticipated movements in the right-hand-side variables and movements in interest rates.
12. W . Michael Cox and Cara S. Lown, "The Capital Gains and Losses on U.S.
Government Debt: 1942-1986," Federal Reserve Bank of Dallas Research
Paper no. 8705 (Dallas, july 1987).
13. C. W. j . Granger, "Investigating Causal Relations by Econometric MCJdels
and Cross-spectral Methods," Econometrica 37 Uuly 1969): 424-31\
14. Gregory C. Chow and An-Ioh Lin, "Best Linear Unbiased Interpolation,
Distribution, and Extrapolation of Time Series by Related Series," Review
01 Economics and Slatistics 53 (November 1971): 372-75.

16

17. For a description of this change in Fed eral Reserve operating procedures, see Marvin Goodfriend and William Whelpley, "Federal Funds:
Instrument of Federal Reserve Policy," Federal Reserve Bank of Richmond Economic Review, September/October 1986, 3-11. Use of the
Chow test assumes both that the hypothesized break point is known
and that the change in regimes is abrupt. Further, it is also necessary to
assume that the error variances are the same across regimes.
18. The two-step procedure was used initially in Robert J. Barro, "Unanticipated Money Growth and Unemployment in the United States,"
American Economic Review 67 (March 1977): 101 -15.
19. Kevin M . Murphy and Robert H. Topel, "Estimation and Inference in
Two-Step Econometric Models," Journal 01 Business & Economic Slatistics
3 (October 1985): 370-79.
20. Mishkin, "Monetary Policy and Long-Term Interest Rates," 30.

Federal Reserve Bank of Dallas