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: s.joshi2@nuigalway.ie

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