Estimating an Import Demand Systems using the
Generalized Maximum Entropy Method
Santosh R. Joshi∗,1,2, Kevin Hanrahan2, Eithne Murphy1, and
Hugh Kelley1
1Department of Economics, National University of Ireland, Galway.
2Rural Economy Research Center, TEAGASC, Athenry
Abstract
The Generalized Maximum Entropy (GME) method is used to esti-
mate import demand systems with many equations. The GME method
allows for estimation of models, which due to paucity of data, are ill-
posed (when the number of unknown parameters exceeds the number of
data points) and/or ill-conditioned (where, for example, there are linear
dependencies among the set of explanatory variables used in estimation).
Such problems are common when estimating the parameters of trade mod-
els. This paper uses the GME method to estimate the parameters of an
Almost Ideal Demand System (AIDS) specification of import demand sys-
tems. The regularity conditions required by microeconomic theory, i.e.
adding up, homogeneity, symmetry and concavity conditions are all ex-
plicitly imposed in the estimation the model parameters. These economic
regularity conditions have to be satisfied if the parameters estimates are to
be used in the parameterisation of an applied general equilibrium (AGE)
model.
The AIDS import demand systems are estimated for two very differ-
ent types of products, electronic goods and cereals. The choice of such
contrasting goods was deliberate as it reveals the advantages of adopting
a flexible functional form specification, such as the AIDS, over more re-
strictive traditional trade specifications such as the CES based Armington
∗Correspondence Author, email: [email protected]
1
model. A priori, one might expect to find complementary relationships
between import demands for goods from different origins where the good
in question is a heterogeneous aggregate such as electronic goods. De-
mands for more homogeneous goods such as cereals are more likely to be
characterised by substitution relationships. Our results show that this is
the case for the two goods examined. Such complex trading patterns, in-
volving both complementary and substitution relationships in the import
demands for goods from different origins, cannot be captured by the com-
monly used Armington trade specification. The use of a flexible functional
form trade specification, such as the AIDS, allows the complexity of trade
patterns to be reflected in the parameterisation of the trade component
of AGE models. The use of the GME method allows for the empirical
estimation of such models and the imposition of the regularity conditions
that are necessary for their use in AGE modelling frameworks.
Keywords: Generalized Maximum Entropy (GME), Almost Ideal De-
mand Systems (AIDS), Constant Elasticity of Substitution (CES), Ap-
plied General Equilibrium (AGE), Armington
JEL Classifications: F1, F11, F17
1 Introduction
The choice of functional form in the trade specification of Partial Equilibrium
(PE) and Applied General Equilibrium (AGE) models is important since func-
tional form choice can be shown to have implications for the economic analysis
of the impact of policy change or other exogenous shocks on trade patterns.
Paucity of data has generally meant that when estimating import demand mod-
els economists have chosen functional forms such as the Constant Elasticity of
Substitution (CES) specification that are parsimonious in parameters. The use
of the CES function in the analysis of trade was first suggested by Armington
(1969) in a model that differentiated products on the basis of their country of
origin. The Armington specification allows for the analysis of intra-industry bi-
lateral trade flows that are found in trade data, however, the restrictive nature
of this functional form has been recognised in the literature. Brown (1987) and
2
Alston et al. (1990) have criticized the Armington specification on the basis of
its restrictiveness and the homothetic nature of the CES based functional form.
Winters (1984), Robinson et al. (1993) and Hertel (1997) have suggested that
Deaton and Muellbauers’s (1980) the Almost Ideal Demand System (AIDS)
specification could be a preferable alternative to the Armington specification
when modelling trade. The AIDS is a flexible functional form that allows for
the modelling of the consumer preferences without the imposition of restrictions
on the nature of substitution or complementarity relationships between pairs of
goods. It is highly probable, given the diverse range of products within the
agricultural and manufacturing sectors, that complementary as well as substi-
tution relationships between products from different countries will exist. It may
be reasonable to expect that relatively heterogenous product (electronics goods)
might have more of complementary pair of countries than relatively homogenous
product (cereals).
With the limited data and the multi-collinearity problems that characterise
trade data, it is often not possible to estimate import demand system specifi-
cations such as the AIDS using conventional estimation methods (this in part
explains the popularity of the Armington specification). Moreover, if compre-
hensive analysis of a change in trade policy is to be undertaken it is preferable
that the trade specification of the AGE model used has as many regions as pos-
sible. The greater the number of regions modelled, the larger the dimensions
of the import demand system and the greater the number of unknown param-
eters to be estimated. Given the trade datasets available, this often means
that the number of unknown parameters to be estimated exceeds the number
of data points, i.e. the estimation problem is ill-posed. The prices and income
(expenditure) data in trade datasets are also often collinear. This may mean
that the estimation problem is ill-conditioned, with the result that parameter
estimates are likely to be highly unstable. Although exact collinearity is rare,
severe collinearity leads to least square estimators with high variances. This in
turn means that small changes in the data can give rise to to large changes in
parameters with wrong signs or implausible magnitudes (Fraser, 2000).
For estimation problems which may be ill-posed and/or ill-conditioned, such
as the estimation of flexible functional form specifications of import models, the
3
GME method allows for consistent estimation. Moreover, the consistent GME
estimator does not require distributional error assumption and is asymptotically
normal. GME estimates are robust even if errors are not normal and the exoge-
nous variables are correlated (Golan et al., 2001). Van Akkeren et al. (2002)
show with Monte Carlo simulations that GME estimators display much more
desirable properties (lower mean square error loss) in the small samples that
are typically available when estimating trade functions. They also illustrate the
superiority of GME estimators when data are ill-conditioned due to substantial
multi-collinearity, as is usually the case with aggregate time series price data
(Gohin and Femenia, 2009). Additionally, given that the estimated model must
satisfy the conditions of consumer demand theory if one plans to use the esti-
mated trade model in a PE or AGE modelling framework, these must be imposed
in estimation. The imposition of equality, inequality and nonlinear constraints
on model parameters that are necessary for the satisfaction of the regularity
conditions of microeconomic theory can be easily implemented using the GME
method. This is because the GME estimators are only implicitly defined as
the solution to an optimisation problem subject to constraints. Asymptotic
properties of GME estimators are similar to the conventional estimators.
The objective of this paper is to use GME methods to estimate import de-
mand systems for cereals and for electronic goods using the AIDS specification.
The trade data used and the dimensionality of the trade models estimated (the
number of trading regions) mean that the estimation problems faced are both
ill-posed and ill-conditioned and thus ideally suited to the use of GME methods.
The GME method also allows us to impose the adding up, homogeneity, sym-
metry and regularity conditions of microeconomic theory in estimating import
demand models for cereals and electronic goods. The next section discusses the
AIDS functional form. The generalized maximum entropy methodology is then
presented in section 3. The data used for estimation are described in section
4. The results obtained are discussed in section 5 and some conclusions are
presented in section 6.
4
2 An Almost Ideal Demand System
The unrestrictive way of generalizing the specification and maintaining the as-
sumption of national product differentiation as in the Armington specifictioan
is to use flexible functional forms such as AIDS. These type of functional forms
do not impose constancy and pair wise equality of elasticity of substitution as
in the Armington specification. Deaton and Muellbauer introduce the Almost
Ideal Demand System in their 1980 paper to analyse the demand systems. Now,
the AIDS specification is one of the widely used model for demand analysis. The
AIDS approach use indirect utility approach for demand systems. The AIDS
cost function is represented as follows
logC(U,P ) = α0 +∑k
αklogPk +12
∑k
∑j
γ∗kj logPklogPj + uβ0
∏k
P βk k (1)
From Shepard lemma i.e. taking the price derivative of the cost function
gives quantity demanded, and multiply both side by Pi/C following demand
function represented in terms of shares can be derived,
Si =PiXi
C= αi +
∑j
γij logPj + βiUβ0
∏P βk k (2)
where
γij =12
(γ∗ij + γ∗ji)
For a utility-maximizing consumer, total expenditure µ is equal to C and
this equality can be inverted to give U as a function of P and µ. When this is
done for equation and substituted in equation , budget shares as a function of
P and µ can be represented as follows
Si =PiXi
C= αi +
∑j
γij logPj + βilog(µ
P) (3)
where, P is the price index defined by
logP = α0 +∑k
αklogPk +12
∑j
∑k
γkj logPklogPj
Full details of the AIDS specification can be found in Deaton and Muellbauer
1980 paper. αi, γij and βi are the parameters of the AIDS specification. The βi
parameters measure the change in ith budget shares with change in log( µP ) with
logPj held constant. Similarly, the γij parameters measure the change in the
5
ith budget share following a unit proportional change in logPj with log(µ/P )
held constant.
The AIDS specification for the importing countries ‘k’ and exporting coun-
tries ‘i’ can be represented as follows.
Ski =P ki X
ki
µk= αki +
∑j
γkij logPkj + βki log(
µk
P k) (4)
where, k = 1,2,...m+1 number of importing countries and i or j = 1,2,...m num-
ber of exporting countries. For ‘(m+1)’ importing countries and ‘m’ exporting
countries, there are ‘ 12m × (m + 5)’ parameters for ‘m × m × (m + 1)’ price
elasticities.
Constraints from consumer demand theory
The adding up constraints require following restrictions on the parameters for
all j, ∑i
αki = 1,∑i
βki = 0,∑i
γkij = 0. (5)
The homogeneity constraints require following restrictions on the parameters
for all j, ∑j
γkij = 0. (6)
The symmetry constraints require following restrictions,
γkij = γkji. (7)
The concavity constraints is satisfied if Slusky matrix lij is negative semi-definite
or eigenvalues of the matrix are all negative. Slusky matrix can be written as
lij = kijµ/pipj . (8)
where,
kkij = γkij + βki βkj log(µk/P k)− Ski δij + Ski S
kj .
Eigenvalues of Slusky matrix lij has same sign as of matrix kij . So, if matrix
kij is negative semi-definite or all its eigenvalues are negative then negativity
condition is satisfied. Kroneker delta δij is unity if i = j and zero if i 6= j.
Several authors have investigated ways of reparameterizing the Slusky matrix
so that concavity may be directly imposed locally or globally during estimation.
6
Moschini (1998) and Ryan and Wales (1998) show how the Lau’s(1978) approach
of Cholesky decomposition may be adopted to impose concavity locally in the
AIDS model. The Slusky matric L is of the form L = G+R where G has the same
number of independent elements as the Slusky matrix L and R is a matrix each
of the elements is a function of some or all of the other parameters in the model.
In the case of AIDS model, γkij is G and βki βkj log(µk/P k)− Ski δij + Ski S
kj is R.
Then, using the method of Lau’s(1978) Cholesky decomposition method, C can
be equated to a new matrix defined by−AA′, where A is lower triangular matrix,
and in the demand equations G can be replaced with −AA′ − R. Estimation
of A (rather than of G) and of the parameters in L guarantees that the slusky
matrix is negative semidefinite.
γkij = (−AA′)kij − βki βkj log(µk/P k) + Ski δij − Ski Skj . (9)
It is necessary to have estimated functional forms used in the applied general
equilibrium models should satisfy curvature conditions. Jorgenson and Frau-
meni (1981) applied their version of Lau’s (1978b) method of imposing curva-
ture conditions in their 36 industry translog study of U.S. industries, they ended
up setting 204 out of 360 second order parameters equal to zero. Moreover, al-
though their method imposes curvature conditions globally, it does so at the
cost of flexibility (Diewert and Wales, 1987). Concavity plays an important role
when predicting future consumption patterns when price levels are expected to
change and when conducting policy analysis that depends on price elasticities.
The growth in the use of applied general equilibrium models (e.g. GTAP) and
partial equilibrium models (e.g. CAPRI or FAPRI) to analyse the trade poli-
cies, it needs to be ensured that price and income elasticities are estimated in a
theoretically consistent manner (Cranfield and Pellow, 2004).
Elasticities
The parameters of the AIDS cannot be used for useful economic interpretations.
So, it is required to calculate the expenditure and price elasticities from those
parameters. The expenditure elasticities at the sample mean is
εki = 1 +βkiski. (10)
7
Marshallian price elasticities at the sample mean is
ηkij = −δij +γkijski− βkiski
(αkj +∑k
γkjklogPkk ) (11)
where δij = 0 for i 6= j and δij = 1fori = j.
Hicksian price elasticities is given by
εkij = ηkij + εiskj (12)
3 Generalized Maximum Entropy method
The traditional maximum entropy (ME) method is based on the information
theory developed by Shannon(1948). Shannon defined entropy as the measure
of uncertainty (state of knowledge) of a collection of events. Let x be a random
variable with possible outcomes xs, s = 1, 2,...N, with probabilities qs such that∑s qs = 1. Shannon defined the entropy of the distribution q = (q1, q2, ...qs), as
H(q) = −∑s
qslnqs (13)
when, qs = 0 then H(q) = 0 and when q1 = q2 = .... = qs = 1/N then H(q) is
maximum. To determine the probability assigned, Jaynes (1957a, 1957b) pro-
posed maximum entropy principle which is to maximize entropy subject to the
available sample moment information and the requirement that the probablili-
ties be proper (added to one).
Golan et al. (1996) proposed Generalized Maximum Entropy by specifying
error term in maximum entropy framework. The GME method uses a flexible,
dual loss objective function: a weight average of the entropy of the deterministic
part of the model and the entropy from the disturbance or stochastic part (Golan
et. al 1996). Different weight could be given to deterministic and stochastic
part of the model. Intuitively, giving the higher weight to deterministic part
will allow data to speak more. Balanced approach is used in this paper where
equal weight is given to both objectives. ME is special case of the GME where
no weight is placed on the entropy of the error terms and where the data are
represented in terms of exact moments.
8
In generalized maximum entropy formulations, we define ill-posed pure in-
verse problem as
y = Xβ + e (14)
where y is a T- dimensional vector of observables, β is a (K > T )-dimensional
vector of coordinates that reflects the unknown and unobservable coefficients,
and X is a linear operator that is a known (T x K) non-invertible matrix. e is
T- dimensional vector of unobserved disturbance.
β is defined by support space Z and probability q. Consistent with this
specification, rewrite β as
β = Zq (15)
where Z is a (K ×KM) matrix and q is a KM dimensional vector of weights.
For each βk, there exists a discrete probability distribution that is defined
over the parameter space [0,1] by a set of equally distanced discrete points
z = [z1, z2, ...., zM ] with corresponding probabilities qk = [qk1, qk2, ....., qkM ]
and with M ≥ 2. So, βk can be expressed as convex combination of M- dimen-
sional vector of support points of zk and M- dimensional positive weights qk
that sums to one.
β = Zq =
z′
1 0 . . 0
0 z′
2 . . 0
. . . . .
. . . . .
0 . . . z′
k
q1
q2
.
.
qk
z′
kqk =∑m
zmqkm = βk (16)
for k = 1,2 ,...,K, m = 1,2,...,M
Similarly, the vector of disturbance e can be defined as
e = Vw (17)
where V is a (T x TJ) matrix and w is a TJ dimentional vector of weights.
For each et is convex combinations of J-dimensional vector of support space
vT and J-dimensional positive weights wT that sums to one. The T unknown
9
disturbance e may be written in matrix form as
e = Vw =
v′
1 0 . . 0
0 v′
2 . . 0
. . . . .
. . . . .
0 . . . v′
T
w1
w2
.
.
wT
eT = v′TwT =
∑J
vtJwt (18)
The natural support vector for the error term v = (−1, 0, 1) because all the
dependent variables are shares that lie between 0 and 1.
Using reparameterized unknowns β = Zq and e = Vw,
y = Xβ + e = XZq + Vw (19)
The objective of the generalized entropy problem is to recover the unknown
parameters through the sets of probabilities, q and w. At the optimal solution,
the probabilities must satisfy the model or consistency constraints additivity
constraints. Accordingly, Golan et. al (1997) propose a generalized maximum
entropy solution to the linear inverse problem with noise that selects q, w >> 0
to maximize
H(q,w) = −q′lnq−w
′lnw (20)
subject to
y = XZq + Vw (21)
1k = (IK ⊗ 1′M)q (22)
1T = (IT ⊗ 1′J)w (23)
The Generalized Maximum Entropy objective is strictly concave on the in-
terior of the additivity constraints set, and a unique solution exists if the inter-
section of the consistency and additivity constraints sets is non-empty. GME
selects probabilities on supports Z and V that are most uniform (i.e. uncertain)
and satisfy the observed information. The optimal probability vectors, q and w,
may be used to form point estimates of the unknown parameter vector, β = Zq,
and the unknown disturbances, e = Vw.
10
The choice and dimension of the support space on the parameters and error
term is discussed in Golan et al. 1996 (chapter 8). The choice of support
space on the parameters i.e. the restrictions imposed on the parameter space
through Z reflect prior knowledge about the unknown parameters. However,
such knowledge is not always available, and researchers may want to entertain
a variety of plausible bounds on β . Wide bounds may be used without extreme
risk consequences if our knowledge is minimal and we want to ensure that Z
contains β . Intuitively, increasing the bounds increases the impact of the data
and decreases the impact of support. The dimension of the support space on the
parameters and error. The increase in the number of points in the support and
allocate them in the equidistant fashion, the variance of the uniform distribution
decreases. The greatest improvement in precision comes from using the M and
J to be 5.
The computation of asymptotic standard errors for estimated coefficients is
also possible and may facilitate a more conventional inference apporach. The
assumptions and calculation of asymptotic standard error is described in the
Appendix I.
Substituting these reparameterized terms in the AIDS specification,
Skit =∑b
akibckib +
∑j
∑d
zkijdqkijdlogP
kjt + zkdq
kidlog(
µktP kt
) +∑h
vhwith (24)
Here, b, d and h is dimensions of the support space c, q and w respectively.
GME adding up conditions,∑d
qkijd =∑d
qkid =∑h
wkith = 1 (25)
So GME estimator for AIDS is to maximize
H(q,w) = −q′lnq−w′lnw (26)
subject to import share equations (equation 24 ), GME adding up condition
(equation 25) and constraints from consumer demand theory (equations 5, 6,
7 and 9). Forming the Lagrangean and solving for the first order conditions
yields the optimal solution qhat and what. Then estimates of the AIDS can be
determined by
αki =∑b
akibckib (27)
11
γkij =∑d
zkijdqkijd (28)
βki =∑d
zkidqkid (29)
ekit =∑d
vkithwkith (30)
These estimates are then used to calculate the expenditure elasticities and
Hicksian own and cross price elasticities using equations 10 and 12 respectively.
4 Data
More heterogenous goods (electronics) and relatively homogenous goods (cere-
als) are chosen for the estimation in view of capturing both complementary and
substitution relationships that may exists in complex trading pattern. Cere-
als sector used in this paper is the aggregation of GTAP sectoral Classification
(GSC2) No. 1, 2 and 3. Electronic goods used in this paper is GTAP Sectoral
Classification (GSC2) No. 40 with the view of using these estimates in GTAP
model. Data used in this paper is taken from UN COMTRADE database of
the classification Standard International Trade Commodity (SITC) Rev3 data
at 4 digit level and aggregated to map at GTAP Sectoral Classification level.
Yearly data of import from the period 1988 - 2008 are used to estimate for both
the sectors. Eleven regions are chosen for electronic sector are Brazil, China,
DEDC (Group of Developed Countries), India, Ireland, LDC (Group of Least
Developed Countries), REU15 (Rest of the EU 15), REU27 (Rest of EU 27),
RWorld (Rest of the World), UK and USA 1. It would have been better to
compare between electronic goods and cereals if trade data of cereals for all
the eleven regions was available but unfortunately, it seems there is no bilateral
trade between some of the regions for cereals. So, only five regions chosen for
cereals sector are Ireland, UK, DEDC, REU15, and RWorld. Data are in terms
of Values (dollars) and Quantities (kg). Price are calculated as the ratio of val-
ues to quantities at SITC Rev3 4-digit level. Then the price at the aggregation
level of GTAP is calculated using the weighted average at the price level of SITC
Rev3 4-digit level.1This aggregation of different region is followed from the Horridge and Labrode (2008) for
their TASTE program
12
In this paper, we estimate with the assumption of the separability between
domestic and imported products. Eventhough, this assumption of separability
between domestic and imported products have already been questioned (Win-
ters, 1984), domestic product could not be included in the estimation typically
because data for domestic product is not available in the same classification as
SITC Rev3.
5 Results
GME estimates are estimated by maximizing the entropy function subject to
constraints of AIDS specification, GME adding up condition for probabilities
and consumer demand theory restrictions of adding up, homogeneity, symme-
try and concavity. The AIDS parameters are calculated using equations 27,
28 and 29. The parameters estimated for the AIDS model have no economic
meaning in themselves. So, the Hicksian own and cross price elasticities and
expendtiture elasticites (refer Table 1 for cereals and Table 2 electronics goods)
are calculated at the sample mean . With the concavity imposed, all the own
price elasticites are negative for both the cereals and electronics goods as ex-
pected. In the import of cereals and electronics goods by different countries,
both complementary and substitution between the pair of countries are seen.
More complementary pair of countries are found in electronic goods than in
cereals (only 7 pairs of countries are complementary out of 30 pairs in cereals
and 420 pairs of countries are complementary out of 990 pairs of countries in
electronics goods). Parameters estimated are shown in tables of Appendix II.
About one third of the parameter estimated are statistically significantly differ-
ent from zero on the basis of asymptotic t-statistics (refer tables in appendix
II). Asymptotic standard errors are calculated as described in Appendix I and
have to interpreted with cautious in the GME context.
It is found that estimated parameters are sensitive to support vector chosen
to parameters. This in turn has an impact on the other calculated values of
expenditure, own and cross price elasticities showing changes sign as well as
magnitude. Sensivity of support vector to the parameter estimated is done
in this paper. First, support vector for α is selected as [-1,-0.5,0,0.5,1] and
13
Figure 1: Hicksian Own and Cross Price Elasticities Cereals
support vector for both γ and β are chosen as [-1,-0.5,0,0.5,1], [-5,-2.5,0, 2.5,5],
[-20,-10,0,10,20] and [-100,-50,0,50, 100] consecutively to see the effect on the
estimated parameter. It is found that there is only some changes in estimated
parameter. Then, support vector for α is chosen as [-100,-50,0,50, 100] and for
both γ and β is selected as [-1,-0.5,0,0.5,1], [-5,-2.5,0, 2.5,5], [-20,-10,0,10,20] and
[-100,-50,0,50, 100] consecutively. It is found that at the higher support vector
of α, estimated parameter differ widely in first two support vector of γ and β
in both magnitude and direction but for later two support vectors, difference
in estimated parameter is very less. It would appear therefore that the selected
support vectors clearly influence on the estimated values. So,the results in the
estimation shown are estimated using wider support vector of [-100,-50,0,50,
100] for α, γ and β. If no prior information about the parameter, it is also
suggested that to chose the wider support vectors such that it is wide enough
to include all the possible outcomes (Golan et al. 2001). The natural support
vector for the error terms is [-1,-0.5,0,0.5,1] as all the dependable variable is
shares that lies between 0 and 1 in our model.
All of the own price estimates of cereals have a negative sign and appear to
be sensible in terms of magnitude of the estimates. The cross price estimates
for cereals have both negative and positive signs implying complementary and
substitution relationships. Most of the cross price elasticity are positive. The
results shows that there are only few negative cross price elasticities. Further-
14
Figure 2: Hicksian Own and Cross Price Elasticities for Electronic goods
15
more, negative cross price elasticities are mostly low except at very low import
shares. (refer fig 1 and table 1). In DEDC, cross price elasticities are all posi-
tive. In REU15 and UK, there is only one pair of countries that have negative
cross price elasticities. In RWorld and USA, there are two and three pairs of
countries that are complementary respectively. These cross price elasticities are
asymmetric meaning that the change in price of US cereals in import demand
of UK cereals in DEDC is different than the change in price of UK cereals in
import demand of USA cereals in DEDC.
Own, cross and expenditure elasticities for electronics goods of 11 importing
countries are shown in table 2. The results show that cross price elasticities
are in very diverse range. At low import shares from 0 to 0.001, the cross
price elasticities are on the very high range, at import shares from 0.01 to 0.001
cross price elasticities are on high range, at import shares from 0.1 to 0.01 and
0.1 to 0.8 cross price elasticities range is moderate (refer figure 2). Own price
elasticities are all negative as expected. The own price elasticities at low import
shares below 0.02 starts to decrease exponentially. Above the 0.02 import share,
own price elasticities is usually within -2. It can be clearly seen from the results
that import shares have a huge affect on the own and cross price elasticities of
electronic goods.
Expenditure elasticities of cereals and electronic goods are in all the three
categories (refer table 1), above 1 (superior goods), between is 0 to 1 (normal
goods) and below 0 (inferior goods). For DEDC as importing countries for ce-
reals, expenditure elasticities are all negative except for USA . And for REU15,
it is all positive and above 1 for DEDC, RWorld and UK and below 1 for USA
cereals. Refer table 1 and table 2 for expenditure elasticites for cereals and
electronic goods respectively. The result shows that the superior goods in one
country could be inferior in the other country. For instance, LDC product may
be considered to be inferior in Brazil while superior in China. It is possible that
different quality of goods is imported by different countries. Clearly, import
shares have influence in the expenditure elasticies of electronic goods as well.
16
Table 1: Hicksian Own and Cross Price Elasticities for Cereals
Importing Country: DEDC
Price Elasticities Expenditure
Elasticities
REU15 RWorld UK USA
REU15 -1.747 0.050 0.086 1.611 -2.718
RWorld 0.070 -2.180 0.020 2.090 -0.095
UK 1.737 0.291 -4.141 2.114 -7.791
USA 0.081 0.075 0.005 -0.162 1.249
Importing Country:REU15
DEDC RWorld UK USA
DEDC -1.296 0.134 -0.390 1.552 1.378
RWorld 0.071 -3.337 1.997 1.270 1.560
UK -0.250 2.434 -2.185 0.001 1.040
USA 0.768 1.195 0.001 -1.964 0.255
Importing Country:RWorld
DEDC REU15 UK USA
DEDC -1.280 -0.014 -0.011 1.305 1.403
REU15 -0.024 -0.235 0.226 0.032 0.235
UK -0.157 2.018 -1.952 0.091 -0.391
USA 0.577 0.009 0.003 -0.589 1.071
Importing Country:UK
DEDC REU15 RWorld USA
DEDC -0.393 0.385 0.158 -0.269 1.293
REU15 0.076 -0.400 0.005 0.198 1.054
RWorld 0.430 0.072 -1.846 1.224 4.335
USA -0.556 2.066 0.927 -2.557 -2.697
Importing Country:USA
DEDC REU15 RWorld UK
DEDC -0.240 0.179 -0.014 0.075 1.206
REU15 1.406 -1.698 0.417 -0.124 -0.685
RWorld -0.105 0.392 -0.167 -0.119 1.263
UK 26.510 -5.598 -5.710 -15.202 -8.644
17
Table
2:
Hic
ksi
an
Ow
nand
Cro
ssP
rice
Ela
stic
itie
sfo
rE
lectr
onic
pro
duct
Importin
gC
ountry:
Brazil
Pric
eEla
stic
itie
sExpendit
ure
Ela
stic
itie
s
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Chin
a-1
.780
-0.0
63
-0.1
03
-0.4
62
-0.1
21
-0.1
62
-0.4
27
-0.9
42
0.8
89
1.0
75
2.0
96
DED
C0.1
70
-1.5
94
0.0
99
0.0
50
-0.0
49
0.1
37
0.0
42
0.7
13
-0.5
86
1.1
37
-0.1
20
India
-12.0
64
42.8
93
-4.4
06
-5.4
84
0.0
92
7.9
35
-7.1
87
8.6
45
20.8
77
24.2
67
-75.5
69
Irela
nd
-8.5
46
2.2
76
-0.6
16
-6.0
75
-0.7
24
-0.2
81
-3.5
58
7.1
18
5.8
26
8.6
44
-4.0
64
LD
C-2
72.3
75
-120.1
48
1.6
61
-97.9
93
-67.4
90
66.2
33
-54.6
06
-166.6
16
-113.5
32
1493.2
43
-668.3
76
REU
15
-0.0
50
-0.0
11
-0.0
03
-0.0
37
-0.0
03
-0.9
41
0.1
75
0.1
01
0.0
71
-0.4
82
1.1
79
REU
27
-7.0
54
1.0
73
-0.6
88
-3.0
59
-0.3
57
3.5
88
-3.3
78
0.2
80
4.5
55
4.4
83
0.5
56
RW
orld
-0.3
11
0.0
57
-0.0
23
0.0
91
-0.0
43
-0.0
58
-0.0
03
-1.8
88
-0.2
90
0.4
07
2.0
60
UK
5.5
57
-9.7
45
0.8
01
2.1
42
-0.3
34
-0.8
52
1.9
65
-10.1
01
-11.3
83
7.3
36
14.6
15
USA
0.4
92
0.5
08
-0.0
02
0.1
03
0.1
28
-0.0
32
0.0
77
0.9
09
0.4
50
-2.6
65
0.0
32
Importin
gC
ountry:
Chin
a
Brazil
DED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Brazil
-14.8
53
-42.4
20
0.7
41
1.7
13
10.2
89
47.4
29
9.6
74
-4.3
04
10.2
91
11.4
97
-30.0
57
DED
C-0
.041
-1.0
30
-0.0
36
0.0
91
-0.0
63
-0.0
77
-0.1
01
0.9
73
0.0
58
-0.0
61
0.2
87
India
0.7
47
-27.9
07
-10.8
21
11.2
84
0.2
28
-15.7
95
-3.0
61
127.0
29
-5.4
17
-17.0
55
-59.2
33
Irela
nd
0.1
02
3.9
61
0.7
21
-3.0
50
-0.6
87
1.5
79
-0.0
96
-16.6
80
0.8
82
0.8
83
12.3
85
LD
C89.4
33
-886.4
76
1.7
99
-94.2
48
-363.1
32
-402.7
95
-311.2
07
1292.3
53
127.4
98
-221.9
40
768.7
24
REU
15
0.1
10
-0.1
54
-0.0
52
0.1
02
-0.0
94
-0.7
90
-0.1
56
0.9
67
0.0
40
0.0
26
0.0
02
REU
27
0.7
69
-10.8
02
-0.2
60
-0.0
88
-2.8
50
-5.4
04
-3.5
46
18.3
23
2.1
59
-1.7
67
3.4
66
RW
orld
-0.0
09
0.1
40
0.0
40
-0.0
72
0.0
82
0.0
20
0.1
02
-1.8
66
-0.0
56
-0.0
13
1.6
31
UK
0.2
11
1.8
30
-0.1
27
0.3
30
0.3
29
0.4
23
0.5
80
-1.4
92
-1.6
18
0.4
10
-0.8
75
USA
0.0
19
-0.1
64
-0.0
52
0.0
72
-0.0
38
0.0
00
-0.0
41
0.6
47
0.0
32
-0.7
42
0.2
67
18
Importin
gC
ountry:
DED
C
Brazil
Chin
aIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Brazil
-22.1
66
25.3
27
-19.7
41
-7.0
92
9.0
86
24.2
72
2.2
87
-15.5
79
10.7
60
41.9
98
-49.1
53
Chin
a0.1
64
-0.6
40
0.1
55
-0.0
26
-0.0
89
-0.4
29
-0.0
41
-1.0
76
-0.2
06
-1.5
10
3.6
98
India
-57.6
26
86.1
30
-125.7
05
-52.8
69
52.8
60
68.4
92
6.2
46
90.8
13
31.1
48
171.5
04
-270.9
94
Irela
nd
-0.6
09
0.3
56
-1.5
16
-2.4
88
0.5
45
1.5
01
0.1
54
1.8
83
0.5
91
0.5
84
-1.0
03
LD
C103.7
59
-159.3
04
206.5
55
74.9
08
-260.7
89
-29.7
89
-12.6
50
-382.7
21
-101.7
31
217.3
71
344.3
91
REU
15
0.1
84
-0.1
58
0.1
28
0.1
59
0.0
07
-1.0
36
-0.0
34
-0.1
96
-0.2
34
0.0
55
1.1
22
REU
27
0.3
93
-1.4
31
0.3
20
0.2
68
-0.1
87
-1.4
25
-2.0
44
-3.3
76
-0.1
17
-0.1
54
7.7
54
RW
orld
-0.1
02
-0.0
64
-0.0
10
0.0
39
-0.0
67
-0.0
79
-0.0
15
-1.0
11
0.0
85
-0.0
72
1.2
96
UK
0.3
02
-0.4
42
0.2
51
0.2
23
-0.2
62
-0.7
74
0.0
18
1.0
46
-1.9
34
0.5
57
1.0
15
USA
0.0
83
-0.0
94
0.0
90
0.0
11
0.0
95
0.1
47
0.0
41
0.4
12
0.0
90
-0.8
45
-0.0
30
Importin
gC
ountry:
India
Brazil
Chin
aD
ED
CIr
ela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Brazil
-3.9
30
0.7
55
-6.5
24
-2.8
76
2.0
41
-5.2
28
0.1
46
17.6
28
-2.4
06
-1.4
20
1.8
13
Chin
a0.0
03
-2.4
01
0.0
38
-0.3
70
-0.0
58
-0.3
39
-0.2
15
-0.3
19
0.1
98
0.2
32
3.2
30
DED
C-0
.030
0.4
19
-1.0
93
-0.0
43
0.1
34
-0.0
12
-0.0
92
0.2
55
0.0
26
0.3
41
0.0
95
Irela
nd
-0.5
20
-11.8
39
-2.9
24
-3.7
96
0.5
44
-0.2
27
-2.4
78
11.4
29
1.6
34
-0.8
29
9.0
07
LD
C12.0
08
-52.6
76
167.1
72
18.0
15
-38.5
71
-36.7
62
-2.0
35
-9.8
31
-30.8
83
5.1
07
-31.5
44
REU
15
-0.0
33
-0.0
87
-0.1
54
0.0
25
-0.0
44
-0.7
36
-0.0
25
0.0
18
0.0
36
-0.0
09
1.0
10
REU
27
0.0
32
-10.6
11
-6.7
72
-3.6
96
-0.0
98
-2.2
81
-4.2
10
14.8
31
0.4
39
0.8
57
11.5
09
RW
orld
0.0
33
0.1
92
-0.0
21
0.1
51
-0.0
07
0.0
21
0.1
32
-1.2
55
-0.0
66
-0.0
56
0.8
75
UK
-0.0
52
1.1
21
0.1
16
0.2
39
-0.1
20
0.2
31
0.0
68
-0.4
76
-0.8
84
-0.3
41
0.0
98
USA
-0.0
05
0.5
31
0.2
73
0.0
16
0.0
00
0.0
76
0.0
45
0.0
96
-0.0
74
-1.2
61
0.3
03
Importin
gC
ountry:
Irela
nd
Brazil
Chin
aD
ED
CIn
dia
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Brazil
-87.4
75
81.5
58
-14.5
42
-4.0
93
-44.4
16
-53.8
56
65.9
29
-38.0
77
-61.4
78
219.3
75
-62.9
24
19
Chin
a0.2
11
-2.7
04
1.0
16
-0.0
71
0.6
57
-1.2
57
-0.3
73
-2.5
53
-0.0
67
0.5
40
4.6
01
DED
C-0
.036
1.0
11
-0.8
83
0.1
02
-0.1
66
0.0
05
0.3
32
1.2
00
-0.0
29
-0.2
31
-1.3
06
India
-1.0
89
-7.0
95
14.4
97
-10.6
00
6.4
29
-11.1
84
-6.5
21
12.8
92
-32.1
05
26.2
39
8.5
37
LD
C-6
.600
37.5
00
-11.9
04
3.6
47
-17.6
63
31.4
47
6.9
14
-1.1
39
10.9
04
-28.1
86
-24.9
21
REU
15
-0.0
71
-0.3
08
-0.2
49
-0.0
43
0.2
08
-1.3
18
-0.0
08
0.0
30
-0.3
91
0.8
96
1.2
53
REU
27
0.8
74
-1.8
60
1.9
53
-0.3
31
0.5
91
-0.6
66
-2.3
64
-1.6
42
0.2
00
-1.8
00
5.0
45
RW
orld
-0.0
38
-0.5
07
0.1
55
0.0
39
-0.0
36
-0.1
12
-0.0
49
-1.2
43
0.0
41
-0.3
61
2.1
13
UK
-0.0
61
0.1
96
-0.2
54
-0.0
95
0.0
30
-0.2
67
0.0
62
0.2
84
-0.9
68
-0.0
56
1.1
28
USA
0.1
56
0.4
71
-0.1
77
0.0
82
-0.1
72
0.8
78
-0.0
31
0.2
51
0.2
94
-1.2
12
-0.5
39
Importin
gC
ountry:
LD
C
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
REU
15
REU
27
RW
orld
UK
USA
Brazil
-11.9
99
25.7
53
-24.1
69
4.9
38
-11.9
41
3.7
32
18.4
87
23.0
00
-5.7
19
0.9
29
-27.5
85
Chin
a0.7
84
-2.5
49
1.5
82
-0.6
79
0.3
86
-0.3
59
-3.4
92
-1.9
29
0.3
58
0.3
05
6.4
70
DED
C-0
.309
0.9
12
-1.6
86
0.1
24
0.1
63
0.2
31
1.3
61
0.7
58
-0.2
06
-0.2
60
-1.4
87
India
8.4
47
-38.0
65
13.0
15
-21.5
84
17.6
89
-8.1
54
-40.0
09
-25.8
74
32.5
96
24.1
49
44.7
98
Irela
nd
-0.3
07
0.4
87
-0.0
95
0.2
63
-1.0
45
0.0
74
-0.3
93
0.1
35
-0.4
88
-0.1
58
1.6
27
REU
15
42.4
37
-126.7
82
232.9
40
-53.5
89
37.1
24
-64.8
67
-254.2
78
-81.3
06
110.6
78
140.9
33
19.7
03
REU
27
0.0
28
-0.1
83
0.0
75
-0.0
51
-0.0
57
-0.0
84
-1.1
06
0.0
08
-0.0
14
0.0
04
1.4
53
RW
orld
1.2
14
-3.3
27
2.5
33
-0.7
90
-0.0
64
-0.3
92
-2.2
53
-4.4
36
1.1
02
-0.0
69
7.5
27
UK
-0.1
39
0.5
29
-0.4
10
0.3
66
-0.2
17
0.1
71
0.8
24
0.5
75
-1.0
68
-0.1
01
-0.8
21
USA
-0.0
24
0.5
00
-0.4
97
0.2
82
0.0
41
0.2
18
0.8
94
0.2
05
-0.1
03
-1.0
02
-0.8
00
Importin
gC
ountry:
REU
15
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
27
RW
orld
UK
USA
Brazil
-12.7
80
-5.8
25
32.0
75
-6.9
23
1.5
05
-5.9
17
2.4
85
6.9
80
-18.6
14
-7.7
89
14.8
03
Chin
a-0
.063
-0.6
71
-0.9
17
-0.1
15
-0.1
04
0.0
37
-0.3
21
-1.0
64
-0.8
09
-0.0
76
4.1
03
DED
C0.2
60
-0.0
64
-1.4
54
0.1
04
0.0
63
0.2
37
0.2
53
-0.0
40
0.1
78
0.6
44
-0.1
81
India
-11.0
76
-9.9
65
28.5
70
-49.4
22
24.7
93
6.5
67
-10.7
69
10.0
68
-1.3
41
45.4
39
-32.8
65
Irela
nd
0.0
71
-0.0
35
-0.0
82
0.5
08
-0.8
71
-0.0
22
-0.1
54
0.8
67
-0.5
97
-1.5
27
1.8
42
20
LD
C-9
6.5
25
47.8
45
523.5
23
66.6
06
-9.2
07
-143.9
79
4.0
92
-124.4
55
-40.0
11
-201.5
89
-26.3
01
REU
27
0.0
84
-0.7
15
0.0
69
-0.2
20
-0.2
31
0.0
04
-1.6
72
-1.4
27
-0.4
87
0.0
62
4.5
33
RW
orld
0.0
51
-0.0
63
-0.2
79
0.0
00
0.1
42
-0.0
38
-0.0
69
-0.9
94
0.1
02
-0.0
29
1.1
76
UK
-0.1
65
-0.1
77
0.2
03
-0.0
37
-0.0
98
-0.0
27
0.0
51
0.5
72
-0.4
37
0.1
04
0.0
10
USA
-0.0
41
0.4
03
0.6
85
0.1
94
-0.2
63
-0.1
00
0.2
53
0.3
67
0.1
12
-1.4
06
-0.2
05
Importin
gC
ountry:
REU
27
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
RW
orld
UK
USA
Brazil
-6.2
16
-8.4
41
-11.0
10
-5.2
24
14.5
93
1.2
80
19.1
28
-25.7
93
-0.4
81
15.9
79
6.1
84
Chin
a-0
.075
-1.2
46
-0.1
61
-0.2
24
-0.2
19
-0.2
69
-0.0
34
-1.6
70
0.3
69
0.3
47
3.1
84
DED
C-0
.098
0.0
77
-0.6
58
0.0
04
0.2
78
0.0
83
0.1
11
-0.2
64
0.0
31
-0.0
38
0.4
74
India
-6.9
68
-37.9
93
-5.3
48
-12.9
28
3.0
62
-4.8
60
18.8
32
-62.5
24
17.7
01
23.4
91
67.5
36
Irela
nd
0.9
78
-1.7
61
1.6
01
0.1
93
-7.5
88
-0.8
71
0.4
46
1.1
30
1.5
99
-0.7
85
5.0
58
LD
C8.6
00
-216.7
93
43.9
43
-25.1
88
-91.2
97
-158.5
43
-133.4
34
-95.7
32
195.6
61
278.4
44
194.3
41
REU
15
0.0
41
0.2
32
0.0
22
0.0
68
0.0
68
-0.0
14
-0.9
86
0.2
20
-0.0
91
-0.0
41
0.4
79
RW
orld
-0.0
80
-0.4
41
-0.1
85
-0.1
12
0.0
97
-0.0
22
-0.0
83
-0.8
59
0.0
25
0.1
17
1.5
43
UK
-0.0
02
0.9
00
0.1
29
0.2
34
0.4
11
0.4
35
-0.2
91
0.6
14
-1.4
66
-0.5
17
-0.4
48
USA
0.1
87
0.7
41
0.0
25
0.2
44
-0.0
68
0.4
86
0.1
07
0.8
86
-0.4
15
-1.8
54
-0.3
40
Importin
gC
ountry:
RW
orld
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
UK
USA
Brazil
-5.9
84
4.5
02
23.1
98
-1.1
16
-0.1
01
0.1
60
-4.1
16
4.8
20
-0.1
83
-5.9
75
-15.2
05
Chin
a0.0
13
-0.8
31
-1.1
87
0.0
58
-0.0
77
0.1
00
-0.3
77
-0.0
23
-0.0
92
-0.7
10
3.1
25
DED
C0.1
56
-0.1
89
-0.9
83
-0.0
22
0.0
28
0.1
23
0.1
50
-0.0
42
0.2
24
0.4
09
0.1
46
India
-1.4
59
10.9
58
3.5
64
-5.0
48
2.2
84
-0.1
25
-1.4
26
3.8
58
6.6
29
4.0
07
-23.2
41
Irela
nd
-0.0
72
-1.5
83
-0.8
45
0.2
91
-2.4
83
0.1
05
-3.4
05
0.4
82
-0.6
11
2.8
90
5.2
34
LD
C2.8
28
127.4
39
250.8
08
-2.2
40
8.7
50
-124.8
79
-96.5
21
-13.7
27
-106.5
61
-198.9
09
153.0
12
REU
15
-0.1
11
0.0
12
0.2
19
-0.0
64
-0.2
36
-0.0
64
-0.6
75
0.2
52
0.1
05
0.1
11
0.4
51
REU
27
1.0
78
-1.4
23
-3.6
29
0.6
27
0.5
29
-0.1
42
2.2
75
-3.3
77
1.0
31
-5.3
14
8.3
45
UK
-0.0
54
-0.1
11
1.6
51
0.3
15
-0.1
68
-0.3
61
0.3
47
0.3
93
-4.4
85
1.4
11
1.0
62
21
USA
-0.0
89
0.0
44
0.2
93
-0.0
20
0.1
80
-0.0
63
0.0
42
-0.1
05
0.1
82
-0.9
95
0.5
32
Importin
gC
ountry:
UK
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
USA
Brazil
-21.9
53
-10.3
63
9.8
09
-4.5
97
-5.4
77
0.6
61
-46.5
83
-3.9
60
-9.1
48
18.3
85
73.2
26
Chin
a-0
.244
-1.0
04
-1.0
89
-0.3
03
0.1
17
0.0
48
-2.0
16
0.0
59
-0.2
24
-0.6
35
5.2
91
DED
C0.2
14
-0.1
18
-1.8
16
0.1
25
0.3
33
-0.0
50
1.3
34
0.1
81
0.3
77
0.1
80
-0.7
60
India
-8.0
12
-17.5
86
14.8
90
-18.5
65
6.8
27
5.3
78
-57.7
76
-8.3
76
10.9
96
35.5
87
36.6
37
Irela
nd
-0.0
22
0.2
70
0.4
63
0.1
16
-0.8
70
0.0
72
-0.4
56
-0.1
61
0.2
99
-0.5
59
0.8
50
LD
C8.0
87
26.5
27
-24.4
25
35.8
29
50.7
09
-24.0
01
234.3
01
7.8
72
-60.4
08
-37.7
08
-216.7
81
REU
15
-0.0
81
-0.0
69
0.2
10
-0.1
00
-0.1
06
0.0
52
-1.0
56
-0.0
18
-0.2
09
0.1
01
1.2
75
REU
27
-0.1
25
0.0
94
0.1
21
-0.2
40
-0.6
85
0.0
11
-1.7
54
-1.2
29
-1.8
44
0.2
01
5.4
50
RW
orld
0.0
45
0.1
42
-0.0
12
0.0
67
0.0
73
-0.0
58
-0.2
23
-0.0
91
-0.8
55
-0.0
74
0.9
87
USA
0.2
65
0.0
84
0.1
32
0.2
09
-0.1
25
-0.0
55
0.9
31
0.1
86
0.2
76
-1.2
17
-0.6
86
Importin
gC
ountry:
USA
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
Brazil
-8.7
00
-16.2
19
28.5
88
1.0
27
3.1
76
-2.2
64
-1.8
09
1.7
34
-11.5
19
3.1
21
2.8
66
Chin
a-0
.308
-1.9
49
-0.0
96
0.1
25
0.3
08
-0.3
53
0.1
32
0.0
60
-2.0
98
-0.0
31
4.2
11
DED
C0.2
88
0.6
13
-1.0
31
-0.0
31
-0.0
54
0.0
67
0.0
28
-0.0
67
0.5
78
-0.0
03
-0.3
87
India
3.6
76
27.1
16
-5.1
16
-8.5
62
-6.4
27
10.0
64
-0.7
01
2.4
12
-1.3
63
0.7
95
-21.8
93
Irela
nd
1.0
77
5.8
13
-2.6
40
-0.6
38
-4.0
38
3.7
78
1.7
96
-0.4
09
-6.0
19
-1.0
96
2.3
77
LD
C-1
79.2
93
-1341.9
50
880.9
74
231.7
86
909.3
02
-1100.1
80
-504.4
28
159.7
15
2038.2
14
104.9
81
-1199.1
10
REU
15
-0.1
14
1.3
30
0.5
39
-0.0
30
0.4
02
-0.4
77
-2.0
20
0.0
76
1.6
29
0.2
83
-1.6
18
REU
27
1.0
15
2.2
26
-4.6
10
0.3
82
-0.6
99
1.1
16
0.5
07
-1.1
01
-0.0
05
-0.5
58
1.7
28
RW
orld
-0.0
57
-0.1
47
-0.0
95
-0.0
20
-0.0
85
0.0
94
0.0
19
0.0
03
-0.8
05
-0.0
05
1.0
97
UK
0.5
11
0.0
92
-0.6
35
0.0
19
-0.5
22
0.1
72
0.5
29
-0.1
56
-0.4
81
-1.2
60
1.7
30
22
6 Conclusions
The generalized maximum entropy estimation method is a technique that is
useful when estimating import demand systems with limited trade data and
collinear exogenous variables (Golan et.al. 1996, Faser, 2000, Gohin and Fe-
menia 2009). The equality (adding up, homogeneity and symmetry) and non-
linearity (concavity) constraints required by microeconomic theory can be easily
imposed using GME methods. The regularity of the estimated import demand
models means that they can be used in an AGE modelling framework. With
GME estimators, no assumptions about the error structure of the model need
be made and the estimator is more robust and efficient than other (Golan 1996).
The data used in the estimation of the cereals and electronics goods models
are taken from the UN COMTRADE database SITC rev. 3 and are aggregated
to the level of GTAP Sectoral Classifications such that parameter estimates can
be used in GTAP model. Usually, PE and AGE modellers prefer to use pa-
rameters and trade elasticities from the economic literature. These parameters
may have been estimated at very different product and regional aggregation
levels than those that apply in the model to which they are being applied. This
could lead to substantial inaccuracies when applying AGE models to address
policy issues. These inaccuracies could be ones of trade flow magnitudes and
directions.
As expected, our results shows negative Hicksian own price elasticities. The
Hicksian cross price elasticites of import demand between pair of goods from
different countries may differ in magnitude but must have the same sign. In
other words, for a representative consumer in Ireland, two sources of import
supply (such as the US and UK) are either substitutes or complements. The
estimated expenditure elasticites have a range of values, some being greater
than 1, others less than 1, while some estimated elasticities are even negative.
These cross price and expenditure elasticity results are in stark contrast with
what would emerge using an Armington specification. With an Armington
specification cross price elasticities are always constrained to be positive and
the expenditure elasticities must be 1.
It can be seen that the calculated price elasticites of the AIDS specifica-
tion of the estimated import demand models for electronic goods are highly
23
dependent on the import shares. In general, the results show that countries
with lower import shares tend to have higher elasticities and that conversely,
countries with higher import shares tend to have lower elasticities. This is an
important result for countries with small import shares. These countries will
gain (lose) hugely if their product is substitutable (complementary) to the coun-
try that increases its price. It is also seen that relatively heterogenous products
(electronics goods) results in more complementary relationships between pairs
of countries than homogeneous goods (cereals). This is to be expected as elec-
tronics goods encompass a wide range of products from simple products like
diodes, transistors etc. to sophisticated products like photocopying machines.
However, these estimated parameters have to be interpreted with caution as
t-statistics are calculated using asymptotic standard errors and only about one
third of the parameters are statistically signficant.
24
REFERENCE
Alston, J.M., Carter C.S., Green R., and Pick D., “Whither Armington trade
model?”, American Journal of Agricultural Economics, Vol 72, No.2, May 1990,
455-467
Armington P.S., “A Theory of Demand for products distinguished by place
of production”, IMF Staff papers, Vol. XVI, March 1969, 159-178
Armington P.S., “The Geographic pattern of trade and the effects of price
changes”, IMF Staff papers, Vol.XVI, July 1969, 177-199
Arrow, K.J., Chenery, H.B., Minhas B.S. and Solow, R.M.,“Capital-Labor Sub-
stitution and Economic Efficiency”, The Review of Economics and Statistics,
Vol.43, No.3, 1961, 225-250.
Cranfield, J.A.L. Pellow, Scott, “The role of global vs local negativity in func-
tional form selection: An application to Canadian consumer demands”, Eco-
nomic Modelling, Vol.21, No. 2, 2004, 249-263
Deaton and Muellbauer, “An Almost Ideal Demand System”, American Eco-
nomic Review, Vol. 70, No. 3, 1980, 312-326
Golan A, Perloff J M and Shen E Z, “Estimating a Demand System with Non-
negativity constraints: Mexican meat demand”, The Review of Economics and
Statistics, Vol. 83, No.3, Aug 2001, pp. 541-550
Golan A, Judge G and Miller D, Maximum Entropy Econometrics: Robust
estimation with limited data, John Wiley and Sons, 1996
Gohin A and Femenia F, “Estimating Price Elasticities of Food Trade Func-
tions; How Relevant is the CES-based Gravity Approach?”, Journal of Agricul-
tural Economics, Vol. 60, No. 2, 2009, 253-272
25
Hertel, T., Global Trade Analysis: Modeling and Applications, Cambridge
Press, 1997
Lau, L.J., “Testing and Imposing Monotonicity, Convexity and Quasiconvex-
ity” in Production Economics: A Dual Approach to theory and applications,
Vol 1, ed by M. Fuss and D. McFadden, Amsterdam North-Holland, 1978, 269-
286.
Moshini, G., “The semiflexible almost ideal demand system”, European Eco-
nomic Review, Vol.42, 1998, 349-364
Philips L, Applied Consumption Analysis, North-Holland, 1983
Ryan, D.L. and T. J. Wales, “A Simple Method for Imposing Local Curva-
ture in some Flexible Consumer Demand Systems”, Journal of Business and
Economic Statistics, Vol. 16, No. 3, 1998, 331-338.
Winters L A, “Separability and the specification of foreign trade functions”,
Journal of International Economics, Vol. 17, 1984, 239-263
26
Appendix:I
The following are the conditions required to ensure that GME estimator is
consistent and asymptotically normal.
• The error support v for eachi equation are symmetric around zero.
• The support space X spans the true values for each one of the unknown
parameters and has finite lower and upper bounds.
• The errors are independently and identically distributed for each of the
equations with mean zero and contemporaneous n×n variance-covariance
matrix,∑
, of the vector of disturbance for the set of share equations such
that .
• Exists and is nonsingular, where X is a block diagonal matrix consisting
X1, X2, ....X10.
Under these assumptions, the distribution of the GME estimator will be
β v N(β, (X ′(∑−1
⊗ IT )X)−1)
27
Appendix: II
Table 3: Alpha, Gamma and Beta Parameters for Bovine
Importing Country: DEDC
Alpha Gamma Beta
REU15 RWorld UK USA
REU15 1.008∗ -0.187∗ -0.031 -0.014 0.232∗∗ -0.172∗
(0.317) (0.049) (0.054) (0.823) (0.126) (0.024)
RWorld 0.240∗ -0.031 -0.047 -0.003 0.081 -0.036
(0.021) (0.034) (0.051) (0.055) (0.088) (0.022)
UK 0.112∗ -0.014 -0.003 -0.009 0.026 -0.020
(0.024) (0.704) (0.108) (0.063) (0.309) (0.408)
USA -0.359∗ 0.232∗ 0.081 0.026 -0.339∗ 0.229∗
(0.023) (0.047) (0.076) (0.060) (0.021) (0.033)
Importing Country: REU15
Alpha Gamma Beta
DEDC RWorld UK USA
DEDC -0.447 -0.100 -0.110 -0.102 0.312∗ 0.058
(1.372) (0.071) (0.242) (2.024) (0.105) (0.150)
RWorld -1.082∗ -0.110 -1.012∗ 0.501∗ 0.620∗ 0.164
(0.169) (0.140) (0.329) (0.249) (0.206) (0.205)
UK 0.030 -0.102 0.501∗ -0.344∗ -0.055 0.010
(0.116) (2.875) (0.149) (0.170) (1.787) (0.093)
USA 2.499∗ 0.312 0.620∗ -0.055 -0.877∗ -0.232
(0.157) (0.353) (0.293) (0.232) (0.220) (0.182)
Importing Country: RWorld
Alpha Gamma Beta
DEDC REU15 UK USA
DEDC -0.709 -0.235 0.073 0.016 0.146∗ 0.102
(1.144) (0.299) (0.158) (0.183) (0.048) (0.154)
REU15 1.285∗ 0.073 -0.040 0.005 -0.038∗ -0.119∗
(0.330) (0.103) (0.046) (0.053) (0.017) (0.045)
UK 0.245 0.016 0.005 -0.022 0.001 -0.024
(0.149) (1.210) (0.317) (0.024) (1.181) (0.309)
USA 0.180∗ 0.146 -0.038 0.001 -0.109 0.041
(0.044) (0.350) (0.109) (0.007) (0.341) (0.107)
Importing Country: UK
Alpha Gamma Beta
DEDC REU15 RWorld USA
DEDC -0.147 0.050 -0.044 -0.033 0.027 0.037
(1.517) (0.177) (0.362) (1.154) (0.135) (0.086)
REU15 0.448∗ -0.044 -0.038 -0.071 0.152 0.035
(0.187) (0.168) (0.186) (0.142) (0.128) (0.045)
RWorld -1.187∗ -0.033 -0.071 -0.238 0.342 0.156
(0.187) (2.939) (0.343) (0.142) (0.701) (0.082)
USA 1.886∗ 0.027 0.152 0.342∗ -0.520∗ -0.228∗
(0.096) (0.362) (0.325) (0.073) (0.086) (0.078)
Importing Country: USA
28
Alpha Gamma Beta
DEDC REU15 RWorld UK
DEDC -0.296 -0.197∗ 0.242∗ -0.126 0.080∗∗ 0.163∗
(0.967) (0.059) (0.053) (0.790) (0.048) (0.016)
REU15 1.272∗ 0.242∗ -0.268∗ 0.062 -0.037 -0.169∗
(0.143) (0.112) (0.107) (0.117) (0.092) (0.032)
RWorld -0.162∗ -0.126 0.062 0.072 -0.009 0.028∗
(0.072) (0.717) (0.044) (0.059) (0.214) (0.013)
UK 0.186 0.080 -0.037 -0.009 -0.035 -0.022
(0.145) (0.106) (0.083) (0.118) (0.032) (0.025)
Values in the parentheses are asymptotic standard errors.
* Significantly different from zero at the 5 % level of significance (critical value +/- 1.96).
** Significantly different from zero at the 10 % level of significance (critical value +/- 1.645).
29
Table
4:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
Brazil
Alp
ha
Gam
ma
Beta
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Chin
a-0
.605
-0.1
11
0.0
92
0.0
08
-0.0
31
-0.0
01
-0.0
13
-0.0
38
-0.1
98
0.0
06
0.2
87
0.1
02
(1.7
27)
(0.1
27)
(0.1
09)
(0.0
28)
(0.0
53)
(0.0
32)
(0.2
08)
(0.0
76)
(0.1
34)
(0.1
38)
(0.2
69)
(0.1
69)
DED
C0.2
29
0.0
92
-0.2
62∗
-0.0
15
-0.0
12
-0.0
26
0.0
18
0.0
04
0.3
01∗
0.0
41
-0.1
41
-0.1
76∗∗
(1.0
40)
(0.0
76)
(0.0
66)
(0.0
17)
(0.0
32)
(0.0
19)
(0.1
25)
(0.0
46)
(0.0
81)
(0.0
83)
(0.1
62)
(0.1
02)
India
0.2
03
0.0
08
-0.0
15
-0.0
08∗
-0.0
07
-0.0
04
0.0
03
-0.0
04
0.0
46∗
0.0
39∗
-0.0
58
-0.0
38∗∗
(0.2
25)
(0.0
17)
(0.0
14)
(0.0
04)
(0.0
07)
(0.0
04)
(0.0
27)
(0.0
10)
(0.0
17)
(0.0
18)
(0.0
35)
(0.0
22)
Irela
nd
0.1
22
-0.0
31
-0.0
12
-0.0
07
-0.0
27∗
-0.0
06
-0.0
02
-0.0
17
0.0
59∗
0.0
46
-0.0
03
-0.0
24
(0.3
25)
(0.0
24)
(0.0
21)
(0.0
05)
(0.0
10)
(0.0
06)
(0.0
39)
(0.0
14)
(0.0
25)
(0.0
26)
(0.0
51)
(0.0
32)
LD
C0.1
10
-0.0
01
-0.0
26∗∗
-0.0
04
-0.0
06
-0.0
05
0.0
02
-0.0
02
0.0
19
0.0
13
0.0
09
-0.0
23
(0.2
37)
(0.0
17)
(0.0
15)
(0.0
04)
(0.0
07)
(0.0
04)
(0.0
29)
(0.0
10)
(0.0
18)
(0.0
19)
(0.0
37)
(0.0
23)
REU
15
0.0
06
-0.0
13
0.0
18
0.0
03
-0.0
02
0.0
02
0.0
07
0.0
20
-0.0
10
-0.0
07
-0.0
17
0.0
20
(0.5
80)
(0.0
43)
(0.0
37)
(0.0
09)
(0.0
18)
(0.0
11)
(0.0
70)
(0.0
26)
(0.0
45)
(0.0
47)
(0.0
90)
(0.0
57)
REU
27
-0.0
18
-0.0
38∗∗
0.0
04
-0.0
04
-0.0
17∗∗
-0.0
02
0.0
20
-0.0
13
0.0
04
0.0
27
0.0
20
-0.0
02
(0.2
99)
(0.0
22)
(0.0
19)
(0.0
05)
(0.0
09)
(0.0
06)
(0.0
36)
(0.0
13)
(0.0
23)
(0.0
24)
(0.0
47)
(0.0
29)
RW
orld
-1.5
85
-0.1
98∗
0.3
01∗
0.0
46∗
0.0
59
0.0
19
-0.0
10
0.0
04
-0.5
69∗
-0.3
06∗
0.6
54∗
0.2
97∗
(1.1
12)
(0.0
82)
(0.0
70)
(0.0
18)
(0.0
34)
(0.0
21)
(0.1
34)
(0.0
49)
(0.0
86)
(0.0
89)
(0.1
73)
(0.1
09)
UK
-0.9
02
0.0
06
0.0
41
0.0
39∗
0.0
46∗
0.0
13
-0.0
07
0.0
27
-0.3
06∗
-0.2
55∗
0.3
96∗
0.1
68∗
(0.6
49)
(0.0
48)
(0.0
41)
(0.0
10)
(0.0
20)
(0.0
12)
(0.0
78)
(0.0
29)
(0.0
50)
(0.0
52)
(0.1
01)
(0.0
64)
USA
2.4
40
0.2
87∗
-0.1
41
-0.0
58∗
-0.0
03
0.0
09
-0.0
17
0.0
20
0.6
54∗
0.3
96∗
-1.1
48∗
-0.3
24∗
(1.5
84)
(0.1
16)
(0.1
00)
(0.0
25)
(0.0
49)
(0.0
29)
(0.1
91)
(0.0
70)
(0.1
23)
(0.1
27)
(0.2
47)
(0.1
55)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
30
Table
5:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
Chin
a
Alp
ha
Gam
ma
Beta
Brazil
DED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Brazil
0.0
26
-0.0
03
-0.0
17
0.0
00
0.0
02
0.0
03
0.0
07
0.0
02
0.0
06
0.0
02
0.0
00
-0.0
07
(0.1
72)
(0.0
03)
(0.0
13)
(0.0
03)
(0.0
01)
(0.0
02)
(0.0
05)
(0.0
04)
(0.0
17)
(0.0
08)
(0.0
14)
(0.0
09)
DED
C1.5
31∗
-0.0
17∗∗
-0.2
31∗
-0.0
22∗
0.0
58∗
0.0
00
-0.1
17∗
-0.0
22
0.4
53∗
-0.0
03
-0.0
99∗∗
-0.1
94∗
(0.6
67)
(0.0
10)
(0.0
52)
(0.0
10)
(0.0
05)
(0.0
09)
(0.0
18)
(0.0
14)
(0.0
64)
(0.0
32)
(0.0
53)
(0.0
36)
India
0.0
98
0.0
00
-0.0
22
-0.0
03
0.0
05∗
0.0
01
-0.0
10∗∗
0.0
00
0.0
41∗
-0.0
03
-0.0
09
-0.0
13
(0.2
12)
(0.0
03)
(0.0
17)
(0.0
03)
(0.0
02)
(0.0
03)
(0.0
06)
(0.0
04)
(0.0
20)
(0.0
10)
(0.0
17)
(0.0
12)
Irela
nd
-0.2
57
0.0
02
0.0
58
0.0
05
-0.0
14∗
-0.0
06
0.0
25∗
-0.0
01
-0.0
94∗
0.0
07
0.0
19
0.0
39
(0.4
13)
(0.0
06)
(0.0
32)
(0.0
06)
(0.0
03)
(0.0
06)
(0.0
11)
(0.0
09)
(0.0
40)
(0.0
20)
(0.0
33)
(0.0
22)
LD
C-0
.087
0.0
03
0.0
00
0.0
01
-0.0
06
-0.0
11∗
-0.0
01∗
-0.0
08
0.0
14
0.0
05
0.0
03
0.0
20
(0.3
48)
(0.0
05)
(0.0
27)
(0.0
05)
(0.0
03)
(0.0
05)
(0.0
09)
(0.0
07)
(0.0
34)
(0.0
17)
(0.0
28)
(0.0
19)
REU
15
0.6
34
0.0
07
-0.1
17∗
-0.0
10
0.0
25∗
-0.0
01
-0.0
26
-0.0
12
0.1
75∗
-0.0
05
-0.0
36
-0.0
90∗
(0.7
32)
(0.0
11)
(0.0
57)
(0.0
11)
(0.0
06)
(0.0
10)
(0.0
19)
(0.0
15)
(0.0
71)
(0.0
35)
(0.0
58)
(0.0
40)
REU
27
-0.0
03
0.0
02
-0.0
22
0.0
00
-0.0
01
-0.0
08∗∗
-0.0
12
-0.0
07
0.0
44
0.0
07
-0.0
02
0.0
07
(0.3
42)
(0.0
05)
(0.0
27)
(0.0
05)
(0.0
03)
(0.0
05)
(0.0
09)
(0.0
07)
(0.0
33)
(0.0
17)
(0.0
27)
(0.0
19)
RW
orld
-1.5
67
0.0
06
0.4
53∗
0.0
41∗
-0.0
94∗
0.0
14
0.1
75∗
0.0
44∗
-0.7
75∗
0.0
04
0.1
33
0.3
30∗
(1.0
01)
(0.0
15)
(0.0
78)
(0.0
15)
(0.0
08)
(0.0
13)
(0.0
27)
(0.0
21)
(0.0
96)
(0.0
48)
(0.0
80)
(0.0
54)
UK
0.1
15
0.0
02
-0.0
03
-0.0
03
0.0
07∗
0.0
05
-0.0
05
0.0
07
0.0
04
-0.0
08
-0.0
04
-0.0
20
(0.3
07)
(0.0
05)
(0.0
24)
(0.0
05)
(0.0
02)
(0.0
04)
(0.0
08)
(0.0
06)
(0.0
30)
(0.0
15)
(0.0
24)
(0.0
17)
USA
0.5
10
0.0
00
-0.0
99∗
-0.0
09
0.0
19∗
0.0
03
-0.0
36∗
-0.0
02
0.1
33∗
-0.0
04
-0.0
05
-0.0
72∗
(0.6
44)
(0.0
10)
(0.0
50)
(0.0
10)
(0.0
05)
(0.0
09)
(0.0
17)
(0.0
14)
(0.0
62)
(0.0
31)
(0.0
51)
(0.0
35)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
31
Table
6:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
DED
C
Alp
ha
Gam
ma
Beta
Brazil
Chin
aIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Brazil
0.3
63
-0.0
40∗
0.1
21∗
-0.0
52∗
-0.0
17
0.0
20
0.0
25
0.0
13
-0.0
06
0.0
10
-0.0
74∗∗
-0.0
56
(0.4
96)
(0.0
12)
(0.0
21)
(0.0
16)
(0.0
18)
(0.0
15)
(0.0
33)
(0.0
08)
(0.0
29)
(0.0
34)
(0.0
41)
(0.0
49)
Chin
a-2
.082
0.1
21∗
-0.5
20∗
0.2
04∗
0.0
52
-0.0
71∗∗
-0.0
40
-0.0
70∗
-0.2
06∗
-0.0
16
0.5
47∗
0.3
40∗
(1.4
01)
(0.0
33)
(0.0
58)
(0.0
46)
(0.0
51)
(0.0
42)
(0.0
95)
(0.0
22)
(0.0
83)
(0.0
95)
(0.1
17)
(0.1
39)
India
0.6
93
-0.0
52∗
0.2
04∗
-0.1
03∗
-0.0
37
0.0
38∗
0.0
22
0.0
22∗
0.0
56
0.0
09
-0.1
58∗
-0.1
03∗∗
(0.6
16)
(0.0
14)
(0.0
26)
(0.0
20)
(0.0
23)
(0.0
19)
(0.0
42)
(0.0
10)
(0.0
36)
(0.0
42)
(0.0
52)
(0.0
61)
Irela
nd
0.2
17
-0.0
17∗
0.0
52∗
-0.0
37∗
-0.0
26∗
0.0
13
0.0
20
0.0
08
0.0
32∗
0.0
07
-0.0
53∗
-0.0
28
(0.2
63)
(0.0
06)
(0.0
11)
(0.0
09)
(0.0
10)
(0.0
08)
(0.0
18)
(0.0
04)
(0.0
16)
(0.0
18)
(0.0
22)
(0.0
26)
LD
C-0
.238
0.0
20∗
-0.0
71∗
0.0
38∗
0.0
13
-0.0
31∗
-0.0
02
-0.0
08
-0.0
44∗
-0.0
09
0.0
93∗
0.0
33
(0.3
63)
(0.0
09)
(0.0
15)
(0.0
12)
(0.0
13)
(0.0
11)
(0.0
25)
(0.0
06)
(0.0
22)
(0.0
25)
(0.0
30)
(0.0
36)
REU
15
0.0
03
0.0
25∗
-0.0
40∗
0.0
22∗
0.0
20
-0.0
02
-0.0
04
-0.0
06
-0.0
25
-0.0
26
0.0
36
0.0
14
(0.3
34)
(0.0
08)
(0.0
14)
(0.0
11)
(0.0
12)
(0.0
10)
(0.0
23)
(0.0
05)
(0.0
20)
(0.0
23)
(0.0
28)
(0.0
33)
REU
27
-0.2
52∗∗
0.0
13∗
-0.0
70∗
0.0
22∗
0.0
08
-0.0
08∗∗
-0.0
06
-0.0
13∗
-0.0
27∗
0.0
00
0.0
81∗
0.0
38∗
(0.1
46)
(0.0
03)
(0.0
06)
(0.0
05)
(0.0
05)
(0.0
04)
(0.0
10)
(0.0
02)
(0.0
09)
(0.0
10)
(0.0
12)
(0.0
15)
RW
orld
-0.3
88
-0.0
06
-0.2
06∗
0.0
56
0.0
32
-0.0
44
-0.0
25
-0.0
27
-0.0
27
0.0
35
0.2
11∗
0.1
10
(1.0
60)
(0.0
25)
(0.0
44)
(0.0
35)
(0.0
39)
(0.0
32)
(0.0
72)
(0.0
17)
(0.0
63)
(0.0
72)
(0.0
89)
(0.1
05)
UK
0.0
36
0.0
10
-0.0
16
0.0
09
0.0
07
-0.0
09
-0.0
26
0.0
00
0.0
35
-0.0
31
0.0
20
0.0
01
(0.3
61)
(0.0
08)
(0.0
15)
(0.0
12)
(0.0
13)
(0.0
11)
(0.0
247)
(0.0
06)
(0.0
21)
(0.0
25)
(0.0
30)
(0.0
36)
USA
2.6
49∗
-0.0
74∗
0.5
47∗
-0.1
58∗
-0.0
53∗∗
0.0
93∗
0.0
36
0.0
81∗
0.2
11∗
0.0
20
-0.7
02∗
-0.3
48∗
(0.8
00)
(0.0
19)
(0.0
33)
(0.0
27)
(0.0
29)
(0.0
24)
(0.0
54)
(0.0
13)
(0.0
47)
(0.0
54)
(0.0
67)
(0.0
80)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
32
Table
7:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
India
Alp
ha
Gam
ma
Beta
Brazil
Chin
aD
ED
CIr
ela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Brazil
0.0
06
-0.0
02
0.0
00
-0.0
04
-0.0
02
0.0
02
-0.0
04
0.0
00
0.0
13
-0.0
02
-0.0
01
0.0
01
(0.0
51)
(0.0
02)
(0.0
06)
(0.0
04)
(0.0
03)
(0.0
02)
(0.0
03)
(0.0
02)
(0.0
09)
(0.0
04)
(0.0
09)
(0.0
04)
Chin
a-1
.513∗
0.0
00
-0.4
68∗
0.2
10∗
-0.0
84∗
-0.0
02
-0.0
11
-0.0
60∗
0.1
30∗
0.0
69∗
0.2
16∗
0.2
76∗
(0.3
81)
(0.0
15)
(0.0
42)
(0.0
31)
(0.0
24)
(0.0
12)
(0.0
24)
(0.0
14)
(0.0
64)
(0.0
32)
(0.0
67)
(0.0
30)
DED
C0.9
50∗
-0.0
04
0.2
10∗
-0.1
16∗
0.0
13
0.0
18
-0.0
17
0.0
03
-0.0
46
-0.0
19
-0.0
42
-0.1
37∗
(0.3
74)
(0.0
15)
(0.0
41)
(0.0
31)
(0.0
24)
(0.0
12)
(0.0
23)
(0.0
14)
(0.0
63)
(0.0
31)
(0.0
65)
(0.0
30)
Irela
nd
-0.1
92
-0.0
02
-0.0
84∗
0.0
13
-0.0
16
0.0
03
0.0
03
-0.0
14∗∗
0.0
67
0.0
12
0.0
19
0.0
33
(0.2
15)
(0.0
09)
(0.0
24)
(0.0
18)
(0.0
14)
(0.0
07)
(0.0
13)
(0.0
08)
(0.0
36)
(0.0
18)
(0.0
38)
(0.0
17)
LD
C0.0
15
0.0
02
-0.0
02
0.0
18∗
0.0
03
-0.0
05
-0.0
05
0.0
00
-0.0
04
-0.0
05
-0.0
02
-0.0
04
(0.0
97)
(0.0
04)
(0.0
11)
(0.0
08)
(0.0
06)
(0.0
03)
(0.0
06)
(0.0
04)
(0.0
16)
(0.0
08)
(0.0
17)
(0.0
08)
REU
15
0.0
80
-0.0
04
-0.0
11
-0.0
17
0.0
03
-0.0
05
0.0
31∗
-0.0
03
0.0
03
0.0
04
0.0
00
0.0
01
(0.2
37)
(0.0
10)
(0.0
26)
(0.0
20)
(0.0
15)
(0.0
08)
(0.0
15)
(0.0
09)
(0.0
40)
(0.0
20)
(0.0
42)
(0.0
19)
REU
27
-0.1
59
0.0
00
-0.0
60∗
0.0
03
-0.0
14
0.0
00
-0.0
03
-0.0
12∗∗
0.0
59∗
0.0
06
0.0
22
0.0
29∗
(0.1
76)
(0.0
07)
(0.0
19)
(0.0
15)
(0.0
11)
(0.0
06)
(0.0
11)
(0.0
06)
(0.0
30)
(0.0
15)
(0.0
31)
(0.0
14)
RW
orld
0.7
02
0.0
13
0.1
30∗
-0.0
46
0.0
67∗
-0.0
04
0.0
03
0.0
59∗
-0.1
32∗∗
-0.0
34
-0.0
56
-0.0
50
(0.4
48)
(0.0
18)
(0.0
49)
(0.0
37)
(0.0
29)
(0.0
14)
(0.0
28)
(0.0
16)
(0.0
75)
(0.0
37)
(0.0
78)
(0.0
36)
UK
0.2
11
-0.0
02
0.0
69∗
-0.0
19∗∗
0.0
12
-0.0
05
0.0
04
0.0
06
-0.0
34
-0.0
01
-0.0
32
-0.0
30∗
(0.1
33)
(0.0
05)
(0.0
15)
(0.0
11)
(0.0
08)
(0.0
04)
(0.0
08)
(0.0
05)
(0.0
22)
(0.0
11)
(0.0
23)
(0.0
11)
USA
0.8
99∗
-0.0
01
0.2
16∗
-0.0
42∗
0.0
19
-0.0
02
0.0
00
0.0
22∗
-0.0
56
-0.0
32
-0.1
25∗
-0.1
18∗
(0.2
34)
(0.0
09)
(0.0
26)
(0.0
19)
(0.0
15)
(0.0
07)
(0.0
15)
(0.0
09)
(0.0
39)
(0.0
20)
(0.0
41)
(0.0
19)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
33
Table
8:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
Irela
nd
Alp
ha
Gam
ma
Beta
Brazil
Chin
aD
ED
CIn
dia
LD
CR
EU
15
REU
27
RW
orld
UK
USA
Brazil
0.0
34
-0.0
15∗
0.0
19
-0.0
09
-0.0
01
-0.0
08∗∗
-0.0
10
0.0
12
-0.0
03
-0.0
12
0.0
27
-0.0
11
(0.1
28)
(0.0
06)
(0.0
13)
(0.0
18)
(0.0
05)
(0.0
05)
(0.0
11)
(0.0
07)
(0.0
20)
(0.0
11)
(0.0
21)
(0.0
14)
Chin
a-0
.594
0.0
19
-0.2
04∗
0.1
99
-0.0
07
0.0
56
-0.0
63
-0.0
46
-0.2
38
0.0
29
0.2
56
0.2
25∗
(1.0
03)
(0.0
50)
(0.1
03)
(0.1
43)
(0.0
35)
(0.0
39)
(0.0
84)
(0.0
54)
(0.1
54)
(0.0
88)
(0.1
66)
(0.1
07)
DED
C0.7
43
-0.0
09
0.1
99∗
-0.1
25∗∗
0.0
12
-0.0
32
-0.0
15
0.0
56∗
0.1
98∗
-0.0
37
-0.2
48∗
-0.2
27∗
(0.5
08)
(0.0
25)
(0.0
52)
(0.0
73)
(0.0
18)
(0.0
20)
(0.0
43)
(0.0
27)
(0.0
78)
(0.0
45)
(0.0
84)
(0.0
54)
India
-0.0
19
-0.0
01
-0.0
07
0.0
12
-0.0
06
0.0
04
-0.0
07
-0.0
05
0.0
07
-0.0
20∗∗
0.0
22
0.0
05
(0.1
18)
(0.0
06)
(0.0
12)
(0.0
17)
(0.0
04)
(0.0
05)
(0.0
10)
(0.0
06)
(0.0
18)
(0.0
10)
(0.0
20)
(0.0
13)
LD
C0.1
05
-0.0
08
0.0
56
-0.0
32
0.0
04
-0.0
21∗∗
0.0
34
0.0
11
0.0
09
0.0
08
-0.0
62
-0.0
30
(0.2
95)
(0.0
15)
(0.0
30)
(0.0
42)
(0.0
10)
(0.0
11)
(0.0
25)
(0.0
16)
(0.0
45)
(0.0
26)
(0.0
49)
(0.0
32)
REU
15
0.0
16
-0.0
10
-0.0
63
-0.0
15
-0.0
07
0.0
34∗
-0.0
46
-0.0
05
-0.0
09
-0.0
54
0.1
74∗
0.0
38
(0.4
25)
(0.0
21)
(0.0
44)
(0.0
61)
(0.0
15)
(0.0
16)
(0.0
36)
(0.0
23)
(0.0
65)
(0.0
37)
(0.0
70)
(0.0
45)
REU
27
-0.1
36
0.0
12
-0.0
46∗
0.0
56∗
-0.0
05
0.0
11
-0.0
05
-0.0
22
-0.0
39
0.0
10
0.0
28
0.0
51∗
(0.1
58)
(0.0
08)
(0.0
16)
(0.0
23)
(0.0
06)
(0.0
06)
(0.0
13)
(0.0
09)
(0.0
24)
(0.0
14)
(0.0
26)
(0.0
17)
RW
orld
-0.5
02
-0.0
03
-0.2
38∗
0.1
98
0.0
07
0.0
09
-0.0
09
-0.0
39
-0.1
53
0.0
50
0.1
78
0.2
68∗
(0.7
31)
(0.0
37)
(0.0
75)
(0.1
04)
(0.0
26)
(0.0
28)
(0.0
62)
(0.0
39)
(0.1
13)
(0.0
64)
(0.1
21)
(0.0
78)
UK
0.1
71
-0.0
12
0.0
29
-0.0
37
-0.0
20
0.0
08
-0.0
54
0.0
10
0.0
50
0.0
11
0.0
15
0.0
27
(0.9
18)
(0.0
46)
(0.0
95)
(0.1
31)
(0.0
33)
(0.0
35)
(0.0
77)
(0.0
49)
(0.1
41)
(0.0
81)
(0.1
52)
(0.0
98)
USA
1.1
80∗
0.0
27
0.2
56∗
-0.2
48∗
0.0
22
-0.0
62∗
0.1
74∗
0.0
28
0.1
78∗
0.0
15
-0.3
89∗
-0.3
45∗
(0.5
48)
(0.0
27)
(0.0
56)
(0.0
78)
(0.0
19)
(0.0
21)
(0.0
46)
(0.0
29)
(0.0
84)
(0.0
48)
(0.0
91)
(0.0
59)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
34
Table
9:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
LD
C
Alp
ha
Gam
ma
Beta
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
REU
15
REU
27
RW
orld
UK
USA
Brazil
0.3
23∗
-0.0
27∗
0.0
98∗
-0.1
23∗
0.0
17
-0.0
11∗
0.0
06
0.0
54∗
0.0
76∗
-0.0
50∗
-0.0
40∗
-0.0
43∗
(0.0
95)
(0.0
06)
(0.0
11)
(0.0
09)
(0.0
09)
(0.0
05)
(0.0
23)
(0.0
05)
(0.0
15)
(0.0
16)
(0.0
14)
(0.0
09)
Chin
a-1
.745∗
0.0
98∗
-0.4
21∗
0.5
84∗
-0.0
85∗
-0.0
26∗
-0.0
20
-0.3
14∗
-0.3
36∗
0.2
63∗
0.2
58∗
0.2
53∗
(0.2
43)
(0.0
14)
(0.0
28)
(0.0
24)
(0.0
23)
(0.0
12)
(0.0
59)
(0.0
13)
(0.0
39)
(0.0
41)
(0.0
34)
(0.0
22)
DED
C2.5
51∗
-0.1
23∗
0.5
84∗
-0.7
67∗
0.0
88∗
0.0
80∗
0.0
36
0.3
85∗
0.4
28∗
-0.3
53∗
-0.3
57∗
-0.3
34∗
(0.1
50)
(0.0
09)
(0.0
18)
(0.0
15)
(0.0
14)
(0.0
07)
(0.0
37)
(0.0
08)
(0.0
24)
(0.0
25)
(0.0
21)
(0.0
14)
India
-0.2
88∗
0.0
17∗
-0.0
85∗
0.0
88∗
-0.0
26∗
0.0
09∗
-0.0
08
-0.0
58∗
-0.0
59∗
0.0
65∗
0.0
57∗
0.0
38∗
(0.0
90)
(0.0
05)
(0.0
11)
(0.0
09)
(0.0
08)
(0.0
04)
(0.0
22)
(0.0
05)
(0.0
14)
(0.0
15)
(0.0
13)
(0.0
08)
Irela
nd
-0.1
88
-0.0
11
-0.0
26∗∗
0.0
80∗
0.0
09
-0.0
10∗∗
0.0
04
-0.0
52∗
-0.0
32∗∗
0.0
08
0.0
30
0.0
43∗
(0.1
17)
(0.0
07)
(0.0
14)
(0.0
12)
(0.0
11)
(0.0
06)
(0.0
29)
(0.0
06)
(0.0
19)
(0.0
20)
(0.0
17)
(0.0
11)
REU
15
-0.0
30
0.0
06
-0.0
20∗
0.0
36∗
-0.0
08
0.0
04
-0.0
08
-0.0
35∗
-0.0
13
0.0
17
0.0
21
0.0
02
(0.0
77)
(0.0
04)
(0.0
09)
(0.0
08)
(0.0
07)
(0.0
04)
(0.0
19)
(0.0
04)
(0.0
12)
(0.0
13)
(0.0
11)
(0.0
07)
REU
27
-0.7
75∗
0.0
54∗
-0.3
14∗
0.3
85∗
-0.0
58∗
-0.0
52∗
-0.0
35
-0.1
48∗
-0.1
69∗
0.1
66∗
0.1
71∗
0.1
76∗
(0.1
26)
(0.0
07)
(0.0
15)
(0.0
12)
(0.0
12)
(0.0
06)
(0.0
31)
(0.0
07)
(0.0
20)
(0.0
21)
(0.0
18)
(0.0
12)
RW
orld
-1.2
60∗
0.0
76∗
-0.3
36∗
0.4
28∗
-0.0
59∗
-0.0
32∗
-0.0
13
-0.1
69∗
-0.2
67∗
0.2
03∗
0.1
69∗
0.1
78∗
(0.2
13)
(0.0
12)
(0.0
25)
(0.0
21)
(0.0
20)
(0.0
10)
(0.0
52)
(0.0
11)
(0.0
34)
(0.0
36)
(0.0
30)
(0.0
20)
UK
1.2
15∗
-0.0
50∗
0.2
63∗
-0.3
53∗
0.0
65∗
0.0
08
0.0
17
0.1
66∗
0.2
03∗
-0.1
59∗
-0.1
60∗
-0.1
57∗
(0.1
46)
(0.0
08)
(0.0
17)
(0.0
14)
(0.0
14)
(0.0
07)
(0.0
36)
(0.0
08)
(0.0
23)
(0.0
24)
(0.0
21)
(0.0
13)
USA
1.1
96∗
-0.0
40∗
0.2
58∗
-0.3
57∗
0.0
57∗
0.0
30∗
0.0
21
0.1
71∗
0.1
69∗
-0.1
60∗
-0.1
50∗
-0.1
55∗
(0.1
74)
(0.0
10)
(0.0
20)
(0.0
17)
(0.0
16)
(0.0
08)
(0.0
43)
(0.0
09)
(0.0
28)
(0.0
29)
(0.0
25)
(0.0
16)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
35
Table
10:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
REU
15
Alp
ha
Gam
ma
Beta
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
27
RW
orld
UK
USA
Brazil
-0.1
48
-0.0
19
-0.0
42
0.0
73∗
-0.0
06
-0.0
01
-0.0
08
-0.0
15∗
0.0
10
-0.0
08
0.0
17
0.0
19
(0.5
48)
(0.0
16)
(0.0
30)
(0.0
35)
(0.0
12)
(0.0
20)
(0.0
11)
(0.0
05)
(0.0
29)
(0.0
28)
(0.0
37)
(0.0
60)
Chin
a-2
.246
-0.0
42
-0.5
40∗
0.3
82∗
0.0
44
-0.0
61
0.0
08
-0.3
49∗
-0.1
13
0.2
21
0.4
51∗
0.3
22
(2.3
73)
(0.0
70)
(0.1
31)
(0.1
50)
(0.0
52)
(0.0
86)
(0.0
49)
(0.0
22)
(0.1
24)
(0.1
23)
(0.1
61)
(0.2
58)
DED
C1.7
83
0.0
73
0.3
82∗
-0.4
12∗
-0.0
19
0.0
47
0.0
41
0.2
64∗
-0.0
05
-0.1
76∗
-0.1
94∗∗
-0.2
21
(1.5
91)
(0.0
47)
(0.0
88)
(0.1
01)
(0.0
35)
(0.0
57)
(0.0
33)
(0.0
15)
(0.0
83)
(0.0
82)
(0.1
08)
(0.1
73)
India
0.1
78
-0.0
06
0.0
44
-0.0
19
-0.0
47∗
0.0
26
0.0
05
0.0
19∗
0.0
09
-0.0
29
-0.0
02
-0.0
29
(0.6
90)
(0.0
20)
(0.0
38)
(0.0
44)
(0.0
15)
(0.0
25)
(0.0
14)
(0.0
06)
(0.0
36)
(0.0
36)
(0.0
47)
(0.0
75)
Irela
nd
-0.1
80
-0.0
01
-0.0
61
0.0
47
0.0
26
0.0
00
0.0
00
-0.0
39∗
0.0
34
0.0
08
-0.0
13
0.0
34
(0.7
74)
(0.0
23)
(0.0
43)
(0.0
49)
(0.0
17)
(0.0
28)
(0.0
16)
(0.0
07)
(0.0
41)
(0.0
40)
(0.0
53)
(0.0
84)
LD
C0.0
23
-0.0
08
0.0
08
0.0
41
0.0
05
0.0
00
-0.0
12∗∗
0.0
03
-0.0
11
-0.0
06
-0.0
21
-0.0
02
(0.3
47)
(0.0
10)
(0.0
19)
(0.0
22)
(0.0
08)
(0.0
13)
(0.0
07)
(0.0
03)
(0.0
18)
(0.0
18)
(0.0
24)
(0.0
38)
REU
27
-1.2
70
-0.0
15
-0.3
49∗
0.2
64∗
0.0
19
-0.0
39
0.0
03
-0.2
06∗
-0.0
73
0.1
42∗
0.2
53∗
0.1
76
(1.0
72)
(0.0
32)
(0.0
59)
(0.0
68)
(0.0
23)
(0.0
39)
(0.0
22)
(0.0
10)
(0.0
56)
(0.0
56)
(0.0
73)
(0.1
17)
RW
orld
-0.1
03
0.0
10
-0.1
13
-0.0
05
0.0
09
0.0
34
-0.0
11
-0.0
73∗
0.0
01
0.0
81
0.0
67
0.0
53
(2.2
25)
(0.0
66)
(0.1
23)
(0.1
41)
(0.0
49)
(0.0
80)
(0.0
46)
(0.0
20)
(0.1
17)
(0.1
15)
(0.1
51)
(0.2
42)
UK
1.1
35
-0.0
08
0.2
21∗
-0.1
76
-0.0
29
0.0
08
-0.0
06
0.1
42∗
0.0
81
-0.0
52
-0.1
82
-0.1
38
(1.8
78)
(0.0
55)
(0.1
04)
(0.1
19)
(0.0
41)
(0.0
68)
(0.0
39)
(0.0
17)
(0.0
98)
(0.0
97)
(0.1
28)
(0.2
04)
USA
1.8
28
0.0
17
0.4
51∗
-0.1
94∗
-0.0
02
-0.0
13
-0.0
21
0.2
53∗
0.0
67
-0.1
82∗
-0.3
75∗
-0.2
13
(1.4
09)
(0.0
42)
(0.0
78)
(0.0
89)
(0.0
31)
(0.0
51)
(0.0
29)
(0.0
13)
(0.0
74)
(0.0
73)
(0.0
96)
(0.1
53)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
36
Table
11:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
REU
27
Alp
ha
Gam
ma
Beta
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
RW
orld
UK
USA
Brazil
-0.0
82
-0.0
04
-0.0
18
-0.0
06
-0.0
07
0.0
09
0.0
00
0.0
31∗
-0.0
28∗∗
0.0
05
0.0
19
0.0
04
(0.1
69)
(0.0
08)
(0.0
22)
(0.0
29)
(0.0
06)
(0.0
17)
(0.0
05)
(0.0
09)
(0.0
15)
(0.0
22)
(0.0
28)
(0.0
17)
Chin
a-3
.085∗
-0.0
18
-0.5
26∗
0.1
27
-0.1
30∗
-0.1
50∗
-0.0
86∗
0.6
83∗
-0.4
68∗
0.2
64∗
0.3
03∗
0.1
93∗
(0.7
11)
(0.0
32)
(0.0
91)
(0.1
21)
(0.0
25)
(0.0
71)
(0.0
21)
(0.0
40)
(0.0
65)
(0.0
93)
(0.1
16)
(0.0
70)
DED
C0.8
28∗
-0.0
06
0.1
27∗
-0.0
04
0.0
27∗
0.0
56∗
0.0
22∗
-0.1
54∗
0.0
53∗
-0.0
52∗
-0.0
68∗
-0.0
46∗
(0.1
51)
(0.0
07)
(0.0
19)
(0.0
26)
(0.0
05)
(0.0
15)
(0.0
05)
(0.0
08)
(0.0
14)
(0.0
20)
(0.0
25)
(0.0
15)
India
-0.6
90∗
-0.0
07
-0.1
30∗
0.0
27
-0.0
31∗
-0.0
26
-0.0
16∗∗
0.1
57∗
-0.1
06∗
0.0
60
0.0
72
0.0
41
(0.2
96)
(0.0
14)
(0.0
38)
(0.0
50)
(0.0
10)
(0.0
30)
(0.0
09)
(0.0
16)
(0.0
27)
(0.0
39)
(0.0
48)
(0.0
29)
Irela
nd
-0.7
72∗
0.0
09
-0.1
50∗
0.0
56∗
-0.0
26∗
-0.1
14∗
-0.0
26∗
0.1
81∗
-0.0
68∗
0.0
79∗
0.0
60∗
0.0
49∗
(0.1
48)
(0.0
07)
(0.0
19)
(0.0
25)
(0.0
05)
(0.0
15)
(0.0
04)
(0.0
08)
(0.0
14)
(0.0
20)
(0.0
24)
(0.0
15)
LD
C-0
.397
0.0
00
-0.0
86∗
0.0
22
-0.0
16∗∗
-0.0
26
-0.0
26∗
0.0
66∗
-0.0
50∗
0.0
51
0.0
66
0.0
23
(0.2
70)
(0.0
12)
(0.0
35)
(0.0
46)
(0.0
10)
(0.0
27)
(0.0
08)
(0.0
15)
(0.0
25)
(0.0
36)
(0.0
44)
(0.0
26)
REU
15
4.1
38∗
0.0
31
0.6
83∗
-0.1
54
0.1
57∗
0.1
81∗
0.0
66∗
-0.7
89∗
0.4
65∗
-0.3
07∗
-0.3
33∗
-0.2
24∗
(0.7
24)
(0.0
33)
(0.0
93)
(0.1
23)
(0.0
25)
(0.0
73)
(0.0
22)
(0.0
40)
(0.0
66)
(0.0
95)
(0.1
18)
(0.0
71)
RW
orld
-2.0
65∗
-0.0
28
-0.4
68∗
0.0
53
-0.1
06∗
-0.0
68
-0.0
50∗
0.4
65∗
-0.1
91∗
0.1
70
0.2
22
0.1
37
(0.7
68)
(0.0
35)
(0.0
99)
(0.1
31)
(0.0
27)
(0.0
77)
(0.0
23)
(0.0
43)
(0.0
70)
(0.1
01)
(0.1
25)
(0.0
75)
UK
1.4
27∗
0.0
05
0.2
64∗
-0.0
52
0.0
60∗
0.0
79∗
0.0
51∗
-0.3
07∗
0.1
70∗
-0.1
24∗
-0.1
45∗
-0.0
82∗
(0.2
81)
(0.0
13)
(0.0
36)
(0.0
48)
(0.0
10)
(0.0
28)
(0.0
08)
(0.0
16)
(0.0
26)
(0.0
37)
(0.0
46)
(0.0
28)
USA
1.6
98∗
0.0
19
0.3
03∗
-0.0
68
0.0
72∗
0.0
60∗
0.0
66∗
-0.3
33∗
0.2
22∗
-0.1
45∗
-0.1
96∗
-0.0
96∗
(0.2
65)
(0.0
12)
(0.0
34)
(0.0
45)
(0.0
09)
(0.0
27)
(0.0
08)
(0.0
15)
(0.0
24)
(0.0
35)
(0.0
43)
(0.0
26)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
37
Table
12:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
RW
orld
Alp
ha
Gam
ma
Beta
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
UK
USA
Brazil
0.4
71
-0.0
29∗
0.1
66∗
-0.0
55
-0.0
21
0.0
20
0.0
08
-0.0
48
0.0
43∗
-0.0
01
-0.0
83∗
-0.0
41∗∗
(0.3
59)
(0.0
11)
(0.0
26)
(0.0
34)
(0.0
18)
(0.0
14)
(0.0
12)
(0.0
43)
(0.0
10)
(0.0
59)
(0.0
19)
(0.0
24)
Chin
a-4
.326∗
0.1
66∗
-1.5
27∗
0.9
17∗
0.1
96∗
-0.2
22∗
-0.0
56
0.3
03∗
-0.3
13∗
-0.0
12
0.5
48∗
0.4
10∗
(1.0
87)
(0.0
32)
(0.0
78)
(0.1
03)
(0.0
56)
(0.0
42)
(0.0
37)
(0.1
31)
(0.0
31)
(0.1
79)
(0.0
58)
(0.0
72)
DED
C3.0
54∗
-0.0
55∗
0.9
17∗
-0.7
11∗
-0.1
22∗
0.1
38∗
0.0
84∗
-0.1
90∗
0.1
80∗
0.0
63
-0.3
05∗
-0.2
56∗
(0.7
57)
(0.0
22)
(0.0
55)
(0.0
71)
(0.0
39)
(0.0
29)
(0.0
25)
(0.0
91)
(0.0
22)
(0.1
25)
(0.0
40)
(0.0
50)
India
0.5
14
-0.0
21∗
0.1
96∗
-0.1
22∗
-0.0
28∗
0.0
28∗
0.0
08
-0.0
45
0.0
42∗
0.0
12
-0.0
69∗
-0.0
46∗
(0.2
81)
(0.0
08)
(0.0
20)
(0.0
27)
(0.0
14)
(0.0
11)
(0.0
09)
(0.0
34)
(0.0
08)
(0.0
46)
(0.0
15)
(0.0
19)
Irela
nd
-0.5
68
0.0
20
-0.2
22∗
0.1
38∗
0.0
28
-0.0
46∗
-0.0
08
0.0
06
-0.0
34∗
-0.0
07
0.1
25∗
0.0
53∗
(0.4
09)
(0.0
12)
(0.0
30)
(0.0
39)
(0.0
21)
(0.0
16)
(0.0
14)
(0.0
49)
(0.0
12)
(0.0
68)
(0.0
22)
(0.0
27)
LD
C-0
.191
0.0
08
-0.0
56∗
0.0
84∗
0.0
08
-0.0
08
-0.0
19∗
0.0
05
-0.0
16∗
-0.0
13
0.0
07
0.0
19
(0.2
52)
(0.0
07)
(0.0
18)
(0.0
24)
(0.0
13)
(0.0
10)
(0.0
09)
(0.0
30)
(0.0
07)
(0.0
42)
(0.0
14)
(0.0
17)
REU
15
1.0
55
-0.0
48∗
0.3
03∗
-0.1
90∗
-0.0
45
0.0
06
0.0
05
-0.0
26
0.0
96∗
0.0
14
-0.1
16∗
-0.0
79∗
(0.5
85)
(0.0
17)
(0.0
42)
(0.0
55)
(0.0
30)
(0.0
23)
(0.0
20)
(0.0
70)
(0.0
17)
(0.0
97)
(0.0
31)
(0.0
39)
REU
27
-0.8
76∗∗
0.0
43∗
-0.3
13∗
0.1
80∗
0.0
42
-0.0
34∗∗
-0.0
16
0.0
96
-0.0
84∗
0.0
12
0.0
74∗
0.0
78∗
(0.5
23)
(0.0
15)
(0.0
38)
(0.0
49)
(0.0
27)
(0.0
20)
(0.0
18)
(0.0
63)
(0.0
15)
(0.0
86)
(0.0
28)
(0.0
34)
UK
0.0
56
-0.0
01
-0.0
12
0.0
63
0.0
12
-0.0
07
-0.0
13
0.0
14
0.0
12
-0.1
21∗
0.0
53∗
0.0
02
(0.3
57)
(0.0
10)
(0.0
26)
(0.0
34)
(0.0
18)
(0.0
14)
(0.0
12)
(0.0
43)
(0.0
10)
(0.0
59)
(0.0
19)
(0.0
24)
USA
1.8
12
-0.0
83∗
0.5
48∗
-0.3
05∗
-0.0
69
0.1
25∗
0.0
07
-0.1
16
0.0
74∗
0.0
53
-0.2
33∗
-0.1
41∗∗
(1.1
95)
(0.0
35)
(0.0
86)
(0.1
13)
(0.0
61)
(0.0
46)
(0.0
40)
(0.1
44)
(0.0
34)
(0.1
97)
(0.0
64)
(0.0
79)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
38
Table
13:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
UK
Alp
ha
Gam
ma
Beta
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
USA
Brazil
-1.1
02∗
-0.1
26∗
-0.1
86∗
0.2
36∗
-0.0
34∗
0.0
08
0.0
26∗
-0.1
24∗
-0.1
11∗
0.0
13
0.2
98∗
0.1
01∗
(0.4
28)
(0.0
08)
(0.0
20)
(0.0
41)
(0.0
09)
(0.0
24)
(0.0
12)
(0.0
40)
(0.0
10)
(0.0
47)
(0.0
31)
(0.0
47)
Chin
a-1
.946∗
-0.1
86∗
-0.3
13∗
0.3
57∗
-0.0
62∗
0.0
33
0.0
48∗
-0.1
93∗
-0.1
89∗
0.0
38
0.4
68∗
0.1
83∗
(0.7
28)
(0.0
14)
(0.0
34)
(0.0
69)
(0.0
15)
(0.0
40)
(0.0
20)
(0.0
68)
(0.0
16)
(0.0
79)
(0.0
52)
(0.0
79)
DED
C2.4
72∗
0.2
36∗
0.3
57∗
-0.5
81∗
0.0
74∗
0.0
08
-0.0
61∗
0.2
92∗
0.2
50∗
-0.0
09
-0.5
67∗
-0.2
18∗
(0.3
92)
(0.0
08)
(0.0
18)
(0.0
37)
(0.0
08)
(0.0
22)
(0.0
11)
(0.0
37)
(0.0
09)
(0.0
43)
(0.0
28)
(0.0
43)
India
-0.3
18
-0.0
34∗
-0.0
62∗
0.0
74∗
-0.0
22∗
0.0
10
0.0
11
-0.0
62∗
-0.0
36∗
0.0
16
0.1
05∗
0.0
28
(0.3
37)
(0.0
07)
(0.0
16)
(0.0
32)
(0.0
07)
(0.0
19)
(0.0
09)
(0.0
32)
(0.0
08)
(0.0
37)
(0.0
24)
(0.0
37)
Irela
nd
0.1
76
0.0
08
0.0
33∗
0.0
08
0.0
10∗
0.0
07
0.0
02
-0.0
23
0.0
00
0.0
16
-0.0
60∗
-0.0
09
(0.2
14)
(0.0
04)
(0.0
10)
(0.0
20)
(0.0
05)
(0.0
12)
(0.0
06)
(0.0
20)
(0.0
05)
(0.0
23)
(0.0
15)
(0.0
23)
LD
C0.2
85
0.0
26∗
0.0
48∗
-0.0
61∗
0.0
11∗
0.0
02
-0.0
09∗
0.0
43∗
0.0
28∗
-0.0
14
-0.0
75∗
-0.0
26
(0.1
74)
(0.0
03)
(0.0
08)
(0.0
17)
(0.0
04)
(0.0
10)
(0.0
05)
(0.0
16)
(0.0
04)
(0.0
19)
(0.0
12)
(0.0
19)
REU
15
-0.7
30
-0.1
24∗
-0.1
93∗
0.2
92∗
-0.0
62∗
-0.0
23
0.0
43∗
-0.0
77
-0.1
10∗
-0.0
49
0.3
03∗
0.0
98
(0.5
08)
(0.0
10)
(0.0
24)
(0.0
48)
(0.0
11)
(0.0
28)
(0.0
14)
(0.0
48)
(0.0
11)
(0.0
55)
(0.0
36)
(0.0
56)
REU
27
-1.1
94∗∗
-0.1
11∗
-0.1
89∗
0.2
50∗
-0.0
36∗
0.0
00
0.0
28
-0.1
10∗∗
-0.1
23∗
-0.0
18
0.3
08∗
0.1
12
(0.6
55)
(0.0
13)
(0.0
30)
(0.0
62)
(0.0
14)
(0.0
36)
(0.0
18)
(0.0
61)
(0.0
15)
(0.0
71)
(0.0
47)
(0.0
72)
RW
orld
0.2
80
0.0
13
0.0
38
-0.0
09
0.0
16
0.0
16
-0.0
14
-0.0
49
-0.0
18
0.0
32
-0.0
25
-0.0
03
(0.8
27)
(0.0
16)
(0.0
38)
(0.0
79)
(0.0
17)
(0.0
46)
(0.0
22)
(0.0
77)
(0.0
19)
(0.0
90)
(0.0
59)
(0.0
90)
USA
3.0
77∗
0.2
98∗
0.4
68∗
-0.5
67∗
0.1
05∗
-0.0
60
-0.0
75∗
0.3
03∗
0.3
08∗
-0.0
25
-0.7
55∗
-0.2
67∗
(0.6
66)
(0.0
13)
(0.0
31)
(0.0
64)
(0.0
14)
(0.0
37)
(0.0
18)
(0.0
62)
(0.0
15)
(0.0
72)
(0.0
48)
(0.0
73)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.9
6).
39
Table
14:
Alp
ha,
Gam
ma
and
Beta
para
mete
rsfo
rE
lectr
onic
sG
oods
Importin
gC
ountry:
USA
Alp
ha
Gam
ma
Beta
Brazil
Chin
aD
ED
CIn
dia
Irela
nd
LD
CR
EU
15
REU
27
RW
orld
UK
Brazil
-0.0
92
-0.0
21
-0.0
59
0.0
92∗
0.0
03
0.0
08
-0.0
05
-0.0
01
0.0
05
-0.0
30
0.0
08
0.0
05
(2.0
42)
(0.0
37)
(0.0
42)
(0.0
39)
(0.0
32)
(0.0
35)
(0.0
33)
(0.0
50)
(0.0
17)
(0.1
87)
(0.0
32)
(0.1
46)
Chin
a-4
.334
-0.0
59
-1.5
65∗
1.3
55∗
0.0
75
0.0
12
0.0
79
0.3
64∗
0.0
00
-0.2
26
-0.0
36
0.4
63
(3.8
22)
(0.0
69)
(0.0
78)
(0.0
72)
(0.0
59)
(0.0
66)
(0.0
62)
(0.0
94)
(0.0
31)
(0.3
50)
(0.0
59)
(0.2
73)
DED
C3.9
98
0.0
92
1.3
55∗
-1.1
44∗
-0.0
56
0.0
11
-0.0
90∗∗
-0.2
79∗
-0.0
11
0.0
96
0.0
25
-0.3
84∗∗
(2.9
56)
(0.0
54)
(0.0
60)
(0.0
56)
(0.0
46)
(0.0
51)
(0.0
48)
(0.0
73)
(0.0
24)
(0.2
70)
(0.0
46)
(0.2
11)
India
0.1
77
0.0
03
0.0
75∗
-0.0
56∗
-0.0
08
-0.0
04
0.0
03
-0.0
14
0.0
02
-0.0
04
0.0
02
-0.0
18
(0.3
92)
(0.0
07)
(0.0
08)
(0.0
07)
(0.0
06)
(0.0
07)
(0.0
06)
(0.0
10)
(0.0
03)
(0.0
36)
(0.0
06)
(0.0
28)
Irela
nd
-0.0
69
0.0
08
0.0
12
0.0
11
-0.0
04
-0.0
25
0.0
33
0.0
23
-0.0
03
-0.0
46
-0.0
10
0.0
11
(1.0
97)
(0.0
20)
(0.0
22)
(0.0
21)
(0.0
17)
(0.0
19)
(0.0
18)
(0.0
27)
(0.0
09)
(0.1
00)
(0.0
17)
(0.0
79)
LD
C0.3
58
-0.0
05
0.0
79∗
-0.0
90∗
0.0
03
0.0
33
-0.0
48∗∗
-0.0
47
0.0
06
0.0
62
0.0
06
-0.0
40
(1.6
88)
(0.0
31)
(0.0
34)
(0.0
32)
(0.0
26)
(0.0
29)
(0.0
28)
(0.0
42)
(0.0
14)
(0.1
54)
(0.0
26)
(0.1
21)
REU
15
1.0
26
-0.0
01
0.3
64∗
-0.2
79∗
-0.0
14
0.0
23
-0.0
47∗
-0.1
15∗
0.0
05
0.0
46
0.0
18
-0.1
01∗∗
(0.7
58)
(0.0
14)
(0.0
15)
(0.0
14)
(0.0
12)
(0.0
13)
(0.0
12)
(0.0
19)
(0.0
06)
(0.0
69)
(0.0
12)
(0.0
54)
REU
27
-0.0
15
0.0
05
0.0
00
-0.0
11
0.0
02
-0.0
03
0.0
06
0.0
05
-0.0
01
0.0
01
-0.0
03
0.0
03
(0.6
77)
(0.0
12)
(0.0
14)
(0.0
13)
(0.0
11)
(0.0
12)
(0.0
11)
(0.0
17)
(0.0
06)
(0.0
62)
(0.0
11)
(0.0
48)
RW
orld
0.0
45
-0.0
30
-0.2
26∗
0.0
96
-0.0
04
-0.0
46
0.0
62
0.0
46
0.0
01
0.1
07
-0.0
06
0.0
49
(3.7
67)
(0.0
68)
(0.0
77)
(0.0
71)
(0.0
59)
(0.0
65)
(0.0
61)
(0.0
93)
(0.0
31)
(0.3
45)
(0.0
59)
(0.2
69)
UK
-0.0
94
0.0
08
-0.0
36∗
0.0
25∗
0.0
02
-0.0
10
0.0
06
0.0
18∗
-0.0
03
-0.0
06
-0.0
05
0.0
12
(0.3
54)
(0.0
06)
(0.0
07)
(0.0
07)
(0.0
06)
(0.0
06)
(0.0
06)
(0.0
09)
(0.0
03)
(0.0
32)
(0.0
06)
(0.0
25)
Valu
es
inth
epare
nth
ese
sare
asy
mpto
tic
standard
err
ors
.
*Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
5%
level
of
signifi
cance
(cri
tical
valu
e+
/-
1.9
6)
**
Sig
nifi
cantl
ydiff
ere
nt
from
zero
at
the
10
%le
vel
of
signifi
cance
(cri
tical
valu
e
+/-
1.6
45).
40