Embed Size (px)
Citation preview
ORI GIN AL PA PER
Commodity trade between EU and Egyptand Orcutt’s hypothesis
Mohsen Bahmani-Oskooee • Amr Sadek Hosny
� Springer Science+Business Media New York 2013
Abstract Orcutt hypothesized that trade flows respond faster to a change in the
nominal exchange rate as compared to a change in relative prices. Although he
recommended testing his hypothesis at commodity level, due to lack of commodity
prices previous studies used aggregate trade flows of one country with the rest of the
world and did not support the hypothesis. In this paper, we test Orcutt’s hypothesis
using trade flows of 59 industries that trade between European Union and Egypt.
These are the industries that account for 100 % of the trade between the two regions
and for which price data are available. We find support for the Orcutt’s hypothesis
in 1/3rd of industries.
Keywords Orcutt’s hypothesis � Egypt-EU. Trade � Industry data
JEL Classification F31
1 Introduction
Nominal exchange rate changes are said to affect trade flows faster than relative
price changes (say due to subsidies or tariff). Orcutt (1950) was the first to
conjecture this without providing empirical support. Subsequent studies which
tested Orcutt’s hypothesis provided mixed findings. The list includes Junz and
Rhomberg (1973), Wilson and Takacs (1979), Bahmani-Oskooee (1986), Tegene
M. Bahmani-Oskooee (&)
Department of Economics, The Center for Research in International Economics,
The University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
e-mail: [email protected]
A. S. Hosny
International Monetary Fund, 700 19th Street, N.W., Washington, DC 20431, USA
e-mail: [email protected]
123
Empirica
DOI 10.1007/s10663-013-9237-8
(1989, 1991), and Bahmani-Oskooee and Kara (2003). The common practice in
these studies is to estimate an import and an export demand models in which a lag
structure is imposed on the relative prices and nominal exchange rate. They then
judge the hypothesis by looking at significant lag length of both variables.
One common feature of the above studies is that they have considered a
country’s trade flows with the rest of the world, though the sample of countries
differs from one study to another. For example while Wilson and Takacs (1979)
included six developed countries in their study, Bahmani-Oskooee(1986)
concentrated on only seven developing countries. Failure to find a uniform
support for Orcutt’s hypothesis could be due to aggregation bias in that a
country’s aggregate trade flows with the rest of the world are employed. A better
approach then would be to disaggregate trade flows by trading partners and test
the hypothesis at bilateral level between two trading partners.1 No attempt has
been made on this regard due to the fact that no price data are available at
bilateral level. In other word, there are no export and import price indexes
between two trading partners whereas these indexes are available between one
country and rest of the world.
Disaggregating trade flows was also favored by Orcutt (1950, p. 126) who argued
that different commodities react differently to price changes and considering trade
flows at commodity level could give us a relatively more clear picture. To the best
of our knowledge, no study has followed this rout neither mostly due to lack of data
on commodity prices. Now that we have come across 59 different commodity prices
that are traded between Egypt and EU, we would like to test Orcutt’s conjecture at
commodity level as favored by Orcutt.
Therefore, the main purpose of this paper is to test Ocrcutt’s conjecture at
commodity level. Quarterly data are only available during the period 1994–2007
and are used in this study. To this end, in Sect. 2 we outline the models and explain
our method which is based on ARDL or bounds testing approach to co integration
and error-correction modeling. Section 3 presents our results and Sect. 4 concludes.
Data definition and sources are cited in an ‘‘Appendix’’.
2 The models and the method
As mentioned before, in this section we try to outline import and export demand
models that have been used before in testing the Orcutt’s hypothesis. The only
modification is to change some notations so that they conform to commodity level
data. Specifically, since the data are reported by Egypt, following Wilson and
Takacs (1979), Bahmani-Oskooee (1986), Tegene (1989, 1991), and Bahmani-
Oskooee and Kara(2003) we assume that Egypt’s import demand for commodity i
takes the following specification in natural logarithm (ln) so that the coefficients
reflect elasticties:
1 The idea is actually borrowed from Rose and Yellen (1989) who raised this concern in testing the
J-Curve effect.
Empirica
123
ln Mit ¼ aþ b lnYEG
t þ c lnPMi
PD
� �t
þd lnE þ et ð1Þ
where Mi is Egypt’s import of commodity i from European Union (EU). We
have assumed that Egypt’s import depends on Egypt’s income (YEG). If an
increase in Egypt’s income boosts Egypt’s imports of commodity i, we would
expect an estimate of b to be positive. However, if increase in Egypt’s income is
due to an increase in the production of import-substitute goods, Egypt’s imports
of commodity i could decline, hence a negative estimate for b. The second
determinant of imports of commodity i is identified to be the relative price of
commodity i. The variable is denoted by import price of commodity i (PMi)
relative to domestic price level (PD). We expect an estimate of c to be negative.
Finally, nominal exchange rate, E, defined as number of Egyptian pounds per
euro is another determinant of Egypt’s imports of commodity i. If depreciation of
Egyptian pound is to reduce Egypt’s imports, an estimate of d is expected to be
negative.
Estimating equation (1) by any method gives only the long-run coefficient
estimates without shedding any light on the Orcutt’s hypothesis. Since the
hypothesis involve dynamic adjustment of imports to changes in relative prices and
the nominal exchange rate, we need to incorporate short-run dynamic adjustment
mechanism into long-run model outlined by equation (1) as in equation (2):
Dln Mit ¼ aþ
Xn
k¼0
bkDlnYEGt�k þ
Xn
k¼0
ckDlnPMi
PD
� �t�k
þXn
k¼0
kkDlnEt�k þXn
k¼1
hkDlnMit�k
þ d1lnYt�1þ d2lnPMi
PD
� �t�1
þd3lnEt�1þ d4 ln Mit�1þ ut ð2Þ
Equation (2) is an error-correction model that follows Pesaran et al. (2001)
and is similar to Engle-Granger representation theorem in spirit. The only
difference is that the lagged error-correction term from (1), i.e., et-1 is replaced
by the lagged level variables since they are equal by deduction. However,
specification (2) by Pesaran et al. (2001) has a few advantages over (Engle and
Granger 1987) specification. One advantage is that there is no need for pre unit-
root testing since the integrating properties of the variables are incorporated in
testing for co integration. Pesaran et al. (2001) propose using standard F test to
establish joint significance of the lagged level variables as a sign of co
integration. However, this F test has new critical values that they tabulate. An
upper bound critical value is provided by assuming all variables to be I(1). A
lower bound critical value is provided by assuming all variables to be I(0). For
co integration, the calculated F statistic should be greater than the upper bound
critical value. They also show that the upper bound critical value could be used
even if some variables are I(1) and some I(0). Since majority of time-series
macro variables are either I(1) or I(0), there is no need for pre unit-root testing.
The second advantage of this approach is that it is a one-step procedure in which
the short-run effects are estimated along with the long-run effects. Indeed, the
long-run effects of all variables on the level of imports are inferred by the
Empirica
123
estimates of d1 – d3 that are normalized on d4. The short-run effects are obtained
by the estimates of coefficients attached to first-differenced variables. Testing
Orcutt’s hypothesis in this set up amounts to determining the number of lags on
the relative price term compared to the number of lags on the nominal
exchange rate. Orcutt’s hypothesis implies that the lags be shorter on the
exchange rate.2
We now ask the same question related to response of exports to a change in
relative prices and the nominal exchange rate. To that end, again we follow previous
research and adopt the following long-run export demand model:
ln Xit ¼ aþ b ln YEU þ c ln
PXi
PEU
� �t
þd ln Et þ xt ð3Þ
where Xi is the demand for Egypt’s export of commodity i by EU. Again, three
variables are assumed to be the main determinant. First is the European Union
income or economic activity, YEU which is expected to have positive effect on
Egypt’s exports. Second is the price that Egypt charges on its exports (PXi)
relative to the price that prevails in Europe (PEU). We expect an estimate of c to
be negative since any increase in relative price of exports will hurt Egypt’s
exports. Finally, the nominal exchange rate, E, is the last determinant. We expect
an estimate of d to be positive since a depreciation of Egyptian pound (i.e., an
increase in E) is expected to boost Egypt’s export of commodity i.3
Testing Orcutt’s conjecture related to exports is no different than testing it for
imports. Again, all we need to do is to express equation (3) in an error-correction
modeling format as in (4):
D lnXit ¼ lþ
Xm
k¼0
ukD ln YEUt�k þ
Xm
k¼0
wkD lnPXi
PEU
� �t�k
þXm
k¼0
nkD ln Et�k
þXm
k¼1
/kD ln Xit�k þ h1 ln YEU
t�1 þ h2 lnPXi
PEU
� �t�1
þh3 ln Et�1 þ h4 ln XEUt�1 þ vt
ð4Þ
We estimate (4) by applying the Ordinary Least Squares and apply the F test
to establish joint significance of lagged level variables, derive the long-run
coefficient estimates by normalizing estimates of h1– h3 on h4 and short-run
effects by the estimates of coefficients attached to first-differenced variables.4
2 For some other applications of this method see Bahmani-Oskooee and Hegerty (2007) (Bahmani-
Oskooee and Gelan 2009), De Vita and Kyaw (2008), Halicioglu (2007), Mohammadi et al. (2008),
Narayan et al. (2007), Payne (2008), Tang (2007), and Wong and Tang (2008).3 For some other estimates of import and export demand functions see King (1993), Alse and Bahmani-
Oskooee (1995), Charos et al. (1996),Truett and Truett (2000), Du and Zhu (2001), Love and Chandra
(2005), Agbola and Damoense (2005), Narayan and Narayan (2005), and Narayan et al. (2007).4 Some studies have only estimated equations (1) and (3) to judge the price elasticities, hence the
Marshall-Lerner condition. Examples are Houthakker and Magee (1969), Marquez and McNeilly (1988),
Bahmani-Oskooee and Niroomand(1998), Caporale and Chui (1999), and Warner and Kreinin (1983).
Empirica
123
3 The results
As mentioned in the introductory section, quarterly data over the period 1994I-
2007IV are available on all variables for each of the 59 industries that trade between
Egypt and Europe.
These 59 industries engage in almost 100 % of trade between the two regions.
Previous research has shown that the estimation results could be sensitive to number
of lags imposed on first differenced variables. Specially, since Orcutt’s hypothesis
involves judging lag length on relative prices versus lag length on the exchange rate,
we must avoid an arbitrary choice of lags length. Since the data are quarterly, we
impose a maximum of eight lags on each first-differenced variable and use Akaike’s
Information Criterion (AIC) to select optimum lags. We then report the results in
several Tables from each optimum model.5
The import demand model outlined by equation (2) is considered first. Due to
volume of the results we only report short-run coefficient estimates of relative prices
and the nominal exchange rate in Table 1. The long-run coefficient estimates of all
three determinants of imports along with diagnostic statistics are then reported in
Table 2.
From the short-run coefficients in Table 1 we gather that the lag length is shorter
on the exchange rate than relative prices in only 20 out of 59 industries. These
industries are coded to be 00, 03, 05, 08, 11, 12, 22, 26, 27, 43, 56, 61, 62, 66, 69,
71, 73, 75, 87, and 89.6 While most of these industries are small (reflected by their
trade shares in Table 2), four relatively large industries are among the 20. These are
industries coded 05 (Vegetables and fruit with 4.09 % trade share), 69 (Manufac-
tures of metals with 2.09 % trade share), 75 (Office machines with 4.81 % trade
share), and 87 (Professional and scientific apparatus with 1.35 % trade share). On
the other hand, there are only nine industries in which lags are shorter on relative
prices as compared to number of lags on the exchange rate. These industries are
coded as 07, 42, 55, 67, 72, 74, 76, and 84. Again four relatively large industries are
among these nine. They are 67 (Iron and steel with 4.40 % trade share), 72
(Machinery specialized for particular industries with 4.33 % trade share), 74
(General industrial machinery with 5.50 % trade share), and 76 (Telecommunica-
tion and sound-recording and producing apparatus with 4.81 % trade share). In the
remaining 30 industries which includes the largest industry coded as 33 (Petroleum
and petroleum related materials with 18.19 % trade share) lag length is the same. In
sum, considering Egypt’s imports from Europe, Orcutt’s hypothesis is supported
only in 1/3rd of the industries. Do these short-run effects translate into the long-run?
To that end we move to Table 2.
From Table 2 we gather that the Egypt’s income carries a significant coefficient
in 32 industries and in 21 of them the coefficient is negative implying that Egypt
follows more of an import-substitution policy. The relative price term carries its
expectedly negative and significant coefficient in 47 out of 59 industries, implying
that relative import price is perhaps the most important determinants of imports in
5 We had to make sure that variables were either I(0) or I(1) and no variable was I(2).6 Note that the name of each industry appears in Table 2.
Empirica
123
Ta
ble
1S
ho
rt-r
un
esti
mat
es—
imp
ort
dem
and
mod
el(2
)
Lag
son
rela
tive
import
pri
ceL
ags
on
nom
inal
exch
ange
rate
01
23
45
70
12
34
56
7
00
-0.5
***
-0.0
70.1
4-
0.0
90.0
02
-0.4
***
3.0
6-
2.6
05.4
2**
-1.0
6-
5.0
7**
01
-1.8
***
1.8
4
02
-0.2
60.1
4
03
-1.7
***
1.0
7*
1.5
2***
0.3
7-
0.2
5-
0.2
30.8
9*
-0.5
01.7
7*
-1.0
4-
2.3
3*
1.7
41.5
52.0
5**
04
-2.1
***
0.7
8
05
-2.2
***
3.1
1***
1.0
51.7
2***
1.7
3***
2.2
9***
1.3
8***
0.2
4-
2.5
6-
1.5
5-
3.1
2*
06
-1.7
***
3.7
9***
3.2
0***
3.4
2***
3.5
9***
3.3
3***
1.3
2***
-6.1
***
-2.7
1-
2.4
0-
3.2
1-
2.2
0-
3.3
8-
6.9
***
-3.7
3
07
-0.3
1**
1.4
2**
-1.7
1**
08
-1.4
***
4.8
6***
3.3
9***
2.3
9***
1.2
3**
0.4
3-
0.7
9-
1.3
0
09
-0.1
4-
1.3
1*
11
-1.1
***
3.0
0***
2.8
8***
3.1
2***
2.5
6***
2.1
6***
0.8
90.5
0-
7.7
***
-7.0
***
12
0.4
7**
-1.9
***
-2.4
***
-1.9
***
-1.6
***
-1.6
***
-0.6
50.0
3-
0.9
61.0
4*
22
-1.7
***
-0.7
4-
0.4
70.1
80.0
90.2
7-
0.4
5**
-0.9
4
23
-0.9
***
0.5
8
24
-1.0
***
-1.1
***
-0.6
4**
-0.2
6-
0.5
1**
-0.7
***
-0.2
5-
1.1
80.0
20.3
6-
0.1
5-
0.3
31.1
6*
-0.4
9-
2.7
2**
26
-0.8
***
0.7
5**
0.6
0**
0.4
0*
0.2
3*
0.0
6-
1.1
9-
1.0
7-
2.5
0**
2.5
8**
2.2
5**
-2.1
9**
27
-1.1
***
2.3
5***
1.5
7***
0.9
4**
0.4
9**
0.3
9**
-2.6
***
-2.7
4**
-4.6
***
-4.4
***
-3.0
2**
28
-1.7
***
4.0
2**
29
-1.2
***
-0.6
6
32
-1.7
***
-3.5
1
33
-1.7
***
1.7
1***
1.8
6***
2.0
2***
1.6
3***
2.2
7***
0.9
6**
-0.7
1-
2.1
50.5
1-
3.3
9*
-2.4
2-
6.0
***
-2.0
0-
3.2
0*
34
-3.3
***
2.7
2**
1.8
3**
2.0
6***
1.4
8***
0.2
4-
0.2
2-
5.1
4-
6.0
7*
2.1
9-
6.1
8*
-9.6
4**
5.4
7*
12.5
***
-7.8
0**
42
-3.5
***
0.7
7-
3.6
0**
43
-1.9
***
1.0
30.3
00.7
20.7
1*
1.0
4***
-0.7
***
1.2
9-
2.7
9*
1.9
5-
6.9
***
2.5
4-
4.6
***
3.2
2**
51
-0.9
***
0.3
8
Empirica
123
Ta
ble
1co
nti
nu
ed
Lag
son
rela
tive
import
pri
ceL
ags
on
nom
inal
exch
ange
rate
01
23
45
70
12
34
56
7
52
-0.9
***
2.5
8***
2.0
3***
1.5
7***
1.3
2***
0.9
3***
0.2
9**
0.3
2-
2.5
***
-1.7
***
-2.1
***
-0.5
4-
1.9
***
-1.2
1**
-1.4
***
53
-1.2
***
0.3
5
54
-0.9
***
0.4
4
55
-0.0
7-
1.6
***
-1.8
***
-1.6
***
-1.7
***
-1.2
***
0.3
31.5
7***
0.6
80.1
62.1
9***
-0.9
4*
0.2
10.8
3*
56
-1.6
***
0.6
40.3
91.0
2**
0.3
70.5
8**
-1.4
6-
1.5
81.9
3*
57
-0.9
***
-0.7
7
58
-0.9
***
0.2
8
59
-0.5
***
-0.0
6
61
-1.0
***
1.7
0***
1.3
9***
0.9
4***
0.7
8***
0.6
1***
1.4
8
62
-0.7
***
0.3
9***
0.2
5*
-0.0
9-
0.4
***
-0.3
***
0.1
7
63
-1.1
***
4.0
1***
3.5
3***
2.9
1***
2.3
9***
1.5
9***
0.5
8***
-1.3
9**
0.4
9-
2.3
***
-0.6
2-
0.3
6-
0.4
00.0
5-
2.0
***
64
-0.5
***
1.0
1*
65
-1.2
***
-0.3
8
66
-1.0
***
2.2
8***
2.0
4***
1.4
2***
0.6
4**
0.4
0**
0.2
3***
1.1
3***
-2.0
***
-1.9
***
-1.5
***
-1.6
***
-0.9
0**
-0.6
1
67
-0.7
***
-0.4
6*
0.0
90.6
0**
0.2
7-
0.4
2*
0.5
7-
1.5
7**
2.0
1**
-0.1
7-
0.4
8-
1.5
8**
3.0
***
-1.0
5
68
-0.9
***
0.1
6
69
-0.7
***
-0.0
3-
0.1
80.2
60.1
40.3
7**
-0.1
6*
-0.8
00.4
30.6
10.1
6-
0.0
90.2
41.6
2***
71
-0.9
***
0.3
6-
0.0
6-
0.6
5**
-0.8
***
-0.7
***
0.9
8-
0.7
0-
0.8
0-
1.1
5-
0.0
08
-1.0
1
72
-0.9
***
0.7
3**
0.7
3***
0.4
8***
0.0
60.3
1**
-0.5
11.0
51.3
1*
-0.4
60.2
90.2
12.0
4***
73
-0.8
***
-1.2
***
-1.1
***
-1.1
***
-1.0
***
-0.6
***
-0.3
4**
2.6
8***
74
-0.6
***
1.1
1***
0.8
1***
0.6
8***
0.3
1**
0.3
4***
0.3
0-
0.7
5-
0.4
1-
1.5
***
-0.7
5*
-1.1
6**
0.2
9-
1.2
***
75
-0.8
***
-0.2
30.0
30.2
90.3
0-
0.1
7-
0.2
40.2
3-
0.2
60.6
0-
1.3
4**
-0.9
9
76
0.2
4**
0.8
20.5
9-
2.1
***
-0.0
3-
0.3
5-
3.2
***
77
-0.9
***
-0.3
4
78
-0.6
***
0.5
7-
0.0
6-
1.1
6**
-1.3
6**
-1.5
0**
Empirica
123
Ta
ble
1co
nti
nu
ed
Lag
son
rela
tive
import
pri
ceL
ags
on
nom
inal
exch
ange
rate
01
23
45
70
12
34
56
7
79
-1.1
***
5.9
0**
81
-0.8
***
-0.3
4
82
-0.6
***
-1.5
5**
83
-0.8
***
-1.8
***
84
-0.7
***
0.8
8**
1.3
5***
1.5
6***
1.6
1***
1.1
2***
-0.6
01.4
6-
0.8
9-
1.0
5-
4.2
***
-1.4
8-
1.4
33.2
9***
85
-0.7
***
-1.8
4**
87
-1.1
***
0.5
00.5
70.8
4***
0.6
6***
0.1
0-
0.4
5
88
-0.4
***
-0.3
5
89
-0.6
***
1.2
3***
1.0
4***
1.0
4***
0.8
6***
0.6
7***
0.1
70.6
7-
0.9
**
0.8
0**
-0.2
3-
0.2
7-
1.2
***
***
Sig
nifi
cant
atth
e1
%si
gnifi
cance
level
,**
at5
%,
*at
10
%
Empirica
123
Ta
ble
2L
on
g-r
un
esti
mat
es&
dia
gn
ost
icte
sts
–im
po
rtd
eman
dm
od
el(2
)
SIT
Cd
escr
ipti
on
(Tra
de
shar
es)
lnY
EG
lnP
M/P
Dln
EF
EC
Mt–
1L
MR
ES
ET
CU
SU
M
(SQ
)
Ad
jR
2
00
Liv
ean
imal
so
ther
than
anim
als
of
div
isio
n
03
(0.0
4%
)
-6
.34
-0
.03
1.0
01
.85
-0
.25*
**
4.6
40
.01
S(S
).4
4
01
Mea
tan
dm
eat
pre
par
atio
ns
(0.0
2%
)-
17
.32
**
-8
.73
**
10
.43
1.6
4-
0.1
5*
*3
.52
2.9
4S
(U)
.22
02
Dai
ryp
rod
uct
san
db
ird
s’eg
gs
(0.2
9%
)-
0.9
4*
*-
0.0
40
.28
7.2
3*
**
-0
.83*
**
5.3
80
.00
2S
(S)
.37
03
Fis
h,
cru
stac
ean
s,aq
uat
icin
ver
teb
rate
san
d
pre
par
atio
ns
ther
eof
(0.8
6%
)
0.4
9-
1.4
1-
0.1
62
.56
*-
1.2
4*
**
9.6
**
0.0
9S
(S)
.75
04
Cer
eals
and
cere
alpre
par
atio
ns
(1.9
9%
)0.0
9-
2.9
**
*1
.64
*5
.27
**
*-
0.6
5*
**
1.8
92
.04
S(S
).4
9
05
Veg
etab
les
and
fruit
(4.0
4%
)1.6
1***
-1
.8*
**
1.3
3*
**
6.3
3*
**
-3
.26*
**
9.8
**
1.1
0S
(S)
.92
06
Su
gar
s,su
gar
pre
par
atio
ns
and
ho
ney
(0.1
6%
)
-3
.88*
**
-3
.8*
**
2.6
3*
**
5.1
6*
**
-1
.41*
**
9.9
**
0.3
4S
(S)
.80
07
Co
ffee
,te
a,co
coa,
spic
es,
and
man
ufa
ctu
res
ther
eof
(0.1
3%
)
-0
.28
-0
.18
0.8
7*
**
14
.60
**
*-
0.9
3*
**
8.4
20
.03
S(S
).6
5
08
Fee
din
gst
uff
for
anim
als
(no
tin
clu
din
g
un
mil
led
cere
als)
(0.1
3%
)
-0
.75*
**
-2
.3*
**
-0
.75
**
*9
.30
**
*-
3.1
5*
**
5.6
10
.05
S(S
).8
1
09
Mis
cell
aneo
us
edib
lep
rod
uct
san
d
pre
par
atio
ns
(0.5
2%
)
1.4
6*
**
-0
.14
-0
.95
**
*6
.59
**
*-
0.7
7*
**
7.0
30
.00
4S
(S)
.33
11
Bev
erag
es(0
.02
%)
0.3
7-
1.9
**
*1
.41
**
*6
.26
**
*-
2.4
9*
**
9.4
0.0
1S
(S)
.80
12
To
bac
coan
dto
bac
com
anu
fact
ure
s(0
.38
%)
-4
.47
6.7
1-
2.4
64
.20
**
-0
.32*
**
9.9
**
1.1
2S
(S)
.62
22
Oil
-see
ds
and
ole
agin
ou
sfr
uit
s(0
.05
%)
2.6
7-
1.7
0*
-0
.00
21
.98
-0
.72*
**
9.7
**
0.5
5S
(S)
.83
23
Cru
de
Ru
bb
er(0
.13
%)
-4
.36*
*-
3.1
2*
*3
.28
**
5.2
0*
**
-0
.43*
**
3.6
80
.90
S(S
).3
4
24
Co
rkan
dw
oo
d(2
.26
)1
.65
-1
.18
**
1.5
33
.63
**
-0
.39*
**
9.2
3.4
7S
(S)
.99
26
Tex
tile
fib
ers
and
thei
rw
aste
s(n
ot
man
ufa
ctu
red
into
yar
no
rfa
bri
c)(0
.47
%)
-4
.28*
**
-1
.77
**
2.4
4*
**
2.7
5*
-0
.72*
**
5.1
50
.48
S(S
).9
1
27
Cru
de
fert
iliz
ers,
and
crude
min
eral
s
(excl
ud
ing
coal
and
pet
role
um
)(0
.49
%)
-0
.11
-1
.7*
**
0.2
5*
10
.34
**
*-
2.2
6*
**
6.4
71
.26
S(S
).8
4
28
Met
alli
fero
us
ore
san
dm
etal
scra
p(1
.26
%)
0.1
0-
1.9
**
*2
.75
**
*1
7.1
0*
**
-1
.11*
**
1.4
42
.45
S(S
).9
1
Empirica
123
Ta
ble
2co
nti
nu
ed
SIT
Cd
escr
ipti
on
(Tra
de
shar
es)
lnY
EG
lnP
M/P
Dln
EF
EC
Mt–
1L
MR
ES
ET
CU
SU
M
(SQ
)
Ad
jR
2
29
Cru
de
anim
alan
dv
eget
able
mat
eria
ls
(0.2
5%
)
0.8
4-
1.2
**
*0
.52
9.6
7*
**
-0
.90*
**
6.9
00
.60
S(S
).8
5
32
Co
al,
cok
ean
db
riq
uet
tes
(0.2
7%
)-
2.9
9-
2.1
**
*1
.01
5.7
2*
**
-0
.77*
**
5.4
86
.1*
*S
(S)
.72
33
Pet
role
um
,pet
role
um
pro
duct
san
dre
late
d
mat
eria
ls(1
8.1
9%
)
-5
.91
-4
.2*
**
8.4
0*
*5
.83
**
*-
0.7
6*
**
9.1
6.9
**
S(S
).8
0
34
Gas
,n
atu
ral
and
man
ufa
ctu
red
(14
.09
%)
5.6
0*
**
-2
.4*
**
-0
.65
10
.95
**
*-
3.0
4*
**
1.6
11
.30
S(S
).9
4
42
Fix
edv
eget
able
fats
and
oil
s,cr
ud
e,re
fin
ed
or
frac
tionat
ed(0
.03
%)
-3
.2**
*-
2.9
**
*1
.90
**
10
.96
**
*-
0.8
6*
**
3.2
50
.69
S(S
).8
6
43
Anim
alor
veg
etab
lefa
tsan
doil
s,pro
cess
ed
(0.0
3%
)
-3
.12*
**
-1
.36
1.3
41
.26
-1
.40*
*8
.80
6.8
**
S(S
).8
7
51
Org
anic
Chem
ical
s(2
.79
%)
-3
.91*
-3
.84
**
3.1
9*
*5
.67
**
*-
0.1
5*
**
2.8
10
.09
S(S
).7
5
52
Ino
rgan
icC
hem
ical
s(0
.42
%)
-4
.69*
*-
7.5
8*
*5
.96
*1
0.8
3*
**
-0
.55*
**
9.9
**
0.1
1S
(S)
.86
53
Dyei
ng,
tannin
gan
dco
lori
ng
mat
eria
ls
(0.5
0%
)
-1
.20*
**
-1
.8*
**
0.6
9*
**
12
.12
**
*-
0.7
1*
**
2.8
30
.61
S(S
).7
2
54
Med
icin
alan
dphar
mac
euti
cal
pro
duct
s
(3.8
1%
)
0.5
9*
*-
0.8
**
*0
.05
8.5
5*
**
-0
.75*
**
6.7
01
.58
S(S
).7
0
55
Ess
enti
aloil
s&
per
fum
em
ater
ials
;
po
lish
ing
&cl
ean
sin
gp
rep
arat
ion
s
(0.3
5%
)
4.2
57
.74
-6
.46
6.9
3*
**
-0
.22*
**
9.1
1.9
4S
(S)
.80
56
Fer
tili
zers
(oth
erth
anth
ose
of
gro
up
27
)
(0.6
6%
)
2.1
1*
**
-1
.8*
**
0.5
63
.19
**
-1
.24*
**
8.3
82
.61
S(S
).9
2
57
Pla
stic
sin
pri
mar
yfo
rms
(3.9
3%
)-
0.5
8-
1.3
**
*0
.55
*7
.16
**
*-
0.6
0*
**
2.8
41
.26
S(S
).4
9
58
Pla
stic
sin
no
n-p
rim
ary
form
s(0
.32
%)
-1
.76*
**
-2
.1*
**
0.7
0*
6.7
1*
**
-0
.45*
**
4.2
32
.22
S(U
).5
9
59
Ch
emic
alm
ater
ials
and
pro
duct
s(1
.21
%)
-0
.55
-0
.92
**
0.7
51
0.3
0*
**
-0
.71*
**
4.4
01
.11
S(S
).5
2
61
Lea
ther
,le
ather
man
ufa
cture
s,an
ddre
ssed
fur
skin
s(0
.21
%)
-3
.16*
**
-1
.0*
**
0.9
9*
**
9.2
9*
**
-3
.07*
**
5.4
62
.79
S(S
).9
2
Empirica
123
Ta
ble
2co
nti
nu
ed
SIT
Cd
escr
ipti
on
(Tra
de
shar
es)
lnY
EG
lnP
M/P
Dln
EF
EC
Mt–
1L
MR
ES
ET
CU
SU
M
(SQ
)
Ad
jR
2
62
Ru
bb
erm
anu
fact
ure
s(0
.53
%)
-0
.56*
*-
1.2
**
*0
.39
**
8.7
1*
**
-0
.95*
**
2.3
81
.25
S(U
).6
6
63
Co
rkan
dw
oo
dm
anu
fact
ure
s(e
xcl
ud
ing
furn
itu
re)
(0.0
9%
)
-0
.9**
*-
1.4
**
*0
.24
**
4.2
8*
*-
3.7
2*
**
9.2
0.0
06
S(S
).9
7
64
Pap
er,
pap
erb
oar
dan
dar
ticl
eso
fp
aper
pu
lp
(1.5
2%
)
-1
.56*
*-
1.1
1*
*0
.87
**
5.6
3*
**
-0
.57*
**
2.5
61
.52
S(U
).4
4
65
Tex
tile
yar
n,
fabri
cs,
mad
e-u
par
ticl
es,
and
rela
ted
pro
du
cts
(1.6
5%
)
-3
.67*
**
-2
.3*
**
1.3
7*
*7
.29
**
*-
0.5
2*
**
4.4
90
.00
2S
(S)
.55
66
No
n-m
etal
lic
min
eral
man
ufa
ctu
res
(1.1
7%
)
-0
.5**
*-
1.2
**
*0
.86
**
*6
.86
**
*-
3.0
4*
**
9.6
**
0.0
1S
(S)
.95
67
Iro
nan
dst
eel
(4.4
0%
)-
1.3
6*
**
-0
.04
0.3
82
.76
*-
1.0
8*
**
9.1
3.8
S(S
).9
2
68
No
n-f
erro
us
met
als
(1.6
1%
)-
0.6
8-
0.7
5*
0.0
85
.38
**
*-
0.5
3*
**
1.7
50
.01
S(S
).7
3
69
Man
ufa
ctu
res
of
met
als
(2.0
9%
)1
.14*
**
-0
.32
-1
.0*
**
3.6
7*
*-
1.0
4*
**
8.8
26
.3*
*S
(S)
.85
71
Po
wer
-gen
erat
ing
mac
hin
ery
and
equ
ipm
ent
(0.8
3%
)
-0
.85
-1
.2*
**
0.9
44
.04
**
-1
.19*
**
9.7
**
0.9
1S
(S)
.85
72
Mac
hin
ery
spec
iali
zed
for
par
ticu
lar
indu
stri
es(4
.33
%)
3.7
0*
**
-1
.7*
**
-1
.4*
**
8.1
5*
**
-1
.27*
**
7.5
75
.7*
*S
(S)
.86
73
Met
alw
ork
ing
mac
hin
ery
(0.6
8%
)0
.03
-0
.58
-2
.14
**
*5
.76
**
*0
.61*
**
4.5
80
.02
S(S
).8
3
74
Gen
eral
ind
ust
rial
mac
hin
ery
and
equ
ipm
ent,
and
mac
hin
ep
arts
(5.5
0%
)
-0
.43
-1
.4*
**
0.7
6*
**
4.6
0*
**
-1
.11*
**
9.7
**
0.0
3S
(S)
.88
75
Offi
cem
ach
ines
and
auto
mat
icd
ata-
pro
cess
ing
mac
hin
es(0
.75
%)
0.0
5-
0.8
**
*0
.49
1.7
4-
0.6
4*
**
9.2
3.1
8S
(S)
.75
76
Tel
ecom
munic
atio
ns
and
sound-r
ecord
ing
and
rep
rod
uci
ng
app
arat
us
(4.8
1%
)
0.8
7-
1.2
**
*2
.36
*6
.64
**
*-
0.2
8*
**
3.2
23
.12
S(S
).5
8
77
Ele
ctri
cal
mac
hin
ery
,ap
par
atu
s&
app
lian
ces
and
elec
tric
alp
arts
(2.1
5%
)
0.1
4-
0.9
**
*-
0.4
4*
**
6.6
1*
**
-0
.74*
**
3.5
40
.00
2S
(U)
.91
Empirica
123
Ta
ble
2co
nti
nu
ed
SIT
Cd
escr
ipti
on
(Tra
de
shar
es)
lnY
EG
lnP
M/P
Dln
EF
EC
Mt–
1L
MR
ES
ET
CU
SU
M
(SQ
)
Ad
jR
2
78
Ro
adv
ehic
les
(in
clu
din
gai
r-cu
shio
n
veh
icle
s)(3
.06
%)
-0
.11
-1
.9*
**
2.2
4*
**
8.5
2*
**
-0
.28*
**
7.1
30
.16
S(S
).5
1
79
Oth
ertr
ansp
ort
equ
ipm
ent
(0.0
4%
)-
2.7
7*
*-
1.1
**
*1
.76
**
11
.37
**
*-
1.0
0*
**
9.3
1.5
6S
(S)
.83
81
Pre
fab
rica
ted
bu
ild
ings;
san
itar
y,
plu
mb
ing
,
and
hea
tin
g(0
.54
%)
0.5
0-
0.4
0-
0.5
6*
5.7
8*
**
-0
.53*
**
2.4
40
.69
S(S
).6
9
82
Fu
rnit
ure
,an
dp
arts
ther
eof;
bed
din
g,
mat
tres
ses
&m
attr
ess
sup
po
rts
(0.3
2%
)
0.7
7*
*-
0.6
**
*-
0.9
**
*1
0.7
4*
**
-0
.96*
**
6.1
10
.00
9S
(S)
.79
83
Tra
vel
go
ods,
han
dbag
san
dsi
mil
ar
con
tain
ers
(0.0
2%
)
4.5
7*
**
-1
.22
**
-1
.44
**
2.5
9*
-0
.46*
**
1.1
95
.9*
*S
(S)
.62
84
Art
icle
so
fap
par
elan
dcl
oth
ing
acce
ssori
es
(0.5
1%
)
-0
.66
-1
.1*
**
0.5
0*
*6
.51
**
*-
1.8
3*
**
9.5
**
0.2
8S
(S)
.88
85
Fo
otw
ear
(0.0
1%
)-
1.0
5*
-0
.7*
**
-0
.9*
**
8.7
3*
**
-0
.76*
**
2.8
90
.11
S(S
).6
6
87
Pro
fess
ion
al,
scie
nti
fic
and
con
tro
llin
g
inst
rum
ents
and
app
arat
us
(1.3
5%
)
0.9
0*
**
-1
.4*
**
-0
.06
3.8
7*
*-
1.3
4*
**
3.3
50
.79
S(U
).9
3
88
Ph
oto
gra
ph
icap
par
atu
s,eq
uip
men
tan
d
sup
pli
esan
do
pti
cal
go
ods
(0.1
1%
)
-1
.2**
*-
0.3
**
*-
0.3
7*
*8
.71
**
*-
0.7
8*
**
2.2
20
.05
S(S
).5
8
89
Mis
cell
aneo
us
man
ufa
ctu
red
arti
cles
(1.5
7%
)
-0
.06
-2
.4*
**
0.9
7*
**
7.2
7*
**
-0
.87*
**
1.3
00
.04
S(S
).6
8
**
*S
ign
ifica
nt
atth
e1
%si
gn
ifica
nce
lev
el,
**
at5
%,
*at
10
%
Empirica
123
Egypt. The nominal exchange rate, however, carries its expectedly negative and
significant coefficient only in 11 industries. Are these long-run estimates meaning-
ful? In order to attach some importance to these estimates, we must establish joint
significance of lagged level variables or co integration in each model. The F test
results help us to achieve this goal. From the F test results, it is clear that in every
model that at least one of the variables were significant, our calculated F statistic is
greater than its critical value tabulated by (Pesaran et al. 2001, Table CI, Case III,
p. 300). The exceptions are very rare.
In addition to the F test results reported in Table 2, a few other diagnostic
statistics are also reported. The first concerns about whether the adjustment of
variables in each model is toward long-run equilibrium values. To test this
hypothesis we follow Pesaran et al. (2001) and use the long-run normalized
coefficient estimates and long-run import demand model (1) and calculate the error
term, normally labeled as error-correction term denoted by ECM. We then replace
the lagged level variables in error-correction model (2) by ECMt-1 and estimate this
new specification at the same optimum lags. A significantly negative coefficient
obtained for ECMt-1 will be an indication of adjustment toward long-run
equilibrium which is the case in almost every optimum model.7 Next we report
the Lagrange Multiplies (LM) statistic to determine whether residuals in each
optimum model are autocorrelation free. The LM statistic is distributed as v2 with
four degrees of freedom. Given its 5 % critical value of 9.48, most models pass this
test, implying absence of autocorrelation. We have also reported Ramsey’s RESET
test to make sure the estimated optimum models are not miss-specified. This statistic
is also distributed as v2 but with one degree of freedom only. Given its critical value
of 3.84 at the usual 5 % significance level, clearly most models pass this test too,
implying correctly specified optimum error-correction models. Furthermore, we
apply Brown et al.’s (1975) CUSUM and CUSUMSQ tests to the residuals of each
optimum model to establish stability of short-run and long-run coefficient estimates.
Stable coefficients are denoted by ‘‘S’’ and unstable ones by ‘‘U’’. As can be seen,
almost all estimated coefficients are stable. Although it is a common practice to
report plot of these two statistics for each industry, due to volume of the results, we
indicate stable coefficients by ‘‘S’’ and unstable ones by ‘‘U’’. As can be seen from
the results, both statistics do support stability of the estimated coefficients in a
majority of the models.8 Finally, adjusted R2 is also reported and clearly indicates
that most models enjoy a good fit.
Next we consider the estimates of Egypt’s exports. Again, we use the same
procedure of selecting optimum lag length and report the short-run coefficients
estimates in Table 3 and long-run estimates along with diagnostic statistics in Table 4.
From Table 3 we gather that lags are shorter on the nominal exchange rate as
compared to the relative prices in 21 industries coded 06, 07, 08, 09, 24, 27, 33, 43, 51,
7 Note that the size of the coefficient itself measures the speed of adjustment. For example, a coefficient
of -0.15 in industry 01 (Meat and meat preparations) implies that 15 % of adjustment takes place within
one quarter since the data are quarterly. However, a coefficient of -3.15 in industry coded 08 (Feeding
stuff for animals) implies that adjustment is very fast and almost 100 % of the adjustment takes place
within 1 month (1/3rd of a quarter).8 For a graphical presentation of these tests see Bahmani-Oskooee et al. (2005).
Empirica
123
Tab
le3
Short
-run
esti
mat
es–
export
dem
and
model
(4)
Lag
length
on
rela
tive
export
pri
ceL
agle
ngth
on
nom
inal
exch
ange
rate
01
23
45
67
01
23
45
67
00
-0.8
***
1.8
9
01
-0.9
***
-0.5
***
2.7
45.9
***
02
0.0
08
-1.1
***
-1.2
***
-1.1
***
-0.8
***
-0.6
***
-0.9
98.7
8**
3.6
83.0
28.3
8**
6.9
8**
9.1
2**
03
-0.8
***
0.1
70.2
2-
0.2
93.4
8***
1.3
6-
1.2
0-
4.6
***
-3.1
5**
04
-2.4
***
3.8
9**
05
-1.4
***
0.6
2-
1.4
**
06
-3.1
***
1.6
7**
0.6
90.9
4*
0.8
7*
1.0
0***
1.1
9***
0.4
0***
5.5
3***
-2.2
2-
3.8
5*
-5.0
5**
07
-0.2
81.0
5***
0.5
8**
0.3
7-
1.0
8
08
-1.1
***
3.7
2***
2.1
0***
1.2
9***
0.4
40.2
20.7
3**
-9.3
8**
-23.6
**
-25.4
**
-3.0
4-
9.9
4**
09
-0.5
6**
-1.1
***
-0.5
7-
0.8
1**
-0.9
***
-1.4
***
-0.6
7**
3.9
9*
-4.3
7*
11
-0.5
2**
0.0
5
12
-0.5
***
0.6
2*
0.0
10.8
3*
0.2
10.7
2*
0.6
10.5
8*
2.4
82.6
2-
0.9
111.3
9*
-4.2
43.4
2-
4.7
83.6
0
22
-0.5
6*
1.0
1
23
-.0
4***
-0.2
5
24
-0.3
43.4
2***
3.1
0***
2.6
3***
2.1
8***
1.5
0***
0.6
9*
0.6
8**
3.6
9*
-2.1
9-
4.0
7**
-3.3
5*
-7.3
***
-3.9
6*
26
1.0
5***
1.2
1
27
-0.6
***
0.5
4**
0.3
6*
0.6
6***
0.3
6**
0.1
04
0.2
4**
0.3
4***
1.9
0***
-1.0
6-
1.6
3**
-1.5
5**
28
0.1
6-
0.6
0
29
-0.0
7-
1.9
***
-1.4
***
-0.9
***
-0.9
***
-0.3
4*
0.2
20.2
11.0
8**
1.1
0**
2.0
2***
1.0
1**
0.1
90.0
31.2
6**
2.5
4***
32
-0.0
23.9
5
33
-0.0
10.2
6***
0.3
7***
0.0
8-
0.1
20.2
0**
0.3
1***
0.1
8*
2.2
1***
-1.3
***
34
0.3
9-
0.6
3
42
-0.4
8**
9.0
1**
43
-0.9
***
0.2
8*
-0.0
90.1
6-
0.1
30.2
1*
0.1
8*
3.9
0**
51
-1.0
***
0.4
8*
0.4
4*
0.6
6**
1.1
4***
1.3
4***
0.9
0***
0.5
0**
6.2
210.2
***
-5.1
1
Empirica
123
Ta
ble
3co
nti
nu
ed
Lag
length
on
rela
tive
export
pri
ceL
agle
ngth
on
nom
inal
exch
ange
rate
01
23
45
67
01
23
45
67
52
-0.7
***
3.2
7**
53
-1.0
***
0.5
4***
0.2
6*
-2.4
5
54
-0.7
***
0.1
4
55
-0.9
***
-0.2
0-
0.1
3-
0.3
2*
0.0
8***
0.4
1**
3.0
9***
2.8
6***
1.2
9-
1.4
00.7
60.1
6-
2.5
***
56
-1.5
***
2.5
7***
2.0
6***
2.0
6***
8.1
6***
-9.1
***
10.4
***
-7.3
8**
-3.6
0-
2.6
8-
10.8
**
57
-1.4
7**
-3.6
8**
-2.6
6*
-2.4
7*
-0.3
4-
1.9
5-
2.5
4**
-3.1
***
9.2
5**
0.4
67.7
8**
2.5
6-
5.2
94.1
87.2
313.3
***
58
-2.2
***
3.7
8***
1.9
2**
1.8
5***
1.1
0*
1.3
4**
1.1
2**
1.2
7***
1.2
5-
7.8
1-
10.1
3*
59
-0.1
70.2
2
61
-0.9
***
2.0
6**
62
-0.8
***
0.5
9
63
-0.9
***
1.1
0**
0.6
41.0
3**
0.6
2*
0.6
7**
0.8
5***
-4.1
5-
0.0
9-
0.7
14.4
4*
-5.7
8**
-0.0
8-
9.4
***
64
-1.1
***
0.9
3
65
-0.4
7*
0.2
5
66
-0.7
***
0.2
7*
0.2
2**
0.3
1***
4.5
5***
67
-0.7
4**
1.6
1
68
-0.5
***
-2.2
***
-1.6
***
-1.1
***
-0.9
***
-0.6
***
-0.1
50.3
1*
0.4
41.3
5*
1.5
4*
0.4
72.8
0***
69
-1.2
***
1.9
3***
1.5
3***
0.9
9***
0.4
9**
0.3
6**
0.3
5**
3.3
3**
-5.5
***
-4.3
***
-3.6
***
-6.8
***
-3.9
***
-5.6
***
71
-0.7
***
12.7
9**
72
-0.9
***
0.1
4-
0.4
***
0.1
2-
0.4
***
-0.0
6-
0.1
30.1
8*
1.4
22.0
1-
0.2
08.8
4**
-0.3
28.3
6**
73
-0.7
***
0.6
3*
0.8
7**
0.7
7**
0.7
7***
0.2
70.0
2-
0.2
1-
9.2
0*
-0.3
6-
6.6
8-
11.4
**
74
-1.0
***
-0.8
***
-0.5
2**
-0.4
7**
-0.3
2*
-0.4
3**
-0.1
30.9
1-
2.1
72.4
72.3
9
75
-0.9
***
3.5
7***
3.0
0***
2.2
9***
1.4
2***
1.0
6***
0.7
6**
-0.2
50.4
7-
1.8
4-
2.8
99.6
5***
76
-0.9
***
-9.4
6*
77
-1.1
***
0.9
0***
0.5
2***
0.2
9***
-1.7
2-
3.7
6*
0.3
62.3
1-
4.7
8**
-6.1
***
78
0.1
02.4
0***
2.1
0***
1.9
9***
1.7
8***
1.2
3***
0.7
2**
0.3
0-
3.0
2-
4.2
4-
4.0
40.9
9-
7.4
7**
Empirica
123
Ta
ble
3co
nti
nu
ed
Lag
length
on
rela
tive
export
pri
ceL
agle
ngth
on
nom
inal
exch
ange
rate
01
23
45
67
01
23
45
67
79
-0.9
***
-3.1
2
81
-0.8
***
0.6
1
82
0.4
1***
-2.6
***
-2.7
***
-2.2
***
-1.5
***
-1.2
***
-1.2
***
-0.9
***
-0.1
30.8
1*
0.8
0*
-0.3
01.0
1**
1.6
2***
83
-0.3
8*
0.0
30.3
3-
1.4
9
84
-0.9
***
0.1
2
85
-0.8
***
3.4
7
87
-0.7
***
1.3
7***
1.0
3***
1.1
4***
0.6
2**
0.1
2-
1.6
2
88
-0.7
***
-2.9
40.8
411.7
1**
-18.8
**
1.7
0-
19.9
**
89
-0.9
***
1.4
2***
1.4
2***
0.8
5**
0.7
0**
-0.2
0.1
1-
0.4
6**
1.9
6-
9.6
***
11.1
***
-6.3
6**
-0.5
52.0
0-
1.9
76.2
9***
***
Sig
nifi
cant
atth
e1
%si
gnifi
cance
level
,**
at5
%,
*at
10
%
Empirica
123
Tab
le4
Lo
ng
-ru
nes
tim
ates
&d
iagn
ost
icte
sts
–ex
po
rtd
eman
dm
od
el(4
)
SIT
Cd
escr
ipti
on
lnY
EU
lnP
X/P
EU
lnE
FE
CM
t–1
LM
RE
SE
TC
US
UM
(SQ
)
Ad
jR
2
00
Liv
ean
imal
so
ther
than
anim
als
of
div
isio
n
03
-6
.2**
*-
0.8
**
*1
.45*
**
8.9
6*
**
-0
.94*
**
4.7
11
.09
S(S
).8
4
01
Mea
tan
dm
eat
pre
par
atio
ns
2.9
8-
0.6
11
.33*
3.4
5*
*-
0.6
8*
**
9.4
1.1
4S
(S)
.84
02
Dai
ryp
rod
uct
san
db
ird
s’eg
gs
12
.29
**
0.4
5-
1.3
46
.10*
**
-0
.69*
**
9.6
**
0.6
9S
(S)
.76
03
Fis
h,
crust
acea
ns,
aquat
icin
ver
tebra
tes
and
pre
par
atio
ns
ther
eof
-4
.95*
*-
0.7
92
.46*
**
7.1
9*
**
-0
.68*
**
6.1
70
.10
S(S
).6
6
04
Cer
eals
and
cere
alpre
par
atio
ns
3.4
7**
-2
.2*
**
3.5
2*
**
4.7
7*
**
-1
.02*
**
5.7
00
.53
S(S
).5
5
05
Veg
etab
les
and
fruit
2.6
2***
-1
.2*
**
1.9
9*
**
20
.6*
**
-1
.23*
**
7.3
80
.12
S(S
).9
9
06
Su
gar
s,su
gar
pre
par
atio
ns
and
ho
ney
-6
.39
-5
.4*
**
6.3
6*
**
11
.1*
**
-1
.09*
**
2.8
80
.00
3S
(S)
.96
07
Co
ffee
,te
a,co
coa,
spic
es,
and
man
ufa
ctu
res
ther
eof
1.4
4*
*-
1.4
**
*1
.46*
**
12
.5*
**
-1
.11*
**
9.3
5.1
7*
*S
(S)
.72
08
Fee
din
gst
uff
for
anim
als
(no
tin
clu
din
g
un
mil
led
cere
als)
-1
.62
-1
.3*
**
0.1
31
2.4
**
*-
4.0
5*
**
4.6
51
.41
S(S
).8
7
09
Mis
cell
aneo
us
edib
lep
rod
uct
san
d
pre
par
atio
ns
0.6
00
.57
2.3
8*
*2
.16
-0
.58*
**
5.3
51
.49
S(S
).7
4
11
Bev
erag
es10.8
***
-0
.51
**
3.6
8*
**
10
.7*
**
-0
.91*
**
2.3
53
.59
S(S
).4
7
12
To
bac
coan
dto
bac
com
anu
fact
ure
s1
4.2
**
*-
0.1
6-
0.1
51
.63
-2
.39*
*9
.20
.53
S(S
).8
7
22
Oil
-see
ds
and
ole
agin
ous
fruit
s-
2.2
6*
-0
.32
0.4
84
.93*
**
-0
.72*
**
2.5
10
.50
S(S
).7
9
23
Cru
de
Ru
bb
er9
.87*
*-
0.6
**
*0
.82
3.1
8*
*-
0.2
5*
**
9.3
44
.82
**
S(U
).3
5
24
Co
rkan
dw
oo
d0
.16
-2
.09
*1
.86*
**
5.4
1*
**
-1
.98*
**
3.2
30
.91
S(S
).5
9
26
Tex
tile
fib
ers
and
thei
rw
aste
s(n
ot
man
ufa
ctu
red
into
yar
no
rfa
bri
c)
0.7
71
.09
**
*-
1.1
3*
**
9.9
0*
**
-0
.77*
**
1.4
21
.45
S(S
).5
5
27
Cru
de
fert
iliz
ers,
and
cru
de
min
eral
s
(excl
ud
ing
coal
and
pet
role
um
)
2.1
4*
**
-1
.1*
**
1.0
2*
**
4.8
**
*-
1.3
1*
**
17
.3*
*1
1.1
**
S(S
).9
2
28
Met
alli
fero
us
ore
san
dm
etal
scra
p2.9
6-
0.4
0-
0.4
85
.69*
**
-0
.61*
**
5.9
90
.03
S(S
).3
1
Empirica
123
Tab
le4
con
tin
ued
SIT
Cd
escr
ipti
on
lnY
EU
lnP
X/P
EU
lnE
FE
CM
t–1
LM
RE
SE
TC
US
UM
(SQ
)
Ad
jR
2
29
Cru
de
anim
alan
dveg
etab
lem
ater
ials
1.1
6***
0.9
2**
-0
.05
8.8
3*
**
-2
.96*
**
20
.9*
*9
.5*
**
S(S
).9
0
32
Co
al,
cok
ean
db
riq
uet
tes
3.5
0-
0.0
21
.11
11
.3*
**
-0
.94*
**
3.8
11
.77
S(S
).4
5
33
Pet
role
um
,pet
role
um
pro
duct
san
dre
late
d
mat
eria
ls
-2
.2**
*-
0.1
31
.01*
**
10
.2*
**
-0
.94*
**
3.6
09
.1*
**
S(S
).7
9
34
Gas
,n
atura
lan
dm
anu
fact
ure
d4
.53
-1
.69
4.2
9*
**
3.0
9*
*-
0.2
7*
**
3.4
88
.8*
**
S(S
).1
5
42
Fix
edv
eget
able
fats
and
oil
s,cr
ud
e,re
fin
ed
or
frac
tio
nat
ed
-3
.04
-0
.72
*4
.62*
**
8.7
1*
**
-0
.82*
**
0.8
21
.00
S(S
).4
6
43
An
imal
or
veg
etab
lefa
tsan
do
ils,
pro
cess
ed6
.36
-0
.51
2.1
73
.01*
*-
0.3
2*
*2
9.6
**
0.3
6S
(S)
.96
51
Org
anic
Chem
ical
s-
1.5
7-
1.2
**
*3
.70*
**
38
.7*
**
-1
.26*
**
9.7
**
6.5
4*
*S
(S)
.92
52
Ino
rgan
icC
hem
ical
s1
.74
-1
.74
-1
.28
2.6
4*
*-
0.2
0*
*4
.66
0.4
0S
(S)
.57
53
Dy
ein
g,
tan
nin
gan
dco
lori
ng
mat
eria
ls0
.23
-1
.4*
**
0.7
61
7.4
**
*-
1.2
9*
**
3.9
42
.95
S(S
).7
4
54
Med
icin
alan
dphar
mac
euti
cal
pro
duct
s-
2.9
0*
*-
0.7
**
*0
.36
12
.5*
**
-1
.04*
**
6.0
80
.00
4S
(S)
.61
55
Ess
enti
aloil
s&
per
fum
em
ater
ials
;
po
lish
ing
&cl
ean
sin
gp
rep
arat
ion
s
2.2
8-
0.4
3*
1.0
8*
**
10
.6*
**
-1
.42*
**
3.0
90
.19
S(S
).9
4
56
Fer
tili
zers
(oth
erth
anth
ose
of
gro
up
27
)1
1.3
**
*-
4.0
**
*3
.17*
**
22
.9*
**
-1
.32*
**
6.6
70
.00
4S
(S)
.78
57
Pla
stic
sin
pri
mar
yfo
rms
23
.15
4.7
1-
2.3
21
.39
-0
.80*
*6
.26
8.2
**
*S
(S)
.82
58
Pla
stic
sin
no
n-p
rim
ary
form
s1
2.5
**
*-
2.4
**
*-
0.2
16
.71*
**
-2
.50*
**
10
.9*
*0
.15
S(S
).7
8
59
Ch
emic
alm
ater
ials
and
pro
duct
s-
3.6
4*
*-
0.3
80
.53
15
.1*
**
-0
.84*
**
9.7
**
9.5
**
*S
(S)
.58
61
Lea
ther
,le
ather
man
ufa
cture
s,an
ddre
ssed
furs
kin
s
6.8
4*
**
-1
.0*
**
0.1
69
.67*
**
-0
.77*
**
4.0
20
.50
S(S
).9
6
62
Ru
bb
erm
anu
fact
ure
s-
3.5
7-
0.6
**
*0
.08
4.5
6*
**
-0
.46*
**
3.9
90
.01
S(U
).7
4
63
Co
rkan
dw
oo
dm
anu
fact
ure
s(e
xcl
ud
ing
furn
itu
re)
-0
.52
-0
.5*
**
0.0
85
.24*
**
-2
.33*
**
3.1
96
.7*
**
S(S
).8
9
64
Pap
er,
pap
erb
oar
dan
dar
ticl
eso
fp
aper
pu
lp-
5.6
3-
2.0
**
*3
.69*
**
3.4
2*
*-
0.3
7*
**
2.7
20
.31
S(U
).2
2
Empirica
123
Tab
le4
con
tin
ued
SIT
Cd
escr
ipti
on
lnY
EU
lnP
X/P
EU
lnE
FE
CM
t–1
LM
RE
SE
TC
US
UM
(SQ
)
Ad
jR
2
65
Tex
tile
yar
n,
fab
rics
,m
ade-
up
arti
cles
,an
d
rela
ted
pro
duct
s
-2
.6**
*-
0.8
2*
*0
.45*
**
3.4
3*
*-
0.5
6*
**
9.9
**
8.2
**
*S
(U)
.70
66
No
n-m
etal
lic
min
eral
man
ufa
ctu
res
-3
.13
-2
.1*
**
-0
.28
4.8
5*
**
-0
.45*
**
4.1
22
.19
S(S
).7
2
67
Iro
nan
dst
eel
4.9
8*
**
-0
.68
**
1.0
3*
*8
.65*
**
-0
.75*
**
4.1
90
.19
S(S
).3
8
68
No
n-f
erro
us
met
als
-1
.31*
*1
.47
**
-1
.21*
**
19
.9*
**
-1
.30*
**
6.3
10
.52
S(S
).7
7
69
Man
ufa
ctu
res
of
met
als
2.7
5*
*-
1.4
**
*3
.14*
**
14
.4*
**
-2
.34*
**
8.6
80
.97
S(S
).9
1
71
Po
wer
-gen
erat
ing
mac
hin
ery
and
equ
ipm
ent
2.7
2-
0.8
**
*1
.69
16
.2*
**
-1
.06*
**
3.2
20
.91
S(S
).5
9
72
Mac
hin
ery
spec
iali
zed
for
par
ticu
lar
indu
stri
es
1.3
5-
0.5
0-
0.8
58
.12*
**
-1
.07*
**
6.7
20
.03
S(S
).9
1
73
Met
alw
ork
ing
mac
hin
ery
12
.5*
**
-1
.3*
**
2.0
75
.50*
**
-1
.27*
**
9.1
1.0
7S
(S)
.76
74
Gen
eral
indust
rial
mac
hin
ery
and
equ
ipm
ent,
and
mac
hin
ep
arts
3.2
0-
0.2
7-
0.8
1*
*7
.42*
**
-0
.82*
**
9.4
1.4
2S
(S)
.66
75
Offi
cem
achin
esan
dau
tom
atic
dat
a-
pro
cess
ing
mac
hin
es
17
.1*
**
-2
.5*
**
-3
.9**
*7
.85*
**
-1
.79*
**
4.4
30
.80
S(S
).7
4
76
Tel
ecom
munic
atio
ns
and
sound-r
ecord
ing
and
repro
du
cin
gap
par
atu
s
1.0
2-
1.1
**
*0
.21
11
.8*
**
-0
.98*
**
4.1
41
.37
S(S
).7
0
77
Ele
ctri
cal
mac
hin
ery
,ap
par
atus
&
app
lian
ces
and
elec
tric
alp
arts
1.4
3-
1.1
**
*0
.18
16
.3*
**
-1
.97*
**
5.7
31
.12
S(S
).9
2
78
Ro
adv
ehic
les
(in
clu
din
gai
r-cu
shio
n
veh
icle
s)
10
.6*
**
-1
.05
**
0.0
77
.29*
**
-2
.15*
**
9.9
**
0.3
8S
(S)
.57
79
Oth
ertr
ansp
ort
equ
ipm
ent
4.6
6-
1.3
**
*0
.74
6.9
7*
**
-0
.67*
**
3.4
50
.00
5S
(U)
.63
81
Pre
fab
rica
ted
bu
ild
ing
s;sa
nit
ary
,p
lum
bin
g,
and
hea
tin
g
9.8
0*
**
-1
.2*
**
0.0
39
.44*
**
-0
.78*
**
1.3
31
.67
S(S
).6
3
82
Fu
rnit
ure
,an
dp
arts
ther
eof;
bed
din
g,
mat
tres
ses
&m
attr
ess
support
s
3.3
2*
**
0.7
2*
**
-0
.41
0.8
**
*-
4.3
2*
**
9.8
**
0.7
2S
(S)
.91
Empirica
123
Tab
le4
con
tin
ued
SIT
Cd
escr
ipti
on
lnY
EU
lnP
X/P
EU
lnE
FE
CM
t–1
LM
RE
SE
TC
US
UM
(SQ
)
Ad
jR
2
83
Tra
vel
go
od
s,h
and
bag
san
dsi
mil
ar
con
tain
ers
3.7
3-
1.3
90
.11
2.8
3*
*-
0.4
8*
**
1.7
60
.02
S(S
).5
0
84
Art
icle
sof
appar
elan
dcl
oth
ing
acce
ssori
es-
3.1
2*
-0
.9*
**
0.4
55
.44*
**
-0
.53*
**
2.9
80
.09
S(S
).9
2
85
Fo
otw
ear
-1
3.7
**
-1
.2*
**
2.9
7*
**
8.4
6*
**
-0
.78*
**
6.1
52
.39
S(U
).8
3
87
Pro
fess
ional
,sc
ienti
fic
and
contr
oll
ing
inst
rum
ents
and
app
arat
us
9.5
0*
**
-2
.2*
**
1.8
2*
**
13
.9*
**
-1
.21*
**
0.4
31
.25
S(S
).6
8
88
Ph
oto
gra
ph
icap
par
atus,
equ
ipm
ent
and
sup
pli
esan
do
pti
cal
go
od
s
-8
.6**
*-
0.4
**
*1
.09*
**
8.4
7*
**
-1
.73*
**
9.3
0.2
0S
(S)
.77
89
Mis
cell
aneo
us
man
ufa
ctu
red
arti
cles
2.3
6*
-0
.9*
**
-1
.6**
*4
.10*
*-
2.1
9*
**
4.2
44
.81
**
S(S
).9
4
**
*S
ign
ifica
nt
atth
e1
%si
gn
ifica
nce
lev
el,
**
at5
%,
*at
10
%
Empirica
123
53, 58, 66, 68, 72, 73, 74, 75, 78, 82, 83, and 87, supporting Orcutt’s hypothesis. The
largest industries in the list happen to be 24 (Cork and wood with 2.26 % trade share);
72 (Machinery specialized for particular industries with 4.33 % trade share); 74
(General industrial machinery with 5.5 % trade share); and 78 (Road vehicles with
3.06 % trade share). On the other hand there are seven industries in which the opposite
is true. In these industries (coded as 02, 03, 05, 55, 56, 77, and 88) lags are shorter on
the relative price term than the exchange rate. In the remaining 31 industries both
variables carry the same number of lags. Therefore, in both Egypt’s imports and
exports Orcutt’s hypothesis receives support in almost 1/3rd of industries.
As for the long-run estimates that are reported in Table 4, European income
carries significant coefficient in 32 cases. In 21 industries the coefficient is
significantly positive implying that as Europe grows, it imports more of these
commodities from Egypt. On the other hand, in 11 industries the coefficient is
negative. These are industries in which Europe is probably following an import-
substitution policy. The relative price of exports carries its expected negative and
significant coefficient in 38 industries and unlike the import demand case, the
exchange rate carries its expected positive and significant coefficient in 24
industries. These long-run coefficient estimates are meaningful because the F
statistic is significant in almost all models. Adjustment of variables in each optimum
model seems to be toward the long-run because the ECMt-1 carries significantly
negative coefficient in every model. Once again, in most models the residuals seem
to be autocorrelation free and most models are correctly specified. Furthermore,
majority of the estimated coefficients are stable and optimum models enjoy a good
fit reflected by the size of adjusted R2.
4 Summary and conclusion
A body of the literature in international finance is concerned with the relative
responsiveness of trade flows to changes in relative prices versus changes in the
exchange rate. Indeed, Orcutt (1950) conjectured that trade flows should respond to
exchange rate changes faster than to relative price changes. The limited number of
previous studies that tried to test Orcutt’s hypothesis used trade flows of one country
with the rest of the world and did not find strong support for the hypothesis.
Suspecting that these studies suffer from aggregation bias, following the J-Curve
literature we thought to disaggregate the trade data by country and test the
hypothesis at bilateral level. This was not possible because no price data are
available at bilateral level for total imports and exports between two countries. The
third rout, again following the J-Curve literature is to concentrate on trade flows
between two countries but disaggregate their trade flows by commodity. However,
commodity price data between two countries are rarely available.
We have come across commodity prices between Europe and Egypt and try to
test Orcutt’s hypothesis at commodity level. There are 59 industries that engage in
100 % of the trade between Egypt and Eurozone. Indeed, disaggregation by
commodity was originally highly recommended by Orcutt (1950, p. 125–126) who
argued that some commodities (e.g. agriculture products) could experience a wide
Empirica
123
fluctuation in their prices and provide better opportunities to test his hypothesis.
Using bounds testing approach to cointegration and error-correction modeling that
distinguishes short-run from the long-run, we find support for Orcutt’s hypothsis in
1/3rd of the industries. In these industries imports and exports reacted to exchange
rate changes faster than relative price changes.
Acknowledgements Valuable comments of an anonymous referee are greatly appreciated. Remaining
errors, however, are authors own responsibility. The views expressed in this paper are those of the authors
and should not be attributed to the University of Wisconsin-Milwaukee and to the IMF, its Executive
Board, or its management.
Appendix
Data definition and sources
Quarterly data over the period 1994Q1-2007Q4 are used to carry out the empirical
analysis. The data sources are as follows:
a. Central Agency for Public Mobilization and Statistics (CAPMAS), Arab
Republic of Egypt.
b. EuroStat Online Database.
c. Ministry of Economic Development, Arab Republic of Egypt.
d. International Financial Statistics IMF (CD-ROM).
Variables
Mi = For each commodity i, M is the volume of Egyptian imports from the
European Union. It is defined as the ratio of the value of Egyptian imports from
the European Union (EU) over the respective import price of commodity i. The
imports data and import prices data for all 59 industries come from source a.
Xi = For each commodity i, X is volume of Egyptian exports to the European
Union. It is defined as the ratio of Egyptian exports to the European Union over
the respective export price of commodity i. For all 59 industries both the export
values and the export prices come from source a.
YEU = EU real GDP. The data come from source b.
YEG = Egyptian real GDP. The data come from source c.
PMi = For each commodity i, PM is import price of commodity i, source a.
PD = domestic price level in Egypt. CPI data (used as a proxy for PD) come
from source d.
PXi = For each commodity i, PX is defined as export price of commodity i,
source a.
PEU = the price level in US. CPI data (used as a proxy for PEU) come from
source d.
E = Nominal bilateral exchange rate defined as number of Egyptian pounds per
Euro. Thus, an increase in E reflects a depreciation of the Egyptian pound, and the
data come from source b.
Empirica
123
References
Agbola FW, Damoense MY (2005) Time-series estimation of import demand functions for pulses in
India. J Econ Stud 32:146–157
Alse J, Bahmani-Oskooee Mohsen (1995) Do devaluations improve or worsen the terms of trade? J Econ
Stud 22:16–25
Bahmani-Oskooee M (1986) Determinants of international trade flows: the case of developing countries.
J Dev Econ 20:107–123
Bahmani-Oskooee M, Gelan A (2009) How stable is the demand for money in African countries. J Econ
Stud 36:216–235
Bahmani-Oskooee M, Hegerty SW (2007) Exchange rate volatility and trade flows: a review article.
J Econ Stud 34:211–255
Bahmani-Oskooee M, Kara O (2003) Relative responsiveness of trade flows to a change in prices and
exchange rate. Intern Rev Appl Econ 17:293–308
Bahmani-Oskooee M, Niroomand N (1998) Long-run price elasticities and the marshall-lerner condition
revisited. Econ Lett 61:101–109
Bahmani-Oskooee M, Economidou C, Goswami GG (2005) How sensitive are Britain’s in payments and
out payments to the value of the British pound. J Econ Stud 32:455–467
Brown RL, Durbin J, Evans JM (1975) Techniques for testing the constancy of regression relations over
time. J R Stat Soc B 37:149–192
Caporale GM, Chui MKF (1999) Estimating income and price elasticities of trade in a co integrating
framework. Rev Int Econ 7(2):254–264
Charos EN, Simos EO, Thompson AR (1996) Exports and industrial growth: a new framework and
evidence. J Econ Stud 23:18–31
De Vita G, Kyaw KS (2008) Determinants of capital flows to developing countries: a structural VAR
analysis. J Econ Stud 35:304–322
Du Hongwei, Zhu Zhen (2001) The effect of exchange-rate risk on exports: some additional empirical
evidence. J Econ Stud 28:106–121
Engle RF, Granger CWJ (1987) Co integration and error correction: representation, estimation, and
testing. Econometrica 55(2):251–276
Halicioglu F (2007) The J-Curve dynamics of Turkish bilateral trade: a co integration approach. J Econ
Stud 34:103–119
Houthakker HS, Magee SP (1969) Income and price elasticities in world trade. Rev Econ Stat
51(2):1111–1125
Junz H, Rhomberg RR (1973) Price competitiveness in export trade among industrial countries. Am Econ
Rev Pap Proc 63:412–418
King Alan (1993) The functional form of import demand: the Case of UK motor vehicle imports,
1980–90. J Econ Stud 20:36–50
Love J, Chandra R (2005) Testing export-led growth in South Asia. J Econ Stud 32:132–145
Marquez J, McNeilly Caryl (1988) Income and price elasticities for export of developing countries. Rev
Econ Stat 70(2):306–314
Mohammadi H, Cak M, Cak D (2008) Wagner’s Hypothesis: new evidence from Turkey using the bounds
testing approach. J Econ Stud 35:94–106
Narayan S, Narayan PK (2005) An empirical analysis of Fiji’s import demand function. J Econ Stud
32:158–168
Narayan PK, Narayan S, Prasad BC, Prasad A (2007) Export-led growth hypothesis: evidence from Papua
New Guinea and Fiji. J Econ Stud 34:341–351
Orcutt GH (1950) Measurement of price elasticities in international trade. Rev Econ Stat 32:117–132
Payne JE (2008) Inflation and inflation uncertainty: evidence from the Caribbean region. J Econ Stud
35:501–511
Pesaran MH, Shin Y, Smith RJ (2001) Bound testing approaches to the analysis of level relationships.
J Appl Econ 16:289–326
Rose AK, Yellen JL (1989) Is There a J-curve? J Monetary Econ 24:53–68
Tang TC (2007) Money demand function for Southeast Asian countries: an empirical view from
expenditure components. J Econ Stud 34:476–496
Tegene A (1989) On the effects of relative prices and effective exchange rates on trade flows of LDCs.
Appl Econ 21:1447–1463
Empirica
123
Tegene A (1991) Trade flows, relative prices, and effective exchange rates: a VAR on Ethiopian data.
Appl Econ 23:1369–1375
Truett Lila J, Truett Dale B (2000) The Demand for imports and economic reform in Spain. J Econ Stud
27:182–199
Warner D, Kreinin ME (1983) Determinants of international trade flows. Rev Econ Stat 65(1):96–104
Wilson JF, Takacs WE (1979) Differential responses to price and exchange rate influences in the foreign
trade of selected industrial countries. Rev Econ Stat 61(2):267–279
Wong KN, Tang TC (2008) The effects of exchange rate variablity on Malaysia’s disaggregated electrical
exports. J Econ Stud 35:154–169
Empirica
123
