Exchange rate fluctuations and disaggregated economic activity in the US: Theory and evidence

Abstract

We examine the extent to which exchange rate fluctuations affect the US sectoral output and price. The evidence indicates that the expansionary and contractionary effects cancel out in determining industrial real output growth in the face of exchange rate fluctuations. More importantly, there is evidence of a reduction in price inflation in several industries, which is statistically significant in Finance, in response to dollar appreciation. This evidence is consistent with the reduction in aggregate demand through the decline in net exports and the increase in aggregate supply through the reduction in the cost of imported intermediate goods.

Full text

Journal of International Money and Finance
21 (2002) 1–31 www.elsevier.com/locate/econbase
Exchange rate fluctuations and disaggregated
economic activity in the US: theory and
evidence
Magda Kandil
a, b,*
, Aghdas Mirzaie
c
a
International Monetary Fund, Washington, DC 20431, USA
b
Department of Economics, University of Wisconsin-Milwaukee, USA
c
Department of Economics, Ohio State University, 423 Arps Hall, 1945 North High Street, Columbus
OH 43210-1172, USA
Abstract
We examine the extent to which exchange rate fluctuations affect the US sectoral output
and price. The evidence indicates that the expansionary and contractionary effects cancel out
in determining industrial real output growth in the face of exchange rate fluctuations. More
importantly, there is evidence of a reduction in price inflation in several industries, which is
statistically significant in Finance, in response to dollar appreciation. This evidence is consist-
ent with the reduction in aggregate demand through the decline in net exports and the increase
in aggregate supply through the reduction in the cost of imported intermediate goods. 2002
Elsevier Science Ltd. All rights reserved.
Keywords: Exchange rate; External exposure; Sectoral activity
1. Introduction
Exchange rates of industrial countries, including the USA, have been highly fluc-
tuating since the agreement to establish flexible exchange rates in the early 1970s.
* Corresponding author. Tel.: +1-202-623-4696; fax: +1-202-589-4696.
E-mail address: publicaffairs@imf.org (M. Kandil).
0261-5606/02/$ - see front matter 2002 Elsevier Science Ltd. All rights reserved.
PII: S0261-5606(01)00016-X

2M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
Fig. 1 illustrates fluctuations in the nominal and real effective exchange rate of the
US$ between 1970 and 1994. The most striking aspect of fluctuations is the spike
centered at 1985. The exchange rate of the dollar appreciated sharply. This appreci-
ation was followed by a severe depreciation after the so-called Plaza Agreement in
September 1985
1
. The evidence remained robust concerning the real appreciation of
the dollar during the 1980s.
The main concern regarding exchange rate fluctuations is that it affects output and
employment levels in the economy. Depreciation may stimulate economic activity
through the initial increase in the price of foreign goods relative to home goods,
producing excess demand for home products. Subsequently, domestic output and
domestic prices increase. Appreciation may cause a contractionary effect on the econ-
omy. In addition, the dollar’s appreciation, by pushing down the dollar’s prices of
imported intermediate goods, decreases the cost of the output produced and contrib-
utes to a reduction in price inflation.
Observed volatility in the exchange rate of the dollar has stimulated a debate in
academia and policy arena over what the government response should be. It was
Fig. 1. Effective exchange rates.
1
The Plaza Agreement involved a coordinated intervention policy among the ‘group 5’(G5) coun-
tries —the USA, Japan, Germany, France, and the UK —in September 1985. Representatives of each
country met at the Plaza Hotel in New York to announce a plan to coordinate foreign exchange-market
intervention aimed at depreciating the dollar as a response to large US trade deficits. The Plaza agreement
had a strong impact on the market. The dollar fell rapidly because of both private and official sales
of dollars.

3M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
advocated that the dollar’s appreciation may be responsible for the recession and
increase in unemployment during the 1980s. Similarly, the recent appreciation of the
dollar attributed to the relatively stable financial market in the USA raises concerns
regarding the negative consequences on employment and industrial production.
Accordingly, some have argued that the government should intervene systematically
in foreign exchange markets to stabilize the dollar and protect employment and out-
put against the adverse effects created by exchange rate fluctuations.
At a disaggregate level, Campa and Goldberg (1997) discuss that changes in the
exchange rate can significantly influence the profitability and performance of US
manufacturing industries. To measure the sensitivity of domestic manufacturing
industries to the dollar fluctuation, one must first examine the channels that transmit
such shocks to production activity and, ultimately, to the economy as a whole. Cap-
turing US industrial reliance on international markets involves measuring the extent
to which manufacturers sell products to foreign markets, use foreign-made inputs,
and more directly compete with foreign manufacturers in domestic markets.
The most widely used indicator typically calculated openness to trade by dividing
(import+export) revenues of final products to domestic production revenues. This
indicator fails to consider the growing use of foreign inputs in the manufacture of
domestic goods. Campa and Goldberg (1997) use several measures to capture differ-
ent industry’s sensitivity to international shocks. The first measure is export shares.
Producers with high export shares are likely to be more sensitive to international
shocks than producers with a lower export share. This measure captures the portion
of a producer’s revenues that is generated in foreign markets. Second, import shares,
or the ratio of imports to consumption of the industry’s output captures foreign pen-
etration in a particular industry. The industry’s output and employment are also likely
to be more sensitive to international shocks when there is a high degree of foreign
penetration in domestic markets. Imported input share or imported inputs as a share
of the value of production is the third measure for the degree of openness
2
. Finally,
they present a measure of net external orientation defined as the difference between
industry export share and import share. Campa and Goldberg conclude that industries
in the United States show the most volatile patterns in net external orientation. After
remaining, on average, primarily export oriented in the 1970s, US industries experi-
enced an increased international exposure in the early to mid 1980s through their
reliance on imported inputs in production. In the late 1980s and in the 1990s, export
shares grew faster than imported shares, raising the positive net external orientation
of US industries (Table 1).
US manufacturing industries have also steadily increased their use of imported
inputs in production, on average from 4% in 1975 to ⬎8% in 1995
3
. The increase
in the imported input use across manufacturing was the greatest in the first half of
the 1980s, when the US dollar dramatically appreciated and reduced the cost of
2
Since data in imported inputs are not available, they construct this series by combining industry
import data with country input–output data that describe the expenditure on different categories of inputs
by each manufacturing industry.
3
See Table 1. For more details, see Campa and Goldberg (1997).

4M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
Table 1
Export share, import share and imported input share of US manufacturing
Industry 1975 1995
Export share Import share Imported input Export share Import share Imported input
share share
Tobacco products 6.9 0.6 1.4 14.9 0.6 2.1
Apparel and other textiles 2.0 8.5 1.3 7.4 31.4 3.2
Furniture and fixtures 1.3 3.0 3.6 5.5 14.1 5.7
Chemical and allied products 10.1 3.6 3.0 15.8 11.0 6.3
Leather and leather products 3.9 17.7 5.6 14.4 59.5 20.5
Primary metal products 5.1 9.8 5.0 11.2 17.4 10.6
Industrial machinery and 23.3 6.3 4.1 25.8 27.8 11.0
equipment
Electronic and other electric 11.1 8.5 4.5 24.2 32.5 11.6
equipment
Transportation equipment 15.8 10.4 6.4 17.8 24.3 15.7
Instruments and related 16.8 7.4 3.8 21.3 20.1 6.3
products
Total manufacturing 8.4 6.3 4.1 13.4 16.3 8.2

5M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
foreign-produced inputs relative to inputs produced domestically. By 1985, imported
inputs as a share of total costs in the US manufacturing industries had risen to about
6%. Even after the dollar depreciated in the second half of the 1980s, the presence
of imported inputs continued to increase in the USA. Overall, the imported input
share has more than doubled in many manufacturing industries over the past two
decades, creating concerns regarding the adverse effects of dollar appreciation on
employment and production in the manufacturing sector of the US economy.
Given demand and supply channels, what are the effects of the dollar appreciation
on industrial real output and price in the USA? Investigating the relationship using
aggregate data may disguise the actual effect of exchange rate fluctuations on the
economy. Given the varying degree of openness for various sectors, a decline in
manufacturing output after the appreciation of the domestic currency might be
counterbalanced by an increase in the finance or service sectors output.
We examine the extent to which exchange rate fluctuations affect the US sectoral
output and price. Section 2 introduces a theoretical model that decomposes move-
ments in the exchange rate into anticipated and unanticipated components using
rational expectations. The solution of the model demonstrates the effects of demand
and supply channels on the output and price responses to changes in the exchange
rate. Based on these solutions, we formulate an empirical model for sectoral real
output and price. The model incorporates demand and supply shifts as well as
exchange rate shifts. The estimation highlights the relative importance of exchange
rate fluctuations in determining sectoral real output and price.
Overall, the evidence indicates that the contractionary and expansionary effects
cancel out in determining industrial real output growth in the face of exchange rate
fluctuations in the USA. Further, there is evidence of a reduction in price inflation
for several industries, which is statistically significant in Finance, in response to
dollar appreciation. This evidence is consistent with the reduction in aggregate
demand through the decline in net exports and the increase in aggregate supply
through the reduction in the cost of imported intermediate goods. Hence, concerns
about the adverse effects of dollar appreciation on economic performance in the USA
are not supported by the results of disaggregated analysis of industrial data.
2. Theoretical background
In the real world, stochastic uncertainty may arise on the demand or supply sides
of the economy. Economic agents are assumed to be rational. Accordingly, rational
expectations of demand and supply shifts enter the theoretical model. Economic fluc-
tuations are then determined by unexpected demand and supply shocks impinging
on the economic system.
We build a macroeconomic model that incorporates exchange rate fluctuations
attributed to the dollar appreciation. Uncertainty enters the model in the form of
disturbances to both aggregate demand and aggregate supply. Within this framework,
the appreciation of the dollar determines aggregate demand through exports, imports,
and the demand for domestic currency, and determines aggregate supply through

6M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
the cost of imported intermediate goods. We show theoretically that the effects of
appreciating the US dollar on the American economy is expansionary via the effect
of the supply side. However, the effects of the dollar appreciation on aggregate
demand makes the final outcome inconclusive
4
.
2.1. Aggregate demand
The demand side of the economy is specified using standard IS-LM equations
with a modification for an open economy. The demand side of the economy combines
equilibrium conditions in the Goods and Money markets. In the specifications below,
all coefficients are positive and throughout the paper, lower case denotes the logar-
ithm of the corresponding level variable. The subscript tdenotes the current value
of the variable.
c
t
⫽c
0
⫹c
1
y
dt
,0⬍c
1
⬍1 (1)
y
dt
⫽y
t
⫺t
t
(2)
t
t
⫽t
0
⫹t
1
y
t
,t
1
⬎0 (3)
i
t
⫽i
0
⫺i
1
r
t
,i
1
⬎0 (4)
log(R
t
)⫽log
冉
S
t
P
t
P
∗
t
冊
⫽s
t
⫹p
t
⫺p
∗
t
(5)
x
t
⫽x
0
⫺x
1
log(R
t
), x
1
⬎0 (6)
im
t
⫽m
0
⫹m
1
y
t
⫹m
2
log(R
t
), m
1
,m
2
⬎0 (7)
y
t
⫽c
t
⫹i
t
⫹g
t
⫹x
t
⫺im
t
(8)
m
t
⫺p
t
⫽⫺l[r
t
⫹(E
t
p
t+1
⫺p
t
)] ⫹fy
t
⫹(E
t
s
t+1
⫺s
t
), l,f,q⬎0. (9)
Eqs. (1) through (8) describe equilibrium conditions in the Goods market. In Eq.
(1), real consumption expenditure, c, varies positively with real disposable income,
y
d
. In Eq. (2), disposable income is defined to be the net of real income, y, minus
taxes, t. In Eq. (3), real taxes are specified as a linear function of real income. In
Eq. (4), real investment expenditure, i, varies negatively with the real interest rate,
r. In Eq. (5), let the domestic price level be represented by Pand the foreign price
level in foreign currency by P
∗
. The spot price of the dollar is denoted by Sand
defined as the number of foreign currency units per units of domestic currency. R
4
Agenor (1991) introduces a theoretical model for a small open economy with a fixed exchange rate
and distinguishes between anticipated and unanticipated movement in the exchange rate. He derives an
aggregate output equation from a rational expectation macro model with imported intermediate goods. In
this framework, he demonstrates that devaluation can be contractionary. Devaluation has a negative impact
on aggregate supply by making the intermediate imported goods more expensive even if the net effect
on aggregate demand is expansionary. Assuming the intermediate imported good is a necessary input in
the production process, more expensive inputs decrease the demand for the product and the amount
to produce.

7M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
is the price of domestically produced goods and services relative to the prices of
foreign produced goods and services, that is, the real effective exchange rate of the
dollar. When Rincreases, the domestic currency appreciates in real terms. The value
of Rmeasures the degree of competitiveness of domestically produced goods and
services relative to those abroad
5
. In Eq. (6), real exports are related to an auton-
omous element, x
0
, which rises when the income level abroad rises, and to relative
prices. The inverse relationship between Rand x, in (6), refers to the fact that when
the domestic price is higher relative to abroad, exports will decrease. In Eq. (7), real
imports, im, are assumed to rise with the level of real income and the real effective
exchange rate of the dollar. Eq. (8) describes the equilibrium condition in the goods
market. Real government spending, g, is assumed to be exogenous. The total expen-
diture by domestic residents in real terms (y) is the sum of real consumption expendi-
ture (c), real investment (i), real government spending (g), and net exports (the real
value of exports, x, minus the real value of imports, im).
In Eq. (9), equilibrium in the money market is obtained by equating the demand
and supply of real money balances. The real money supply is equal to exogenous
nominal balances, m,deflated by price, p. The demand for real money balances is
positively related to real income and inversely related to the nominal interest rate.
The nominal interest rate is defined as the sum of the real interest rate and inflation
expectation at time t.E
t
s
t⫹1
is the expected future value of the dollar at time t.We
assume that citizens in each country must hold domestic money for transactions
purposes but they may speculate by holding foreign money
6
. An unexpected tempor-
ary appreciation of the domestic currency in period twould lead to speculation of
depreciation in period t+1 to restore the steady-state normal trend of the exchange
rate. Consequently, agents decrease the speculative demand for domestic currency,
establishing a negative relationship between the demand for real money balances
and current shocks to the exchange rate of the currency.
2.2. Aggregate supply
On the supply side, output is produced using a production function that combines
labor, capital, energy and imported intermediate goods. When the currency appreci-
ates, it is cheaper to buy intermediate goods from abroad. The price of energy is
paid in dollars. That is, the change in the exchange rate of the dollar does not affect
the cost of imported energy to the USA.
To illustrate, the level of gross domestic output, Q, is produced using a production
function that combines imported intermediate goods, N, labor, L, and the capital
stock, K. The production function is Cobb–Douglas in Nand L, assuming fixed
capital stock
7
. In addition, the production function is dependent on changes in the
5
For a similar definition see Shone (1989).
6
For a similar discussion, see Buiter (1990).
7
By fixing the capital stock, we exclude the possibility that depreciation may increase labor pro-
ductivity by stimulating capital accumulation.

8M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
energy price, Z. Accordingly, the supply-side of this economy can be summarized
in Eqs. (10) through (14) as follows:
Q
t
⫽L
d
t
N
1⫺d
t
e
⫺Z
t
(10)
Y
t
⫽Q
t
⫺1
R
t
N
t
(11)
l
d
t
⫽n
t
⫺h{w
t
⫺p
t
⫹z
t
⫺log d}, h⫽1
1⫺d⬎0 (12)
n
t
⫽l
t
⫹1
d{log(1⫺d)⫺z
t
⫹log(R
t
)} (13)
l
s
t
⫽hlog d⫹w{w
t
⫺E
t⫺1
p
t
}, w⬎0 (14)
Eq. (10) specifies the level of gross domestic output produced, assuming complemen-
tary relation between the labor input and imported intermediate goods. Equation (11)
defines domestic value added (output supplied) or the difference between gross dom-
estic output and the amount of real intermediate imports
8
.
The demand for inputs is derived by calculating the marginal product of Land N
and equating the results with the real cost of labor (the real wage) and the real price
in domestic currency of imported intermediate goods (the inverse of the real
exchange rate). Taking log transformation of the first-order conditions and rearrang-
ing produces Eqs. (12) and (13). The demand for labor varies negatively with the
real wage and positively with imported intermediate goods. Similarly, the demand
for imported intermediate goods increases with the labor input. Dollar appreciation
reduces the real price of imported intermediate goods and, hence, increases the
demand for these goods. Further, a rise in the energy price decreases the demand
for labor and imported intermediate goods.
Eq. (14) hypothesizes a positive log-linear relationship between labor supply and
expected real wage. Supply of labor increases with an increase in the nominal wage
relative to workers’expected price at time t⫺1. An increase in aggregate price rela-
tive to workers’expectations increase the demand for labor and, hence, the nominal
wage. A rise in expected real wage increases employment and, hence, the output
supplied. In addition, aggregate supply moves positively with the foreign currency
price of the dollar. Dollar appreciation decreases the cost of imported goods and
increases the output supplied. Further, the output supplied varies negatively with
changes in the energy price.
2.3. Market equilibrium
We assume that demand and supply shifts in the model are constructed of two
components: anticipated (steady-state) component and an unanticipated (random)
8
This definition follows Agenor (1991) where he introduces a model and assumes intermediate goods
are necessary for the production process and cannot be produced domestically.

9M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
component. The combination of demand and supply-side channels indicates that real
output depends on unanticipated movements in the exchange rate, the money supply,
government spending, and the energy price. In addition, supply-side channels estab-
lish that output varies with anticipated changes in the exchange rate and the
energy price
9
.
Given demand-side channels, aggregate demand increases with an unexpected
increase in government spending or the money supply, creating positive price sur-
prises and, hence, increasing output and price in the short-run. Changes in the energy
price, both anticipated and unanticipated, increase the cost of the output produced,
decreasing output and raising prices
10
.
The effects of real exchange rate fluctuations on the price level and output are
complicated by demand and supply channels as follows:
1. In the goods market, an unexpected appreciation of the dollar will make exports
more expensive and imports cheaper. As a result, the competition from foreign
markets will decrease the demand for domestic products
11
.
2. In the money market, an unexpected temporary increase in the value of the dollar,
relative to anticipated value in the future, prompts agents to hold less dollars and
decreases the interest rate. This channel moderates the negative effect of the
exchange rate shock on aggregate demand, output and price
12
.
3. On the supply side, changes in the exchange rate, both anticipated and unantici-
pated, determine the cost of importing intermediate goods. As the dollar appreci-
ates, producers are inclined to increase imports of intermediate goods, increasing
domestic output and decreasing the cost of production and, hence, the aggregate
price level
13
.
9
Details of the model’s solution are available upon request.
10
The price level may rise unexpectedly in response to energy price shocks, creating incentives to
increase the output produced. This channel moderates the reduction in output and the rise in price in
response to energy price shocks. For a detailed theoretical illustration, see Kandil and Woods (1997).
The moderating effect of the rise in price is further reinforced in our model through the rise in the real
effective exchange rate, reducing the cost of intermediate imported goods.
11
Guittian (1976) and Dornbusch (1988) have stated that devaluation increases production of tradables
through its expenditure switching effects. Devaluation modifies the direction of demand by increasing the
international competitiveness of domestic industries, thus diverting spending from foreign goods to dom-
estic goods. The success of these policies in promoting trade balance largely depends on switching demand
in proper direction and amount as well as on the capacity of the home economy to meet the additional
demand by supplying more goods. Empirical support of this proposition for Group 7 countries over the
1960–1689 period is provided in Mendoza (1992).
12
It is expected that the contractionary effect on aggregate demand stemming from the goods market
will be more dominant. The speculative effect of money demand is likely to be dominant in developing
countries where agents’incentives to hedge against potential fluctuations in the exchange rate of their
domestic currency are high. Given the strong international position of the dollar, agents in the USA are
less concerned, in the short run, with switching between dollars and other foreign currencies.
13
Hirschman (1949) argues that devaluation from an initial trade deficit reduces real national income
and may lead to a fall in aggregate demand. The possibility that devaluation will provide a fall in output
is discussed theoretically in a model by Meade (1951). Diaz-Alejandro (1963) introduced another argu-
ment for contraction following devaluation. The redistribution of income from wages to profit may lead

10 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
3. Empirical models
Our empirical investigation analyzes annual time-series data for output and price
in the USA. Given theory’s predictions for the effects of exchange rate fluctuations
on the aggregate economy, we conduct our empirical investigation in two steps. First,
we test the validity of theory’s predictions using data for aggregate output and price
in the USA. Secondly, we disaggregate the data into sectoral real output and price
according to the SIC System in the USA (see Table 2). Our objective is to analyze
variation in sectoral responses to shocks impinging on the aggregate economy,
including exchange rate shocks
14
. Having observed differences between sectors of
the economy, we will analyze sectoral indicators that may have differentiated sectoral
responses based on the degree of openness and trade regulations.
The time period covered is from 1961 to 1994. Over this span, we assume that
industrial real output growth and price inflation fluctuate in response to aggregate
domestic demand shocks, energy price shocks, and exchange rate shocks. Shocks are
randomly distributed over the time span under investigation. Detailed econometric
methodology is provided in Appendix A. A detailed description and sources of all
data are described in Appendix B
15
.
to a reduction in real income. This point was later confirmed in a general equilibrium model by Cooper
(1971). Krugman and Taylor (1978) and Barbone and Rivera-Batiz (1987) have formalized the same
view. Bruno (1979) postulates that in a typical semi-industrialized country where inputs for manufacturing
are largely imported and cannot be easily replaced by domestic production, firms’inputs cost will increase
following a devaluation or depreciation. Gylfason and Schmid (1983) provide evidence that the final
effect of devaluation depends on the magnitude in which demand and supply curves shift because of
devaluation. Hanson (1983) provides a theoretical approach on the impact of devaluation on output sug-
gesting that the outcome depends on the assumptions regarding the labor market. Solimano (1986) studies
the effect of devaluation and focuses on the relative intensity of domestic value added with respect to
imported inputs in production. Van Wijnbergen (1989) explains that devaluation may cause an adverse
effect on output while it is inflationary.
14
Sectoral data have been analyzed in investigations analyzing macro shocks in a closed economy,
see, for example, Hong and Kandil (1995), Kandil (1995a, 1999a) and Kandil and Woods (1995, 1997).
The literature is very scarce in studies that analyze sectoral responses to exchange rate shocks. Sectoral
data are only available annually and exchange rate data are available since the early 1960s, limiting the
number of observations for time-series investigation.
15
The empirical literature on the effects of exchange rate fluctuations can be summarized as follows.
Agenor (1991) uses annual data for a group of 23 countries in the 1978–1987 period. The result shows
that an anticipated devaluation of the real exchange rate has a negative effect on economic activity, while
an unanticipated depreciation has a positive impact on output. Glick and Hutchison show the contrac-
tionary effect of appreciation on output is unstable and sample specific. Revenga (1992) investigates the
impact of increased import competition on employment and wages, using data for a panel of US manufac-
turing industries over the 1977–1987 period. Karras (1993) employs the structural autoregression approach
(VAR) to investigate the importance of different kind of shocks for the macroeconomic fluctuations of
the US economy over the period 1973–1989. The result shows that supply and demand disturbances have
important role for output fluctuations in both short-run and long-run.

11M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
Table 2
Non-linear 3 SLS parameter estimates for output using government spending and monetary shocks
a
A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 RH0
Aggregate level
0.03 ⫺0.08 0.05 ⫺0.01 0.36 0.08 ⫺0.18 ⫺0.11 ⫺0.01 ⫺0.07 0.37
(0.51) (⫺0.67) (0.42) (⫺0.02) (1.01) (0.17) (⫺0.82) (⫺0.51) (⫺0.07) (⫺0.18) (0.59)
Agriculture
0.10 0.22 ⫺0.17 ⫺0.80 0.27 ⫺0.36 ⫺0.20 0.25 ⫺0.18 ⫺1.08 0.35
(0.66) (0.85) (⫺0.56) (⫺0.51) (0.32) (⫺0.33) (⫺0.38) (0.40) (⫺0.69) (⫺1.71) (0.51)
Construction
0.03 ⫺0.19 ⫺0.02 ⫺0.62 0.11 0.38 0.16 ⫺0.01 ⫺0.09 ⫺0.25 0.58
(0.31) (⫺0.88) (⫺0.09) (⫺0.70) (0.19) (0.40) (0.42) (⫺0.02) (⫺0.48) (⫺0.24) (0.52)
Finance
0.02 ⫺0.02 0.04 0.09 0.23 0.07 ⫺0.12 ⫺0.07 ⫺0.07 0.01 0.20
(0.43) (⫺0.20) (0.33) (0.19) (0.68) (0.19) (⫺0.58) (⫺0.38) (⫺0.67) (0.04) (0.40)
Manufacturing
0.02 ⫺0.16 ⫺0.02 0.06 0.20 0.21 ⫺0.05 0.06 0.03 0.05 0.39
(0.30) (⫺1.37) (⫺0.18) (0.12) (0.58) (0.48) (⫺0.25) (0.31) (0.24) (0.14) (0.76)
Durable goods
⫺0.004 ⫺0.39 0.14 0.26 0.75 0.72 ⫺0.51 0.23 0.05 0.08 0.39
(⫺0.03) (⫺1.54) (0.54) (0.22) (1.00) (0.63) (⫺1.20) (0.47) (0.20) (0.18) (0.72)
Non-durable goods
⫺0.003 ⫺0.09 ⫺0.04 0.17 0.10 0.17 ⫺0.14 0.04 0.03 0.20 0.02
(⫺0.09) (⫺1.44) (⫺0.41) (0.46) (0.41) (0.68) (⫺0.90) (0.31) (0.35) (1.20) (0.06)
Mining
0.22 ⫺0.08 0.06 ⫺1.01 0.30 ⫺1.70 0.87 0.08 ⫺0.11 ⫺0.41 ⫺0.26
(1.31) (⫺0.34) (0.17) (⫺0.59) (0.34) (⫺1.20) (1.35) (0.16) (⫺0.37) (⫺0.91) (⫺0.53)
Retail trade
⫺0.004 ⫺0.26 0.03 0.38 0.22 0.23 ⫺0.12 0.05 0.06 0.10 0.50
(⫺0.05) (⫺1.43) (0.25) (0.57) (0.54) (0.37) (⫺0.52) (0.20) (0.48) (0.18) (0.80)
(continued on next page)

12 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
Table 2 (Continued)
A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 RH0
Service
0.04 ⫺0.03 0.06 0.25 0.26 ⫺0.47 ⫺0.13 0.02 ⫺0.01 ⫺0.8 0.87
**
(0.61) (⫺0.28) (0.65) (0.51) (0.92) (⫺1.20) (⫺0.83) (0.10) (⫺0.16) (⫺1.51) (⫺2.20)
Transportation
0.05 ⫺0.11 0.14 ⫺0.05 0.19 ⫺0.10 ⫺0.17 ⫺0.28 ⫺0.05 ⫺0.05 0.32
(0.94) (⫺1.18) (1.29) (⫺0.10) (0.64) (⫺0.25) (⫺0.79) (⫺1.27) (⫺0.49) (⫺0.23) (0.63)
Wholesale trade
0.09 ⫺0.11 0.10 ⫺0.44 0.27 ⫺0.04 ⫺0.22 ⫺0.02 ⫺0.07 0.07 ⫺0.24
(1.23) (⫺0.85) (0.49) (⫺0.50) (0.54) (⫺0.07) (⫺0.64) (⫺0.08) (⫺0.42) (0.22) (⫺0.58)
a
A0, Intercept; A1, anticipated energy price; A2, unanticipated energy price; A3, anticipated money supply; A4, unanticipated money supply; A5, anticipated
government spending; A6, unanticipated government spending; A7, anticipated real effective exchange rate; A8, unanticipated real effective exchange rate;
A9, error correction; RH0, serial correlation.
**
Significant at 10%, t-ratios are in parentheses.

13M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
3.1. The basic models
We formulate empirical models that approximate the solutions for output and price
in the theoretical model. Accordingly, the empirical model for real output is specified
as follows:
Dy
t
⫽A
0
⫹A
1
E
t⫺1
Dz
t
⫹A
2
(Dz
t
⫺E
t⫺1
Dz
t
)⫹A
3
E
t⫺1
Dm
t
⫹A
4
(Dm
t
⫺E
t⫺1
Dm
t
)⫹A
5
E
t⫺1
Dg
t
⫹A
6
(Dg
t
⫺E
t⫺1
Dg
t
)⫹A
7
E
t⫺1
Ds
t
⫹A
8
(Ds
t
(15)
⫺E
t⫺1
Ds
t
)⫹A
9
EC
t⫺1
⫹n
y
t
.
We test for the non-stationarity of industrial real output
16
. The test results are consist-
ent with non-stationary real output for all industries under investigation. Given these
results, the empirical model for real output is specified in first-difference form where
D(.) is the first-difference operator. Accordingly, all variables in the model enter in
first-difference form. Further, we test for cointegration between the non-stationary
real output and non-stationary variables that enter the model. Given evidence of
cointegration, the empirical model includes an error correction variable
17
.
Theory predicts that output varies with unanticipated demand shifts in the econ-
omy. Agents are expected to negotiate higher wages in anticipation of expansionary
demand shifts, neutralizing the effects of these shifts on output. Nonetheless, antici-
pated demand shifts may determine real output through their effects on anticipated
real effective exchange rate
18
. Consequently, anticipated demand shifts may increase
real output.
To illustrate, let z
t
be the log value of the energy price. Agents’expectation of a
variable at time tbased on information available at time t⫺1 is denoted by E
t⫺1
.
Based on theory’s forecast, output growth is expected to vary negatively with
changes in the energy price, both anticipated and unanticipated, at time t⫺1. Accord-
ingly, A
1
,A
2
⬍0.
We approximate demand shifts using two sources of domestic policies, govern-
ment spending and the money supply. The log values of government spending and
the money supply are denoted by g
t
and m
t
. Unanticipated growth in government
spending and the money supply increases aggregate demand, creating positive price
16
For details, see Kwiatkowski et al. (1992). Results are available upon request.
17
This is the lagged residual from the cointegration regression in which the non-stationary level of
output is regressed on the level of variables that enter the model: the energy price, the money supply,
government spending, and the real exchange rate. Cointegration test results are available upon request.
18
Anticipated demand increases price and the real effective exchange rate, reducing the cost of inter-
mediate imported goods. Additionally, institutional rigidity may interfere with agents’ability to adjust
fully to anticipated demand shifts. In the labor market, contracts may be longer than 1 year, preventing
wages at time tfrom adjusting fully to anticipated demand shifts at time t⫺1. Accordingly, anticipated
demand shifts are not absorbed fully in price. Alternatively, institutional rigidity may be attributed to
price rigidity in the product market. Given the cost of adjusting prices, producers may resort to adjusting
prices at specific intervals over time. Given price rigidity, anticipated demand shifts at time t⫺1may
determine real output growth in the short-run. For a discussion of the implications of sticky-wage and
sticky-price models, see Kandil (1996, 1999a).

14 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
surprises. Hence, A
4
,A
6
⬎0. Anticipated growth in government spending and the
money supply may also increase real output growth. Accordingly, A
3
,A
5
⬎0.
Finally, anticipated appreciation of the real exchange rate determines the cost of
the output supplied. Let s
t
be the log value of the exchange rate of the dollar
19
.As
producers anticipate a lower cost of imported intermediate goods, they increase the
output supplied. Accordingly, A
7
⬎0. Unanticipated change in the exchange rate is
likely, however, to determine both aggregate demand and supply. The cheaper cost
of buying intermediate imports increases the output supplied. However, demand-side
channels render the effect of exchange rate fluctuations indeterminate. Accord-
ingly, A
8
ⱖ0.
Finally, EC
t⫺1
denotes error correction. The unexplained residual of the output
Equation is denoted by n
y
t
.
To establish the robustness of our findings concerning the effects of exchange rate
fluctuations, we replace specific demand shifts (government spending and the money
supply) with a broad measure of aggregate demand (nominal GDP). This broad meas-
ure combines a variety of demand shifts stemming from the goods or money markets.
We modify the empirical model for real output as follows:
Dy
t
⫽A
0
⫹A
1
E
t⫺1
Dz
t
⫹A
2
(Dz
t
⫺E
t⫺1
Dz
t
)⫹A
3
E
t⫺1
Dn
t
⫹A
4
(Dn
t
(16)
⫺E
t⫺1
Dn
t
)⫹A
7
E
t⫺1
Ds
t
⫹A
8
(Ds
t
⫺E
t⫺1
Ds
t
)⫹A
9
EC
t⫺1
⫹n
r
t
.
Here, n
t
denotes the log value of the nominal value of GDP. Accordingly, A
3
⬎0,
and A
4
⬎0.
To demonstrate fluctuations in sectoral output price, an empirical model is speci-
fied as follows:
Dp
t
⫽B
0
⫹B
1
E
t⫺1
Dz
t
⫹B
2
(Dz
t
⫺E
t⫺1
Dz
t
)⫹B
3
E
t⫺1
Dm
t
⫹B
4
(Dm
t
⫺E
t⫺1
Dm
t
)⫹B
5
E
t⫺1
Dg
t
⫹B
6
(Dg
t
⫺E
t⫺1
Dg
t
)⫹B
7
E
t⫺1
Ds
t
⫹B
8
(Ds
t
(17)
⫺E
t⫺1
Ds
t
)⫹B
9
EC
t⫺1
⫹n
p
t
.
Testing for stationarity, sectoral output price is evident to be non-stationary for vari-
ous industries under investigation. Accordingly, the empirical model is specified in
first-difference form. Further, the results of cointegration tests support the hypothesis
that price is cointegrated with variables that enter the model. Accordingly, the error
correction term, EC
t⫺1
, enters the empirical model.
Energy price shifts, both anticipated and unanticipated, increase the cost of the
output produced and, hence, prices. Accordingly, B
1
,B
2
⬎0. Both anticipated and
unanticipated demand shifts increase price inflation. Accordingly, B
3
,B
4
,B
5
,B
6
⬎0.
Given the effect of anticipated dollar appreciation in increasing the output sup-
plied, B
7
⬍0. In contrast, an unanticipated dollar appreciation increases the output
supplied and may contract (goods market effect) or expand (money demand effect)
aggregate demand. The former two channels are deflationary while the latter
19
Empirically, we measure the exchange rate by the real effective exchange rate (see Appendix B).
This measure captures shifts attributed to the nominal exchange rate, s, and the foreign price of imports,
p
∗
, in theory.

15M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
increases price inflation. Given our expectation of the moderate speculative effect
of exchange rate fluctuations on dollar holdings, the deflationary effect may dominate
in response to dollar appreciation.
To establish the robustness of the evidence concerning the effects of exchange
rate fluctuations on price inflation, we reestimate the empirical model in (17) with
a modification that replaces specific demand shifts with a broad measure of aggregate
demand: nominal GDP. Accordingly, we estimate the following model:
Dp
t
⫽B
0
⫹B
1
E
t⫺1
Dz
t
⫹B
2
(Dz
t
⫺E
t⫺1
Dz
t
)⫹B
3
E
t⫺1
Dn
t
⫹B
4
(Dn
t
(18)
⫺E
t⫺1
Dn
t
)⫹B
7
E
t⫺1
Ds
t
⫹B
8
(Ds
t
⫺E
t⫺1
Ds
t
)⫹B
9
EC
t⫺1
⫹n
p
t
.
As indicated before, nominal GDP shifts capture broad measures of aggregate
demand. We expect B
3
,B
4
⬎0.
3.2. Testing for asymmetry
Given the complexity of the theoretical channels, it is possible that the effects
of exchange rate shocks may be asymmetric in the face of dollar appreciation and
depreciation. To test for possible asymmetry, we decompose the exchange rate shock
into positive and negative components, poss and negs
20
. Accordingly, poss and negs
capture unanticipated dollar appreciation and depreciation around an anticipated ste-
ady-state trend. The procedure followed to construct poss and negs is described in
Appendix A. Fig. 2 demonstrates a time-series plot of the distribution of poss and
negs over time.
The empirical models (15) through (18) are reestimated with a modification that
decomposes unanticipated exchange rate shocks into positive and negative compo-
nents. For example, model (15) is modified as follows:
Dy
t
⫽A
0
⫹A
1
E
t⫺1
Dz
t
⫹A
2
(Dz
t
⫺E
t⫺1
Dz
t
)⫹A
3
E
t⫺1
Dm
t
⫹A
4
(Dm
t
⫺E
t⫺1
Dm
t
)⫹A
5
E
t⫺1
Dg
t
⫹A
6
(Dg
t
⫺E
t⫺1
Dg
t
)⫹A
7
E
t⫺1
Ds
t
⫹A
8p
poss
t
(19)
⫹A
8n
negs
t
⫹A
9
EC
t⫺1
⫹n
y
t
A
8p
and A
8n
measure the effects of dollar appreciation and depreciation on real out-
put growth.
4. Empirical results
The results of estimating the empirical model for real output and price are
presented in Tables 2–5 for nine sectors of the US economy: Agriculture; Construc-
tion; Finance; Manufacturing (Durables and Non-durables); Mining; Retail Trade;
Services; Transportation; and Wholesale Trade.
20
The analysis of asymmetry has gained attention recently. For evidence in the face of monetary shocks,
see Kandil (1995b). For evidence in the face of aggregate demand shocks, see Kandil (1996, 1998, 1999b).
For evidence in the face of government spending shocks, see Kandil (2001).

16 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
Fig. 2. Positive and negative shocks on real effective exchange rate of the US$.
4.1. The output equation
The results of estimating the empirical model (15) for real output are presented
in Table 2. At the aggregate level, all parameters are statistically insignificant.
At the sectoral level, there is no evidence of a statistically significant negative
effect of energy price shifts, both anticipated and unanticipated, on real output
growth. Monetary growth shifts, both anticipated and unanticipated, appear insig-
nificant in determining sectoral real output growth. Similarly, there is no evidence
of a statistically significant effect of changes in the growth of government spending
on sectoral real output growth.
More importantly for the purpose of this investigation is to observe the effects of
changes in the real exchange rate on real output growth. All coefficients appear
statistically insignificant. As predicted by the theory, the expansionary and contrac-
tionary effects of the dollar appreciation cancel out on industrial real output growth.
The evidence is more statistically significant in the empirical model (16)
employing the effects of nominal GDP shifts on real output growth
21
. At the aggre-
gate level, anticipated energy price shifts are negative and statistically significant on
real output growth. Aggregate demand shifts, both anticipated and unanticipated,
induce non-neutral positive effects on aggregate real output growth. The contrac-
21
Sectoral output may respond significantly to aggregate demand shocks and insignificantly to both
monetary and government spending shocks. The former approximates a variety of shocks, other than
policy shocks, that include shocks to consumption, investment, velocity, capital mobility and foreign trade.

17M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
Table 3
Non-linear 3 SLS parameter estimates for output using nominal GDP shocks
a
A0 A1 A2 A3 A4 AS A6 A7 RH0
Aggregate level
⫺0.07
**
⫺0.06
*
⫺0.02 1.33
*
0.84
*
⫺0.02 ⫺0.05 ⫺59
*
0.89
*
(⫺2.10) (⫺1.96) (⫺0.89) (3.95) (7.46) (⫺0.32) (⫺1.64) (⫺2.07) (3.69)
Agriculture
0.25 0.37 ⫺0.12 ⫺3.27 0.28 1.10 ⫺0.19 ⫺0.50 0.25
(1.55) (1.17) (⫺0.44) (⫺1.52) (0.22) (1.24) (⫺0.72) (⫺0.93) (1.55)
Construction
⫺0.18 ⫺0.10 0.06 2.49
**
1.03
*
⫺0.04 ⫺0.12 ⫺0.54
**
0.91
*
(⫺1.30) (⫺0.80) (0.52) (1.90) (2.33) (⫺0.15) (⫺1.08) (⫺1.76) (4.91)
Finance
0.01 ⫺0.04 ⫺0.05 0.28 0.48
**
0.002 ⫺0.07 0.02
(0.54) (⫺0.94) (⫺0.85) (0.90) (2.14) (0.02) (⫺1.32) (0.05)
Manufacturing
⫺0.06 ⫺0.13
*
⫺0.09* 1.23
*
0.65
*
⫺0.03 ⫺0.08 ⫺0.73 0.65
(⫺1.46) (⫺2.43) (⫺1.98) (2.50) (3.79) (⫺0.28) (⫺1.29) (⫺1.46) (1.17)
Durable goods
⫺0.15
*
⫺0.34
*
⫺0.15 2.83
*
1.67
*
0.10 ⫺0.10 ⫺0.53 0.77
*
(⫺1.86) (⫺2.73) (⫺1.60) (2.90) (4.59) (0.43) (⫺0.84) (⫺1.52) (1.96)
Non-durable goods
0.02 ⫺0.11
*
⫺0.12
*
0.09 0.70
*
0.08 ⫺0.04 0.18 ⫺0.53
**
(0.88) (⫺3.73) (⫺2.73) (0.30) (4.61) (0.97) (⫺0.86) (0.93) (⫺1.82)
Mining
⫺0.02 ⫺0.06 ⫺0.12 0.56 ⫺0.30 ⫺0.11 0.27 ⫺0.79 ⫺0.17
(⫺0.23) (⫺0.33) (⫺0.41) (0.45) (⫺0.29) (⫺0.25) (1.01) (⫺1.44) (⫺0.30)
Retail Trade
⫺0.03 ⫺0.18
*
⫺0.10 0.92
*
0.92
*
0.002 ⫺0.04 ⫺0.33 0.31
(⫺0.90) (⫺2.57) (⫺1.66) (2.05) (4.00) (0.02) (⫺0.54) (⫺0.66) (0.49)
Service
0.01 ⫺0.04 0.002 0.30 0.43
*
0.01 ⫺0.03 ⫺0.94
*
0.78
*
(0.40) (⫺1.20) (0.05) (0.93) (2.91) (0.07) (⫺0.82) (⫺3.00) (2.53)
Transportation
⫺0.07 ⫺0.11 0.08 1.83
*
0.79
*
⫺0.25 ⫺0.03 0.97
*
(⫺0.28) (⫺1.74) (1.49) (2.78) (3.93) (⫺1.45) (⫺0.49) (5.80)
Wholesale trade
0.97
*
⫺0.17
*
⫺0.14 ⫺0.25 1.04
*
⫺0.10 0.08 ⫺0.53
(1.83) (⫺2.73) (⫺1.68) (⫺0.49) (3.42) (⫺0.53) (0.91) (⫺1.69)
a
RH0, Serial correlation; A0, intercept; A1, anticipated energy price; A2, unanticipated energy price; A3, anticipated nominal GDP; A4, unanticipated nominal GDP; A5, anticipated
real effective exchange rate; A6, unanticipated real effective exchange rate; A7, error correction.
*
Significant at 5%.
**
Significant at 10%, t-ratios are in parentheses.

18 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
Table 4
Non-linear 3 SLS parameter estimates for price using government spending and money supply shocks
a
B0 B1 B2 B3 B4 B5 B6 B7 B8 B9 RH0
Aggregate level
0.03 0.09
*
0.07
*
0.25
**
0.10 0.03 0.11
**
⫺0.01 ⫺0.01 ⫺0.67
*
0.90
*
(0.93) (3.05) (2.37) (1.89) (1.16) (0.25) (2.18) (⫺0.10) (⫺0.29) (⫺2.96) (4.93)
Agriculture
⫺0.70 ⫺0.07 1.18
**
10.32 0.61 ⫺0.91 ⫺0.61 ⫺1.27
**
⫺0.76 ⫺0.64 0.84
*
(⫺0.61) (⫺0.12) (1.91) (0.81) (0.47) (⫺0.22) (⫺0.73) (⫺1.70) (⫺1.58) (⫺1.18) (3.12)
Construction
⫺0.11 0.16 0.03 0.97 0.06 1.17 ⫺0.05 ⫺0.25 ⫺0.10 ⫺0.16 0.86
*
(⫺0.61) (1.41) (0.19) (0.66) (0.16) (0.75) (⫺0.19) (⫺1.09) (⫺0.84) (⫺0.60) (2.79)
Finance
0.30 0.13
*
⫺0.24
**
⫺3.59 ⫺0.39 0.54 ⫺0.01 0.04 ⫺0.23
**
0.34 ⫺0.69
*
(1.08) (2.55) (⫺1.84) (⫺1.09) (⫺1.64) (0.54) (⫺0.04) (0.20) (⫺1.98) (1.65) (⫺3.79)
Manufacturing
⫺0.01 0.18
*
0.20
*
0.49 0.16 0.05 0.03 ⫺0.08 ⫺0.05 ⫺0.68 0.71
(⫺0.18) (6.21) (3.77) (0.91) (1.46) (0.11) (0.44) (⫺1.09) (⫺1.24) (⫺1.13) (1.33)
Durable goods
0.06 0.07 0.14 ⫺0.39 0.07 0.61 0.27
*
⫺0.03 ⫺0.02 ⫺0.87
*
0.96
*
(0.14) (1.58) (1.69) (⫺0.62) (0.48) (0.85) (2.36) (⫺0.27) (⫺0.46) (⫺2.69) (2.93)
Non-durable goods
⫺0.02 0.23
*
0.17
*
0.62 0.06 0.08 0.03 ⫺0.05 0.05 ⫺0.38 0.25
(⫺0.37) (7.07) (2.44) (1.00) (0.57) (0.20) (0.29) (⫺0.37) (0.93) (⫺0.86) (0.50)
Mining
0.12 1.15
*
1.56
*
⫺1.46 0.90 ⫺0.35 0.13 ⫺0.26 ⫺0.04 ⫺1.33
*
0.46
(0.35) (6.35) (4.35) (⫺0.40) (1.40) (⫺0.17) (0.24) (⫺0.60) (⫺0.14) (⫺1.92) (0.80)
Retail trade
⫺0.07 0.08 0.11 0.45 0.20 1.22 0.26 ⫺0.09 ⫺0.02 ⫺1.14
*
0.85
*
(⫺0.54) (1.18) (0.95) (0.46) (0.85) (1.07) (1.57) (⫺0.60) (⫺0.29) (⫺3.32) (4.22)
Service
⫺0.01 0.06 ⫺0.02 1.02 0.09 ⫺0.14 0.003 0.07 0.02 ⫺0.84
*
0.84
*
(⫺0.15) (1.56) (⫺0.30) (1.02) (0.61) (⫺0.26) (0.04) (0.76) (0.38) (⫺2.31) (3.39)
Transportation
0.08 0.02 0.01 ⫺0.64 ⫺0.20 ⫺0.06 0.33 ⫺0.01 0.01 ⫺1.08
*
0.92
*
(0.59) (0.21) (0.08) (⫺0.67) (⫺0.78) (⫺0.06) (1.65) (⫺0.05) (0.08) (⫺3.40) (3.40)
Wholesale trade
⫺0.10 0.32
*
0.34
*
1.25 0.29 0.16 0.02 ⫺0.21 ⫺0.06 ⫺0.92 0.41
(⫺0.72) (4.97) (2.68) (0.96) (1.45) (0.20) (0.12) (⫺1.27) (⫺0.67) (⫺1.29) (0.53)
a
B0, Intercept; B1, anticipated energy price; B2, unanticipated energy price; B3, anticipated money supply; B4, unanticipated money supply; B5, anticipated government spending; B6,
unanticipated government spending; B7, anticipated real effective exchange rate; B8, unanticipated real effective exchange rate; B9, error correction; RH0, serial correlation.
*
Significant at 5%.
**
Significant at 10%, t-ratios are in parentheses.

19M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
Table 5
Non-linear 3 SLS parameter estimates for price using nominal GDP shocks
a
B0 B1 B2 B3 B4 B5 B6 B7 RH0
Aggregate level
0.07
*
0.06
*
0.03 ⫺0.33 0.16 0.02 0.05 ⫺0.59
**
0.89
*
(2.09) (1.96) (0.89) (⫺0.98) (1.40) (0.32) (1.64) (⫺2.07) (3.70)
Agriculture
⫺0.52
*
⫺0.23 ⫺0.03 7.51
*
2.96
*
⫺0.80 ⫺0.23 ⫺0.54 0.54
(⫺2.01) (⫺0.63) (⫺0.09) (2.18) (3.08) (⫺1.14) (⫺0.78) (⫺1.39) (1.45)
Construction
⫺0.18 ⫺0.02 0.03 4.32
**
0.69
**
⫺0.45 ⫺0.09 ⫺0.11 0.99
*
(⫺0.12) (⫺0.17) (0.38) (1.77) (1.88) (⫺1.65) (⫺1.16) (⫺0.42) (4.27)
Finance
0.03 ⫺0.01 0.20
**
1.16 ⫺0.13 ⫺0.33 0.003 ⫺0.01
(⫺0.50) (⫺0.11) (1.84) (1.34) (⫺0.33) (⫺1.31) (0.03) (⫺0.11)
Manufacturing
0.05
*
0.15
*
0.12
*
⫺0.18 0.23
*
⫺0.0004 0.02 ⫺0.64 0.63
(1.86) (4.60) (3.98) (⫺0.57) (2.09) (⫺0.01) (0.64) (⫺1.45) 1.22
Durable goods
0.14
*
0.11
*
⫺0.02 ⫺1.47
*
⫺0.16 ⫺0.07 ⫺0.02 0.08 0.90
*
(2.22) (1.97) (⫺0.42) (⫺2.02) (⫺0.96) (⫺0.63) (⫺0.36) (0.51) (6.56)
Non-durable goods
0.05
**
0.19
*
0.17
*
⫺0.22 0.18 0.10 0.03 ⫺0.54 0.40
(1.86) (4.78) (4.17) (⫺0.63) (1.29) (0.11) (0.63) (⫺1.47) (0.75)
Mining
⫺0.08 1.21
*
0.82
*
0.75 0.28 ⫺0.16 0.04 ⫺0.38 ⫺0.54
(⫺1.20) (9.50) (3.93) (0.90) (0.34) (⫺0.45) (0.17) (⫺1.35) (⫺1.44)
Retail trade
0.04 0.05 0.05 0.11 0.16 ⫺0.04 0.05 ⫺0.84 0.42
(0.69) (0.61) (0.67) (0.17) (0.59) (⫺0.25) (0.75) (⫺1.33) (0.64)
Service
0.01 0.07 0.04 0.57 0.02 0.12 0.002 ⫺0.04
(0.53) (1.68) (0.77) (1.74) (0.08) (1.14) (0.04) (⫺0.10)
Transportation
⫺0.06 0.12 ⫺0.07 1.22
**
0.37 0.28 0.09 ⫺0.03
(⫺1.14) (1.22) (⫺0.68) (1.89) (0.79) (1.29) (0.90) (⫺0.09)
Wholesale trade
⫺0.04 0.21
*
0.33
*
0.76
*
0.06 ⫺0.05 ⫺0.07 ⫺0.57
*
(⫺1.38) (4.51) (4.54) (2.27) (0.23) (⫺0.44) (⫺0.89) (⫺2.04)
a
RHO, Serial correlation; B0, intercept; B1, anticipated energy price; B2, unanticipated energy price; B3, anticipated nominal GDP; B4, unanticipated nominal GDP; B5, anticipated
real effective exchange rate; B6, unanticipated real effective exchange rate; B7, error correction.
*
Significant at 5%.
**
Significant at 10%, t-ratios are in parentheses.

20 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
tionary effects of dollar appreciation are evident by the negative, although statistically
insignificant, effects of exchange rate fluctuations, both anticipated and unanticipated,
on aggregate real output growth.
At the sectoral level, the negative significant effect of anticipated energy price
shifts on real output growth appears more pervasive across industries: Manufacturing
(Durables and Non-durables); Retail Trade; and Wholesale Trade. There is also evi-
dence of a statistically significant negative effect for unanticipated energy price shifts
on output growth in Manufacturing (Non-durables) industries.
The non-neutral effects of anticipated demand shifts are significant in determining
real output growth in Construction, Manufacturing (Durables), Retail Trade, and
Transportation. The statistically significant effect of unanticipated aggregate demand
shifts appears more pervasive across sectors: Construction; Finance; Manufacturing
(Durables and Non-durables); Retail Trade; Services; Transportation; and Whole-
sale Trade.
The evidence remains robust, however, concerning the insignificant effect of fluc-
tuations in the exchange rate on real output growth for industries of the USA.
4.2. The price equation
The results of estimating the empirical model (17) for price are presented in Table
4. At the aggregate level, the inflationary effects of energy price shifts, both antici-
pated and unanticipated, are evident by the statistically significant coefficients. Other
sources of aggregate price inflation are evident by the positive and statistically sig-
nificant effects of anticipated monetary growth and unanticipated government spend-
ing shocks.
At the sectoral level, anticipated energy price shifts are evident by the positive
and statistically significant effect on price inflation in Finance, Manufacturing (Non-
durables), Mining, and Wholesale Trade industries. Similarly, unanticipated energy
price shifts increase price inflation significantly in Agriculture, Manufacturing (Non-
durables), Mining and Wholesale Trade.
Consistent with the evidence for output, changes in the growth of government
spending, both anticipated and unanticipated, are insignificant in explaining sectoral
price inflation. Unanticipated monetary growth has a statistically significant effect
on price inflation in Manufacturing.
More importantly, are the effects of exchange rate fluctuations on price inflation.
Inflation is smaller in the face of anticipated appreciation, as evident by the negative,
although statistically insignificant effect on price in the majority of industries. Unan-
ticipated shocks to the dollar have negative effects on price in the majority of indus-
tries, which is statistically significant in Finance.
The evidence remains robust in the empirical model employing the effects of
nominal GDP shifts on price inflation. At the aggregate level, the inflationary effect
of anticipated energy price shifts remains positive and statistically significant. The
effects of exchange rate shifts, both anticipated and unanticipated, while positive,
are statistically insignificant.
At the sectoral level, the positive significant effect of anticipated energy price

21M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
shifts on price inflation remains pervasive across industries: Manufacturing (Durables
and Non-durables); Mining; and Wholesale Trade. The inflationary effect of unantici-
pated shifts in the energy price is also evident in Finance, Manufacturing (Non-
durables), Mining and Wholesale Trade.
The inflationary effect of anticipated demand shifts is evident for Agriculture,
Transportation and Wholesale Trade. The statistically significant effect of unantici-
pated aggregate demand shifts is evident on price inflation in Agriculture, Construc-
tion, and Manufacturing
22
.
In the empirical model explaining industrial price using nominal GDP shocks, the
negative effect of exchange rate shocks on the output price is evident, although
statistically insignificant, in many industries. As predicted by the theory, the negative
effect on industrial output price is the result of demand and supply changes in
response to a shock to the exchange rate. For example, industries buy cheaper inter-
mediate goods in the wake of dollar appreciation. Concurrently, the dollar appreci-
ation decreases aggregate demand for industrial output in the USA.
4.3. Asymmetry in the effects of exchange rate shocks
Table 6 summarizes the effects of positive and negative exchange rate shocks in
the reestimation of the empirical models (15) through (18)
23
. Consistent with the
evidence in Tables 2 and 3, appreciation and depreciation shocks to the dollar do
not determine real output growth significantly for the various industries. Asymmetry,
however, appears pronounced for the Finance industry. Unanticipated dollar appreci-
ation decreases Finance real output growth significantly. This evidence is consistent
with a decrease in demand in the face of appreciation shocks. Asymmetry also
appears pronounced for the Wholesale Trade industry. Unanticipated dollar
depreciation decreases real output growth significantly in the Wholesale Trade indus-
try. This evidence is consistent with a decrease in supply in the face of
depreciation shocks.
Consistent with the evidence in Tables 4 and 5, exchange rate shocks, both
appreciation and depreciation, have negative effects on industrial price. That is,
inflation is smaller in the face of dollar appreciation; inflation is higher in the face
of dollar depreciation. This signals the importance of supply-side effects on the
response of industrial price to exchange rate shocks. An exception is noted for the
Finance industry. Dollar appreciation increases the price of output for the Finance
industry. We recall the money demand channel in our theoretical model in an attempt
to provide an explanation. Financial institutions may raise the interest rate on dollar-
denominated deposits in an attempt to maintain liquidity in the wake of dollar
appreciation. Consequently, the price of output increases in the Finance industry
following an unanticipated increase in the dollar value.
22
Where price inflation responds negatively to demand or energy price shifts, the evidence indicates
price rigidity. That is, price inflation is decreasing despite inflationary pressures from demand or sup-
ply shifts.
23
Detailed estimates are available upon request.

22 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
Table 6
Non-linear 3S5LS parameter estimates of positive and negative shocks on real effective exchange rate of the US$
a
Model 15 Model 16 Model 17 Model 18
A8
p
A8
n
A6
p
A6
n
B8
p
B8
n
B6
p
B6
n
Aggregate ⫺6.18 4.51 ⫺0.02 0.01 ⫺0.63 ⫺0.53 ⫺0.004 0.03
(⫺0.10) (0.10) (⫺0.40) (0.13) (⫺0.64) (⫺0.64) (⫺0.13) (0.78)
Agriculture ⫺0.69 1.44 0.19 ⫺0.38 1.06 ⫺0.01 ⫺0.25 ⫺0.20
(⫺0.20) (0.33) (0.45) (⫺1.03) (0.38) (⫺0.00) (⫺0.55) (⫺0.49)
Construction ⫺1.83 1.37 ⫺0.14 0.06 1.84 ⫺0.47 0.05 ⫺0.15
(⫺0.50) (0.46) (⫺0.73) (0.32) (0.63) (⫺0.20) (0.53) (⫺1.53)
Finance 4.00 0.90 ⫺0.15
**
0.002 3.26 3.97 0.30
**
⫺0.21
(0.59) (0.27) (⫺1.85) (0.03) (0.57) (0.46) (1.88) (⫺1.56)
Manufacturing 2.14 ⫺0.35 ⫺0.04 0.001 ⫺0.10 ⫺0.29 0.02 ⫺0.005
(0.19) (⫺0.05) (⫺0.37) (0.01) (⫺0.20) (⫺0.62) (0.45) (⫺0.11)
Durable goods ⫺0.33 0.37 ⫺0.15 ⫺0.11 ⫺0.99 ⫺1.26 ⫺0.02 ⫺0.05
(⫺0.01) (0.13) (⫺0.68) (⫺0.53) (⫺0.20) (⫺0.31) (⫺0.34) (⫺0.75)
Non-durable goods 0.61 ⫺1.04 ⫺0.02 ⫺0.02 ⫺0.20 ⫺0.30 0.06 ⫺0.03
(0.13) (⫺0.22) (⫺0.23) (⫺0.26) (⫺0.39) (⫺0.63) (0.90) (⫺0.52)
Mining ⫺0.04 ⫺1.00 ⫺0.26 0.32 ⫺0.11 ⫺0.41 0.13 ⫺0.006
(⫺0.03) (⫺0.65) (⫺0.70) (0.81) (⫺0.29) (⫺1.29) (0.38) (⫺0.02)
Retail trade 1.31 ⫺2.04 ⫺0.11 0.04 1.14 2.94 ⫺0.02 0.09
(0.17) (⫺0.22) (⫺1.24) (0.42) (0.17) (0.18) (⫺0.22) (0.83)
Service 0.57 ⫺0.03 ⫺0.02 0.03 ⫺0.57 0.61 ⫺0.03 0.01
(0.22) (⫺0.01) (⫺0.27) (0.34) (⫺0.32) (0.37) (⫺0.36) (0.14)
Transportation 0.27 ⫺0.43 ⫺0.01 ⫺0.10 1.90 0.41 0.10 0.11
(0.21) (⫺0.32) (⫺0.06) (⫺0.99) (1.30) (0.33) (0.63) (0.67)
Wholesale trade 1.47 1.24 ⫺0.08 0.26
*
0.01 ⫺0.44 ⫺0.03 ⫺0.03
(1.28) (1.05) (⫺0.63) (2.08) (0.01) (⫺0.65) (⫺0.28) (⫺0.25)
a
A8
p
, Positive shock to real effective exchange rate, for output equation including money and government spending; A8
n
negative shock to real effective exchange rate, for output
equation including money and government spending; A6
p
, positive shock to real effective exchange rate, for output equation including nominal GDP; A6
n
, positive shock to real effective
exchange rate, for output equation including nominal GDP; B8
p
, positive shock to real effective exchange rate, for price equation including money and government spending; B8
n
, negative
shock to real effective exchange rate, for price equation including money and government spending; B6
p
, positive shock to real effective exchange rate, for price equation including nominal
GDP; B6
n
, positive shock to real effective exchange rate, for price equation including nominal GDP.
*
Significant at 5%.
**
Significant at 10%, t-ratios are in parentheses.

23M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
4.4. An assessment
Given qualitative differences across industries of the US economy, we offer the
following observations. For more detailed discussion, see The Economic Effects of
Significant US Import Restraints, US International Trade Commission (1995)
24
.
As Campa and Goldberg (1997) pointed out (see Table 1), US industries experi-
enced an increased international exposure in the early to mid-1980s through their
reliance on imported inputs in production. The imported input share (see Table 7)
has more than doubled in many manufacturing industries over the past two decades.
Dollar appreciation decreases the competitiveness of manufactured American pro-
ducts in the international market while increasing domestic demand for manufactured
foreign products. Further, the manufacturing sector has the highest export share
among major US industries (see Table 7). This market mechanism necessitated inter-
vention through voluntary trade restraints
25
. Trade restraints are likely to have moder-
ated the statistical significance of the effects of dollar appreciation on Manufactur-
ing industries
26
.
In the case of agriculture, export share is the second highest among major US
industries (see Table 7). Nonetheless, the import share is relatively smaller. Further,
trade restraints are very stringent. Agricultural products have tarrif equivalents gener-
ally exceeding 20%. Indeed, price deflation in agriculture is not significant in the
face of dollar depreciation.
While the results do not demonstrate significant deflationary effect of dollar
depreciation on the price of output in Construction, the US schedule of commitments
effectively places no limitations on the provision of construction services by foreign
firms. See Table 8 for some illustrative figures between 1990 and 1992. Nonetheless,
24
This report is on the economic effects of significant US import restraints on the US economy, prepared
at the request of the USA Trade Representative as a direct successor to a similar report in 1993. The report
addresses the economic effects of the liberalization of significant US import restraints in Manufacturing,
Agriculture and Services. Within each sector, the authors look at those products which have the highest
import restraints. The base year for the study is 1993. The import restraints examined are tariffs and
quantitative restraints, such as quotas, Voluntary Restraints agreements (VRAs) and Voluntary Export
Restraints (VERs). More specifically, tariffs are specified as the average Most Favored Nation (MFN) ad
valorem tariff calculated for 1993.
25
For example, for the years 1992–1993 and 1993–1994, a voluntary trade restraint of 1.65 million
units per year was in place on imports of autos from Japan, imports in 1993 were at 97% of this quota.
26
Indeed, The Economic Effects of Significant US Import Restraints (1995) provide the following
evidence of trade effects in the Manufacturing industry. The most significant changes in prices faced by
consumers as a result of import liberalization are as follows: Rubber and Plastic Footwear (⫺12%),
Nonrubber Footwear (⫺5.5%), China Tableware (⫺5.4%), Leather Gloves (⫺5.4%) and Ceramic Floor
and Wall Tile (⫺4.9%). Similarly, removal of MFN tarrifs and VER led to a decline of 2098 full-time
equivalent jobs and a reduction of 0.4% in domestic output in Motor Vehicles industries. Consistently,
import prices fell by 1.8% which translates into a 0.9% decline in prices faced by US consumers. The
effects of lower import prices were illustrated by 1.4% increase in imports. However, exports expanded
as well by US$36 million, fueled by a number of factors, including lower input prices. In particular, auto
producers took advantage of lower prices for blast furnace and steel mill products.

24 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
Table 7
Export share, import share, and the degree of openness for the US major industries
Industry 1987 1992
Export share Import share Degree of openness Export share Import share Degree of openness
Agriculture 6.99 3.48 10.47 8.43 6.20 14.63
Mining 4.77 27.74 32.51 5.56 30.51 36.06
Construction 0.02 0.00 0.02 0.01 0.00 0.01
Manufacturing 8.20 15.63 23.83 12.38 16.80 29.19
Transportation and utilities 4.38 1.19 5.57 6.45 1.36 7.81
Wholesales trade 6.21 3.67 9.87 16.63 6.81 23.44
Retail trade 0.02 0.00 0.02 0.00 0.00 0.00
Finance 2.24 0.28 2.52 2.41 0.09 2.50
Service 0.38 0.10 0.47 0.83 0.17 1.00

25M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
Table 8
Trade statistics compared to total output value and employment: selected years
Sectors Variables 1990 1991 1992 1993
Banking, insurance and other financial services
Production (billion dollars) 341 377 407
Exports (billion dollars) 4389 5135 4857
Imports (million dollars) 5167 6022 7179
Construction Production (billion dollars) 240 223 222
Exports (billion dollars) 44 69 72
Imports (million dollars) 15 12 9
Non-durable goods
Non-rubber footwear Employment (1000 workers) 67.3 64.3 62.9
Exports (million dollars) 305.6 341.9 330.8
Imports (million dollars) 8311.9 8587.5 9256.2
Rubber footwear Employment (1000 workers) 10.9 10.8 10.7
Exports (million dollars) 110.8 120.2 119.5
Imports (million dollars) 791.8 1028.7 1332.1
Leather gloves and mittens Employment (1000 workers) 2.8 2.5 12.3
Exports (million dollars) 13.1 12.2 14.0
Imports (million dollars) 112.8 117.0 148.7
Ceramic wall and floor tile Employment (1000 workers) 9.5 9.0 9.0
Exports (million dollars) 21.0 19.3 22.6
Imports (million dollars) 365.1 418.5 471.9
China tableware Employment (1000 workers) 6.0 5.3 5.3
Exports (million dollars) 43.0 50.2 46.8
Imports (million dollars) 290.1 307.8 316.6
Durable goods
Motor vehicles Employment (1000 workers) 316 314 319
Exports (million dollars) 14,892 17,265 18,135
Imports (million dollars) 54,136 56,042 61,760
Intermediate goods
Blast furnaces and steel mills Employment (1000 workers) 196 187 175
Exports (million dollars) 3728 3041 2821
Imports (million dollars) 7760 7841 8552

26 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
the degree of openness (see Table 7) is the lowest in construction among major
US industries.
Trade constraints are less operative in the Finance industry, providing for a larger
degree of openness. Although foreign firms claim that the US regulatory systems
for the financial sector are unnecessarily overlapping and expensive, foreign firms
seem to be treated largely the same as domestic firms. Indeed, there is plenty of
evidence that foreign firms can enter the US financial market: over 400 foreign-
owned insurance companies, from 28 countries, operate in the USA. These firms
write a minimum of 10% of the total insurance market. In the securities market, there
are 63 foreign companies, from 12 countries. As to foreign banking peneteration of
the US market, foreign companies have 21% of total assets in the US commercial
banking system. Table 7 illustrates the export share, the import share, and the degree
of openness in Finance relative to other major US industries. Table 8 summarizes
statistics between 1990 and 1992 which illustrates openness in the Finance industry.
Given the presence of foreign firms in the US financial market, dollar appreciation
decreases the demand for their output and decreases liquidity. This is evident by the
negative statistically significant effect of dollar appreciation on output and the posi-
tive effect on price in Finance.
While the effect of dollar appreciation is not significant in the Services industry,
US imports restraints are not strongly operative in this industry. Despite claims that
foreign providers of some services face constraints on operations in the USA, most
of these barriers are, in fact, requirements that foreign service providers adhere to
some regulatory schemes faced by domestic providers of services
27
. Table 7 illus-
trates, however, that the degree of openness is relatively smaller in the services sector
compared to other major US industries.
The evidence for the Transportation sector indicates no reduction in price inflation
in the face of dollar appreciation
28
. Indeed, within the transportation sector, the air
transport industry has significant restraints in the form of restrictive regulations and
bilateral agreements that effectively restrain international air transport services. Like-
wise, maritime transport is subject to significant import restraints by means of restric-
tive regulations
29
. Maritime transportation has a tarriff equivalent of 89.1%. Further,
Table 7 illustrates that the import share in the Transportation industry is very small.
Finally, we have attempted to explain variation in sectoral exposure to exchange
rate shocks based on trade restraints and available trade statistics. We believe, how-
ever, that more data are necessary to fully explain sector’s external exposure. There
are many other factors that determine the size and character of the sector’s external
exposure. Future research should focus on measures such as sectoral import pen-
27
These regulatory schemes are not considered discriminatory. For example, requirements that foreign
financial firms maintain assets in the US if they want to operate in the US are not considered discriminatory
as long as US firms face the same requirements.
28
In model (18), the response of price inflation is positive to dollar appreciation, both anticipated
and unanticipated.
29
One of the more important set of restrictions is the Merchant Marime Act of 1920 (Jones Act), which
prohibits foreign vessels from carrying domestic freight between US ports.

27M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
etration and use of foreign intermediate inputs toward a thorough investigation of
the output and price effects of the exchange rate at the sectoral level.
5. Summary
This investigation has focused on the effect of exchange rate fluctuations in
determining economic conditions across industries of the USA. Towards this investi-
gation, we build a theoretical model that incorporates the effects of exchange rate
fluctuations on the demand and supply sides of the economy. We identify three
directions for the effects of an unanticipated appreciation of the dollar on the econ-
omy. The first channel is on the demand side through the effects of appreciation in
increasing imports and decreasing exports. The result is a contraction of aggregate
demand. The second channel is through the effect of appreciation in decreasing the
demand for the dollar as agents expect the exchange rate to return to its anticipated
steady-state value. The result is an expansion of aggregate demand. On the supply
side, appreciation allows producers to buy cheaper intermediate goods. The result is
an expansion of the output supplied. The combined effects of the three channels
remain indeterminate on output and price.
We attempt to investigate these effects on the US economy. First, we use aggregate
data for output and price. Secondly, we disaggregate the data using sectoral output
and price data for the USA. Given demand and supply channels, there is little evi-
dence of a significant effect of the dollar appreciation on industrial output growth.
It is noted, however, that dollar appreciation (via the decreased demand channel)
decreases output growth significantly in the Finance industry. In contrast, dollar
depreciation (via the decreased supply channel) decreases output growth significantly
in the Wholesale Trade industry. In contrast, demand contraction and supply expan-
sion are consistent with a negative response of price to exchange rate shocks. It is
noted, however, that price inflation in the finance industry is significantly higher in
the face of dollar appreciation (perhaps due to reduced liquidity in dollar-denomi-
nated assets).
We conclude: given the small degree of openness for industries of the USA, the
results of external shocks and exchange rate fluctuations generate moderate price
effects without significant adverse effects on output growth. Accordingly, concerns
about the adverse effects of dollar appreciation on economic performance are not
supported by the disaggregate evidence for industries of the USA.
Acknowledgements
The authors would like to thank Professor Yoshio Niho for assistance in the theor-
etical derivations, and Professors Swarnjit Arora, Mohsen Bahmani, and Kamil Tah-
miscioglu for helpful comments. We also thank two anonymous referees, and the
Editor, James R. Lothian, for helpful suggestions on earlier drafts of this paper.

28 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
Appendix A
Econometric methodology
The surprise terms that enter models (15) through (18) are unobservable, necessit-
ating the construction of empirical proxies before estimation can take place. Thus,
the empirical models include equations describing agents’forecast of aggregate or
specific demand growth, the change in energy price, and the change in the price of
the dollar in foreign currency (the exchange rate).
To decide on variables in the forecast equations for each of the demand and supply
shifts, we follow a formal causality test. Each variable is regressed on two of its
lags as well as two lags of all variables that enter the model: the change in the log
value of the energy price; nominal GDP; aggregate productivity; the real effective
exchange rate; government spending; and the money supply. We then test the joint
significance of the lags for each variable. The results are available upon request.
Accordingly, the forecast equations account for the lags of variables proven to be
statistically significant.
To test for asymmetry in the effects of exchange rate shocks, positive and negative
components are defined for joint estimation, following the suggestions of Cover
(1992) as:
negs
t
⫽⫺
1
2{abs(Dss
t
)⫺Dss
t
}
poss
t
⫽1
2{abs(Dss
t
)⫹Dss
t
}
where Dss
t
is the exchange rate shock, as specified above, and negs
t
and poss
t
are the
negative and positive components of the shocks. abs(.) is the absolute value operator.
Obtaining a proxy for ex-ante forecasts of the energy price and the exchange rate
is complicated by the assumption that the generating process experienced a structural
change between 1973 and 1974. This assumption is supported by the results of a
formal test suggested in Dufour (1982). For both the period 1961–1973 and the
period 1974–1994, the generating process is modelled as described above.
Surprises that enter the empirical models are then formed by subtracting these
forecasts from the actual change in the forecasted variable. In order to obtain efficient
estimates and ensure correct inferences (i.e., to obtain consistent variance estimates),
the empirical models are estimated jointly with the equation that determines the
proxy variables following the suggestions of Pagan (1984, 1986). To account for the
endogeneity of forecasted variables, instrumental variables are used in the estimation
of the empirical models. The instrument list includes four lags for each of the first-
difference of the log value of the energy price, the exchange rate, the money supply,
government spending, nominal GDP, and aggregate productivity.
Following the suggestions of Engle (1982), the results of the test for serial corre-
lation in simultaneous equation models are consistent with the presence of first-order
autoregressive errors for some industries. To maintain comparability, it is assumed

29M. Kandil, A. Mirzaie / Journal of International Money and Finance 21 (2002) 1–31
in all models that the error term follows an AR(1) process. The estimated models
are transformed, therefore, to eliminate any possibility for serial correlation. The
estimated residuals from the transformed models have zero means and are serially
independent.
Appendix B
Data sources
Sample period: 1961–1994
The following annual data were taken from: The National Income and Product
Accounts of the USA (NIPAA); 1929–1982 Statistical Tables; and US Department
of Commerce/Bureau of Economic Analysis. Updates for the years 1983–1994 are
provided in the July issues of Survey of Current Business.
1. Nominal GDP by industry, Table 6.1.
2. GDP by industry in constant dollars (1982=100), Table 6.2.
3. Sectoral price level=nominal output by industry/constant dollar output by industry.
4. Full-time equivalent employees by industry, Table 6.6B.
5. Sectoral productivity=the ratio of constant dollar output to the full-time equivalent
employees by industry.
Other series are as follows:
1. Producers Price Index (1982=100) for Fuels, Power and Related Products —His-
torical Series 1926–1997, the US Department of Labor, Bureau of Labor Statistics.
2. Real Effective Exchange Rate: real weighted exchange rate (1980=100), Gordon
(1993), Appendix A, Table 8, Quarterly data for 1947–1992 of real weighted
exchange rate (1980=100). Data are transformed to annual using the E-View stat-
istical package. Updates for the years 1993 and 1994 are from International Finan-
cial Statistics. We follow a standard splicing procedure to combine the series from
both sources.
3. Government spending: government expenditure on consumption and gross invest-
ment, NIPAA.
4. Money supply: M2, Commerce Department, data obtained from the Citibase tape.
5. Import and export shares: based on exports, imports, and commodity output, Table
2.1, The Use of Commodities by Industries, 1992 Benchmark Input–Output
accounts for the US economy, 1994 for 1987, and 1992 for November 1997.
6. Degree of openness: the sum of export and import shares.

30 M. Kandil, A. Mirzaie /Journal of International Money and Finance 21 (2002) 1–31
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