Università della Calabria
Dipartimento di Economia e Statistica
______________________________________________
Gruppo CALCOM
THE DRIVERS OF EXPORT DEMAND:
A FOCUS ON THE GIIPS COUNTRIES
Bernardina Algieri
_____________________________
Maggio 2013
1
The Drivers of Export Demand:
A Focus on the GIIPS Countries
Abstract
This study investigates the drivers of export demand of the peripheral economies of the Euro Area, namely
Greece, Ireland, Italy, Portugal, and Spain (GIIPS) for the period 1980-2012. Recently, several authors have
pointed out that changes in trade export shares are not associated with major terms of trade disturbances;
rather, they are the result of other underlying factors, commonly defined as “non-price competitiveness”.
Starting from this premise, the study extends the traditional imperfect substitute trade model to include a
measure of non-price competitiveness: real capital stock. The latter is a measure of a country’s total resource
base, and it captures the presence of product differentiation and product innovation. The results show a
significant link between export demand and cumulative investments. In the short term, GIIPS exports are
dominated by movements of worldwide real income, while changes in price and non-competitiveness take
longer to affect export performance. In the long run, all three variables play a significant role in pushing
exports.
JEL Classification: F32, F02, F15, F41
Keywords: Price and non-price elasticity of Exports, Income Elasticity, GIIPS
1. Introduction
The estimation of trade elasticities is one of the most significant and controversial topics in international
economics. The topic is important, since the performance of different systems of exchange rates depends on
the values of price elasticity, and even the endurance of the euro relies on the extent of relative price
adjustments, which are a necessary condition to reduce trade imbalances and are most likely the underlying
cause of the euro crisis. The issue is controversial, since the estimations obtained for price and income
elasticities are strongly discordant, and often they are at odds with the real experience of many countries.
There is, in fact, a gap between the low observed volatilities in aggregate quantities and the high volatility of
international relative prices (Imbs and Méjean, 2008). Indeed, in the past fifty years, two streams of thought
have emerged among scholars and experts of international trade, namely the “elasticity pessimists” and
“elasticity optimists”. This contrast reached its climax in the 1970s with the so-called “monetary approach”
to the analysis of the balance of payments. The monetary approach rejected the results of many econometric
estimates in favour of the postulate of infinitely large values of trade elasticity with respect to relative prices
(the “law of one price” as a generalization of the hypothesis of the “small open economy”). Currently, the
fact that fiscal austerity has been required in the countries that joined the European Monetary Union, without
touching the dynamics of the labour costs, could perhaps be interpreted as an implicit acceptance of the
monetary approach to the balance of payments.
In this context, this study has the objective of investigating export demand for the peripheral countries of the
Euro Area by extending the traditional trade model, which specifies the demand for exports as a function of a
country’s price competitiveness and a foreign demand variable, to include a non-price competitiveness
factor. The latter has been proxied by real capital stock, which captures the quality and/or variety of
produced goods to take into account the “new” theory of trade. If a country or region is able to attract
effective investments and create an environment conducive to business growth, it will increase its non-price
competitiveness and will lead to a rise in export flows. The inclusion of a proxy for non-price
competitiveness will contribute to better gauge export demand and ameliorate the estimations of price
elasticities as well as clarify the role of exchange rates in the resolution of global imbalances.
A further novelty of the study is its focus on the case of the Euro Area’s Mezzogiorno. Several analyses have
been carried out for the industrialised EU countries, but a specific analysis of the GIIPS group—Greece,
Ireland, Italy, Portugal and Spain—is still missing. This becomes important in light of the lack of
competitiveness of the Southern Euro Area countries and the implied policy measures to be addressed to
foster competitiveness and support economic growth. These countries, with the exception of Ireland, face
2
large trade imbalances; therefore, a key question to address is whether and to what extent exports could
contribute to lessening these imbalances in the medium and long run. In this context, the estimation of price,
non-price and income elasticities of export demand become important to cope with current account
imbalances.
In addition, the estimated price, non-price and income elasticities can be applied to many relevant macroeconomic policy issues, such as the effect of both monetary and fiscal policies and expenditure-switching
policies on a country’s balance of payments, the impact of external balance restrictions on domestic policy
measures, the international transmission of changes in economic activity and prices and the employment
effects of changes in their own or partner countries’ trade restraints. The estimation of trade elasticities plays
a role in the context of global trade imbalances and the fluctuations of the dollar that could precede an
adjustment in the trade balance of the U.S. and its trading partners (Bussière et al., 2011; Obstfeld and
Rogoff, 2005, 2006; Blanchard et al., 2005). Trade elasticities are also crucial in the propagation of shocks
across borders. With the nominal exchange rate being eternally fixed at one rate and a single authority being
in charge of the monetary policy for the Euro Area, certain instruments are not available to the Euro Area
members to deal with an exchange rate shock to facilitate individual preferences. With nominal exchange
rates unable to adjust, prices have to adjust instead, leading to diverging inflation rates and therefore to
diverging real interest rates and real exchange rates. These, in turn, cause divergences in economic
performances.
The remainder of the paper is organised as follows. Section 2 reviews the theoretical literature on export
determinants. Section 3 presents the model for the group of the GIIPS. Section 4 describes data and relevant
statistics. Section 5 explains the econometric procedure and discusses the empirical results. Section 6
provides conclusions.
2. Literature Review
Estimating export demand elasticities is an old task in economics. A first step in the empirical literature goes
back to the studies by Orcutt (1950) and Houtakker and Magee (1969). Goldstein and Kahn (1985) made an
important contribution to the literature. Their study investigated the responsiveness of both export demand
and export supply to price variations for eight industrial countries —Belgium, France, Germany, Italy, Japan,
the Netherlands, the United Kingdom, and the United States—and found that in six of the eight countries the
estimated price elasticities were greater than unity. This implies a fairly sizeable response of exports to
changes in relative prices. Subsequently, several authors have deeply investigated trade elasticity and most of
their econometric estimations indicate that price elasticities fall in a range of 0 to –4.0, while income
elasticities fall between 0.17 and 4.5. Since the values of price elasticities vary considerably, part of the
literature has questioned the effectiveness of real devaluation in affecting exports and imports (Rose 1990,
1991; Ostry and Rose, 1992). Table 1 summarises a set of studies on export equations that include in their
analysis one or more countries belonging to the GIIPS. It is easy to see that price and income elasticities vary
across studies and according to the estimation technique adopted.
For Italy, export price elasticities are all below the unity, ranging from -0.98 in Murata et al.’s research
(2000) to -0.14 in the study of Senhadji and Montenegro (1998) . This common feature across the considered
studies would suggest that competitive devaluation policies are not so effective in stimulating exports.
Income elasticities, when they are not constrained to unity, are very elastic.
For other countries in the GIIPS group, the price elasticity of demand for export is relatively inelastic in
some analyses and quite elastic in others. In the case of Ireland, for example, Bredin et al. (2003) in
investigating the total exports and the sectoral exports SITC 0-4 and SITC 5-8, that correspond broadly to
the exports of indigenous Irish firms and multinationals, respectively, found that exports are more price
elastic for the multinational sector (-3.90) than for indigenous exports (-2.38). Similarly, the long-run
relationship between Irish exports and foreign economic activity is found to be positive, large and
statistically significant, especially in the multinational sector where the estimate is 5.70 compared with 2.59
3
for indigenous exports. According to the authors, this reflects the fact that exports of multinational firms are
generally high-tech products which tend to be highly income elastic. A high income elasticity of demand for
Irish exports was also found by Caporale and Chui (1999), with a value of 2.978, and McGettigan and
Nugent (1995) who found, depending on the measure of exports chosen, a long-run elasticity with respect to
world demand of 1.782 to 2.0429. Compared to Bredin et al. (2003), more contained price elasticities for
Ireland have been gauged by Caporale and Chui (1999) in their dynamic ordinarily least square model and by
O’Donnel (2005). However, Caporale and Chui (1999) have shown an extremely high price elasticity when
an autoregressive distributed lag model is used.
In the case of Greece, most of the studies reveal that price elasticities of the demand for export of goods and
services are more contained than exports of manufacturing. Explicitly, with the exception of the
autoregressive distributed lag model by Caporale and Chui (1999), there is a tendency of export demand to
be inelastic in response to price changes when goods and services are considered, while export demand tends
to be elastic in the case of manufacturing exports. This can be due to the fact that the price elasticity of
aggregate exports is somewhat lower than that for manufactured products, given the apparent insensitivity of
the supply of resource and rural exports to changes in prices, as well as the fact that tourism is countryspecific and lacks substitutes, so that one could expect a lower sensitivity to price changes. The tendency to
have lower prices elasticity for exports of goods and services and higher values for manufacturing is also true
in Spain and Portugal. Income elasticities also vary across studies, ranging between 2.1 (Senhadji and
Montenegro, 1998; Athanasoglou and Bardaka, 2010) and 2.8 (Caporale and Chui, 1999) for Greece, and
between 2.86 (Senhadji and Montenegro, 1998) and 3.32 (Caporale and Chui, 1999) for Spain.
Price competitiveness and external demand are only some of the key determinants of export performances.
Indeed, the persisting residuals resulting from the estimated export equations for some of the EU members as
reported by the European Central Bank (2005, 2012) and OECD (2000, 2005) suggest that these two drivers
alone do not entirely explain export performances; factors such as non-price competitiveness may also play a
crucial role. In this direction, among others, go the studies by the European Commission (2010) and Algieri
(2011). In particular, the first found that for the Euro Area countries, external demand and the real effective
exchange rate account for 55% of variance of exports over the period 1998-2008. The second study, adopting
an unobserved component model, determined that the non-price factor is a valuable driver of export volumes.
To broaden the debate on exports, the proposed model extends the basic version of the imperfect substitutes
model at the aggregate level to include a proxy of non-price competitiveness, the country’s real capital stock.
The inclusion of capital stock gives an indicator of the exporting country’s resource base, and it resembles
the process of product differentiation (Muscatelli et al., 1995). The inclusion of real capital stock also allows
us to take into consideration Krugman’s argument that export growth is systematically related to a bias in
estimates of the income elasticity of export demand, which reflects a failure to account for changing product
quality. This is because income elasticities allow countries to have very different growth rates without strong
trends in equilibrium real exchange rates (Caporale and Chui, 1999). In Krugman’s words, “Fast growing
countries expand their share of world markets, not by reducing the relative prices of their goods, but by
expanding the range of goods that they produce as their economies grow.” He illustrates this point by using
an increasing-returns model of intra-industry trade, where there are no relative price effects and no terms-oftrade effects from export growth. On the same wave length, Sutton (2007) emphasises that non-price
competition becomes more and more important in the course of vertical specialization.
Put differently, the inclusion of real capital stock points to the importance of supply-side factors in trade
models, much of it relating to the observed relationship between estimated elasticities on foreign activity in
export equations and the rate of growth of domestic output referred to as the “45-degree rule” (Krugman
1989, Caporale and Chui, 1999). This empirical regularity implies the need to include a factor that appraises
domestic supply in export equations. This factor can be justified on the basis of new trade theories that
highlight the significance of increasing returns to scale in production and the willingness of consumers for
greater variety (Feenstra, 1994; OECD, 2000; Broda and Weinstein, 2006).
4
Table 1 Comparison of estimated long-run price and income elasticities for exports
Investigator
Algieri (2011)
(6 countries analysed)
Export price elasticity
0.78*
0.43*
Foreign Income elasticity
a unit income elasticity imposed
a unit income elasticity imposed
Country
Spain
Italy
Estimation period
1978:Q1-2009:Q1
Level of aggregation
Export volumes of goods and services
-1.169
2.162
Greece
1962:Q1-1999:Q4
Manufacturing Exports SITC 5-8
-1.63 (a) ; -1.51 (b)
-1.71 (a) ; -1.89 (b)
0.91 (a) ; 0.63 (b)
a unit income elasticity imposed
Ireland
1980:Q1-1999:Q4
Export values of goods and services
ECB (2005)
di Mauro and Maurin
(6 countries analysed)
0.42*
0.58*
a unit income elasticity imposed
a unit income elasticity imposed
Italy
Spain
1992-2003
Export volumes of goods and services
Standard export equations with constraint on foreign demand
and trend term
OECD (2005)
Pain et al.
(36 countries analysed)
-0.604
-1.047
Italy
Spain
Greece
Ireland
Portugal
1982-2002
(quarterly data)
Export volumes of goods and services
Standard export equations with long run elasticity of unity
imposed on export market size and time trend in a ECM
framework
-0.466
a unit income elasticity imposed
a unit income elasticity imposed
a unit income elasticity imposed
a unit income elasticity imposed
a unit income elasticity imposed
-0.42
-1.26
a unit income elasticity imposed
a unit income elasticity imposed
Italy
Spain
1975:Q1-2001:Q1
Volume of manufacturing exports
Standard export equations in a ECM framework
-2.38 SITC 0-4
-3.90 SITC 5-8
-3.20 SITC 0-8
2.59 SITC 0-4
5.70 SITC 5-8
4.87 SITC 0-8
Ireland
1978Q3-1998Q4
Manufacturing exports to the EU
Johansen multivariate cointegration
OECD (2000)
Murata et al.
(23 countries analysed)
-0.98
-1.35
-0.80
-1.40 (non linear trend)
-1.73 (non linear trend)
a unit income elasticity imposed
a unit income elasticity imposed
a unit income elasticity imposed
a unit income elasticity imposed
a unit income elasticity imposed
Italy
Greece
Ireland
Spain
Portugal
1976-1997
(semi-annual data)
Manufacturing export volumes
EUROMON (2006)
Bank of Netherlands
(13 countries analysed)
-0.70
-0.70
a unit income elasticity imposed
a unit income elasticity imposed
Italy
Spain
1970-1999
(quarterly data)
Real exports of goods and services
Senhadji and Montenegro
(1998)
(75 countries analysed)
-0.14
2.26
Italy
1960-1993
Export of goods and non factor
services
-0.18
-2.92
-0.70
-0.93 (DOLS); -0.47 (ARDL)
-1.93 (DOLS); -1.22 (ARDL)
-0.34 (DOLS); -6.12 (ARDL)
-0.69 (DOLS); -1.35 (ARDL)
2.86
1.30
2.81
2.21 (DOLS); 2.02 (ARDL)
3.00 (DOLS); 3.32 (ARDL)
2.97 (DOLS); 3.59 (ARDL)
2.38 (DOLS); 2.10 (ARDL)
Spain
Portugal
Greece
Italy
Spain
Ireland
Greece
1960-1992
Export volumes of goods and services
-0.9
1.6
Italy
1970 to 1997
Real exports of goods and services
Cointegration vectors and ECM
-4.33 (merchandise)
-7.58 (manufacturing)
Traditional model
-0.32
1.78 (merchandise)
2.04 (manufacturing)
Ireland
1975:Q1-1994Q1
Manufacturing and merchandise
exports
Manufacturing export volumes
Cointegration vectors and ECM
Athanasoglou and Bardaka (2010)
O’Donnell (2005)
Banco De Espaňa (2003)
Buisàn and Caballero
(9 countries analysed)
Bredin et al. (2003)
Caporale and Chui (1999)
(8 countries analysed)
Hooper, et al. (2000)
(6 countries analysed)
McGettigan and Nugent (1995)
Anderton (1991)
(5 countries analysed)
-0.466
-0.604
1971:Q2-1988:Q4
Type of equations/model
Unobserved component model
Augmented export equations with capital stock in a VECM
framework
Augmented export equations with industry share in a
Johansen framework (a) and Phillips-Hansen setting (b)
System estimation and single equation approach in a
logarithmic dynamic error correction form within two
scenarios: seemingly unrelated regression estimations
(SURE) and OLS
Standard export equations in a multi country model
Fully modify estimation
Johansen Procedure using two techniques:
(1) DOLS procedure developed by Stock and Watson (1993);
(2) Autoregressive distributed lag (ARDL)
Two scenarios: traditional model and stochastic trend model
Italy
*Relative export prices have a positive sign, since they are defined as competitor export price/ national export price.( XP*/XP). In the other studies relative export prices have a negative sign since they are defined as XP/XP*.
Source: Own Elaborations
5
3. A Model for the GIIPS
Let us suppose that each exporting country belonging to the GIIPS has only one trading partner (the rest of
the world). The GIIPS’s export demand (xt) corresponds with the import demand of the rest of the world
(mt*). We continue to assume that there is a representative agent in the rest of the world, who lives forever
and maximises his utility by choosing how much to consume of his domestic production (yt*) and of the
imported good (mt*). It is supposed further that there is no production sector, because production often
involves the combining of intermediate inputs by using factors of production, while the model makes no
distinction between intermediate and final products. The representative consumer in the rest of the world
maximises an intertemporal utility function over time (Ut) expressed as:
[
Max U t =
]
∞
yt* , mt* t = 0
∞

−1
*
*
Max E t ⋅  ∑ (1 + δ ) ⋅ u ( y t , mt ) 
t = 0

[
]
∞
yt* , mt* t = 0
(1)
subject to his budget constraint:
bt*+1 = (1 + i ) ⋅ bt* + (ht* − y t* ) − p t ⋅ m t* (2)
and to a transversality condition, which excludes Ponzi-games, i.e. the fact that a consumer can freely
consume all lifetime resources, by borrowing forever without extinguishing his debt.
lim =
T →∞
bT* +1
T
∏ (1 + i )
−1
= 0 (3)
t =0
All the starred variables denote the rest of the world (the importing country) while non-starred variables refer
to the GIIPS. E{·} is the expectation operator at time t; δ is the consumer’s rate of time preferences, i.e. the
subjective discount rate, which measures the individual’s impatience to consume; agents are free to borrow
and lend at the same world interest rate i, that is the yield on capital; b* t+1 denotes the next period stock of
GIIPS bonds held by the rest of the world if positive and the next period stock of foreign bonds held by the
GIIPS if negative; pt is the price of the GIIPS goods and services in terms of foreign commodity; and h* is
the stochastic endowment which follows an AR(1) process of the form:
ht* = ϕ ⋅ ht*−1 + (1 − ϕ ) ⋅ h * +ε t*
0 ≤ϕ ≤1
(4)
ε t* ~ (0, σ 2 ) (5)
with an unconditional mean h * and an unconditional variance σ 2 /(1 − ϕ 2 ) . ε t*
is an independent and
identically distributed shock to the stochastic endowment with zero mean and variance σ 2 . ϕ governs the
degree of persistence of the endowment shock.
The first order conditions for the individual’s problem are:
∂L t
∂y *t
∂L t
∂m *t
: u y* (t) - λ t = 0
: u m* (t) - λ t ⋅ p t = 0
λ t = (1 + δ ) -1 ⋅ E t (1 + i ) ⋅ λ t +1
(6)
(7)
(8)
where L is the Lagrangian and λt is the Lagrange multiplier associated with the budget constraint.
6
Consider the case in which the individual utility function is of addilog type, as in Ogaki (1992) and Clarida
(1994), de la Croix and Urban (1998), Senhadji and Montenegro (1998). The specific form of the
instantaneous utility function extended to include product variety is:
u t (y *t , m *t ) =
Γt ⋅ ( y t* )1− β1 Φ t ⋅ v tγ ⋅ (mt* )1− β 2
+
1 − β1
1− β 2
Γt = e
b1 +ξ Γ , t
Φt = e
(9)
(10)
b 2 +ξ Φ , t
(11)
where Γt and Φt are exponential stationary random shocks, which cause variations in the preferences of the
representative agent, ξΓ,t and ξΦ,t are stationary shocks, β1 and β2 are called curvature parameters and their
inverse can be interpreted as long-run intertemporal elasticities of substitution between the domestic and the
imported good, vt is the product variety that varies over time and γ is a taste or quality parameter for the
variety v. Thus, consumers make their choice not only on the base of price and their disposable income, but
also on the variety of products and quality as postulated by the new trade theory. Indeed the latter highlights
that horizontal product differentiation and quality is a source of intra-industry trade. Solving the
maximization problem for the explicit utility function (9) and substituting the values for Γt and Φt we have
the following first order conditions:
y t*
m t*
−
= λt
−
= λt
1
β1
1
⋅ (e
b1 +ξ Γ , t
1
β2
1
⋅ (e
b 2 +ξ Φ , t
)
β2
)
β1
−
⋅pt
(12)
1
β2
γ
β2
⋅vt
(13)
Taking the log for the equations (12) and (13), solving the equation (12) for λ and substituting it in the
equation (13) yields:
ξ Γ , t b 2 ξ Φ ,t
b
β
γ
1
ln m t* = 1 ln y *t −
ln p t +
ln v t − 1 −
+
+
(14)
β2
β2
β2
β2 β2 β2 β2
Posing:
κ=
ξt =
1
β2
1
β2
( b2 − b1 ) (15)
( ξ Γ ,t − ξ Φ ,t ) (16)
Leads to the extended export demand function of the economic imperfect substitutes model:
ln m t* = ln x t = κ +
β1
γ
1
ln y *t −
ln p t +
ln v t + ξ t
β2
β2
β2
(17)
The variety of products (vt), which mirror a non-price competitiveness aspect of international trade has been
proxied by the real capital stock (rkst). The choice has fallen on real capital stock for two main reasons: first,
the variable is available on quarterly frequency, while other measures of innovation and quality, such as
R&D expenditures, patents, educational attainment are available on annual basis and not sufficiently
disaggregated at a higher frequency. Second real capital stock, according to the literature, reflects accurately
the non-price competitiveness factor, since it mirrors product variety and/or the improvement in quality.
Indeed, if firms invest more in their production, the quality of product should increase and therefore exports
are expected also to rise. This is in line with the analyses by Owen and Wren-Lewis (1993), Muscatelli et al.
7
(1995) and Madden et al. (1999) which have shown that capital stock can serve as a product innovation
proxy and have a significant influence on exports. Along this line, Athanasoglou and Bardaka (2010) argue
that it serves as an indirect measure of product variety and quality and hence of product differentiation.
4. Data
The quantitative stochastic equation for the GIIPS formalised as:
ln x t = κ +
β1
γ
1
ln y *t −
ln p t +
ln rks t + ξ t
β2
β2
β2
ξ t ~ (0, σ 2 )
(18)
has been constructed using quarterly data taken by Datastream ranging from 1980:1 to 2012:3. The variable
xt refers to exports of goods and services in real term, y*t is the external demand/income variable, pt is the
real effective exchange rate, and rkst is the real capital stock index. All variables have base 2005=100.
Detailed data information and descriptive statistics are reported in the appendix.
Chart 1 shows the real exports dynamics for the GIIPS countries, over the period under consideration. It
emerges that Ireland has grown faster in terms of exports, compared to the other GIIPS. This pattern has
been firstly driven by foreign multinationals that invested in the country as an export base, attracted by
industrial policy measures. Then in the 1990s, Irish export performance was boosted by relatively low labour
costs by international standards and a favourable exchange rate. The strength of the US economy also played
an important role as a market for exports and a source of foreign direct investment.
Chart 1 Real exports of goods and services. Indices base 2005=100
5.0
4.5
4.0
3.5
3.0
2.5
2.0
80
82
84
86
88
90
92
94
96
LGR_EXP
LPT_EXP
98
LIR_EXP
LSP_EXP
8
00
02
04
06
LIT_EXP
08
10
12
Two proxies for the variable yt have been considered. One is the real world gross domestic production
corrected to exclude the own country’s GDP; the other is the foreign demand1 gauged as a weighted average
of the import volumes of goods and services of the main trading partners, weights defined as the share of
each destination in total exports. The weighting system was drawn from the ECB2.
Two measures of real effective exchange rate have been considered: one based on consumer prices (CPI) and
the other on unit labour costs (ULC). In both cases, the real effective exchange rate is a trade-weighted
exchange rate whose weightings reflect the major trade partners. Thus, the real effective exchange rate is a
significant indicator that measures the variations in competitiveness of the five EA economies. An increase
in this series represents a real appreciation and a loss in price competitiveness. Chart 2 and Chart 3 report the
real effective exchange rates CPI-based and ULC-based respectively for the GIIPS. All of the five peripheral
countries experienced a loss in price competitiveness during the 1980s. The increase in relative price was
especially pronounced for Italy and Spain. The negative trend reverted in the 1990s, to increase again in the
past decade. However, recently the GIIPS indicated some signs of improving competitiveness. The exchange
rate elasticity of trade prices is important, because it determines the potential role of exchange rates in the
resolution of global imbalances, as it affects the response of the trade balance to a change in the exchange
rate. Exchange rate elasticity of exports is also a key parameter in the monitoring and forecasting of real
output growth in these countries, which can be substantially affected by terms-of-trade fluctuations.
Chart 2 Real Effective Exchange Rate Index, CPI based. 2005=100
4.8
4.7
4.6
4.5
4.4
4.3
4.2
80
82
84
86
88
90
92
LGR_REER
LPT_REER
94
96
98
00
LIR_REER
LSP_REER
1
02
04
06
08
10
12
LIT_REER
Foreign demand can be defined either as a weighted average of output in foreign countries (Hooper, Johnson and Marquez, 2000),
or as a weighted average of foreign imports (e.g. Anderton, di Mauro, Moneta, 2004). This latter definition was used because the
ratio of exports to foreign demand can be interpreted as market share.
2
In the ECB dataset, weights are based on trade in manufactured goods and services with the trading partners in specific periods (e.g.
1995-1997; 1998-2000; 2001-2004) and are calculated to account for third market effects. Since overall trade patterns tend to change
only gradually, the weights are updated at three-year intervals. Further details on the methodology underlying the weighting scheme
can be found in ECB monthly bulletin, September 2004, Box 10.
9
Chart 3 Real Effective Exchange Rate Index, ULC based. 2005=100
4.8
4.7
4.6
4.5
4.4
4.3
4.2
4.1
80
82
84
86
88
90
92
94
96
98
LGR_REER_ULC
LIT_REER_ULC
LSP_REER_ULC
00
02
04
06
08
10
12
LIR_REER_ULC
LPT_REER_ULC
The following table reports the correlation matrix of the two real effective exchange rates.
Table 2 Correlation matrix, Sample 1984Q1 2012Q1
Correlation
lgr_reer_ulc
lir_reer_ulc
lgr_reer
lir_reer
lit_reer
lpt_reer
lsp_reer
0.886048
0.93824
lit_reer_ulc
0.688924
lpt_reer_ulc
0.866085
lsp_reer_ulc
0.832579
Notes: l=logs, reer=real effective exchange rate CPI based, reer_ulc=real effective exchange rate ULC based, gr=Greece, ir=Ireland,
it=Italy, pt=Portugal, sp=Spain.
Combing the different considered variables, three specifications have been estimated: the first contains the
real world GDP, the real effective exchange rate CPI based, and real capital stock (a); the second includes
the foreign demand, the real effective exchange rate CPI based, and the real capita stock (b); the third
includes the foreign demand, the real effective exchange rate based on unit labour costs, and the real capital
stock (c). The sample for specification 3 starts in 1984:1, since real effective exchange data based on ULC
are only available starting from that period.
5.
The Johansen and Juselius Methodology
The Johansen and Juselius cointegration methodology (1990) has been adopted in order to identify the longrun relationship among the variables and to test for the presence of more than one co-integrating vector.
Additionally, Johansen and Juselius’ (1990) maximum likelihood estimator corrects for autocorrelation and
10
endogeneity parametrically using vector error correction mechanism specification. Finally, this methodology
performs better than other estimation methods by including additional lags, even when the errors are nonnormally distributed or when the dynamics are unknown, and the model is over-parameterised (Gonzalo,
1994).
Formally, the Johansen and Juselius methodology considers a p-dimensional vector autoregressive model,
which in error correction form is given by:
p −1
∆z t = Πz t − p + ∑ Γi ∆z t −i + ΦS t + ξ t , (19)
i =1
where ∆ is the difference operator and zt=(k x 1) is the vector of non-stationary I(1) variables, explicitly:
z t = [x t ; p t ; y t ; rks t ]
(20)
and:
p
Π = ∑ Ai − I
i =1
i
Γi = ∑ A j − I
(21)
(22)
j =1
I=a (k x k) identity matrix
A=a (k x k) matrix of parameters
The variable St contains a constant term and a time trend, and ξ is a vector of Gaussian, zero mean
disturbances. Гi are (k x k) dimensional matrices of autoregressive coefficients. The long-run matrix ∏ can
be decomposed as the product of α and β, two (k x r) matrices each of rank r, such that ∏=αβ’, where β’
contains the r cointegrating coefficients and α represents the adjustment parameters, which reflect the speed
of adjustment of particular variables with respect to a disturbance in the equilibrium relationship. Therefore,
equation (19) becomes:
( )
p −1
∆z t = αβ ' z t − p + ∑ Γi ∆z t −i + ΦS t + ξ t
(23)
i =1
The maximum likelihood approach makes it possible to test the hypothesis of r cointegrating relations among
the elements of xt,
H0 : Π = α β '
(24)
where the null of no cointegration relation (r=0) implies ∏=0. If ∏ is of rank k, the vector process is
stationary. If rank (∏)=1 there is a cointegrating vector; for other cases in which 1<rank (∏)<k there are
multiple cointegrating vectors.
5.1 Estimations
After transforming the series into logarithmic terms and establishing that the individual series are integrated
of degree one using the standard unit root tests3, the Johansen methodology has been applied (Enders, 1995
page 374). Specifically, to identify the proper model, the five possibilities considered by Johansen (1991,
1995) were tested: (1) the series has no deterministic trends and the cointegrating equations do not have
intercepts; (2) the series has no deterministic trends and the cointegrating equations have intercepts; (3) the
3
The Augmented Dickey-Fuller (1979) and the Philips-Perron (1988) tests have been carried out for the series. The
critical values for the rejection of the null hypothesis of a unit root are those computed according to the McKinnon
criterion. The lag length for the ADF test is based on the Schwarz information criterion. The lag structure for the P-P is
selected using the Bartlett Kernel with automatic Newey-West bandwidth. The two tests have been carried out in three
settings: with a constant, without a constant and with a constant plus a linear trend. The results have not been reported
but are available from the author upon request.
11
series has linear trends but the cointegrating equations only have intercepts; (4) both series and the
cointegrating equations have linear trends; and (5) the series has quadratic trends and the cointegrating
equations have linear trends. Following the Pantula principle (Pantula, 1989), the third model is the most
appropriate for the samples. To identify the proper lag length, the Aikaike Information and Schwarz Criteria
have been implemented. Note that the lag length must be small enough to allow estimation and high enough
to ensure that errors are white noise. To determine the number of cointegrating vectors, both the trace test4
and the Maximum Eigenvalue test5 were performed using the critical values of Mackinnon-Haug-Michelis
(1999). Table 3 reports the results of Johansen’s test for cointegration for the three specifications. The first
row of the trace statistic tests the hypothesis of no cointegration against the alternative of one or more
cointegrating vectors (r>0); the second row tests the null hypothesis of a maximum of one cointegrating
relation (r≤1) against the alternative of r>1 cointegrating vectors, and so on. Since the λtrace statistic values
exceeds the 5 percent critical values for the three specifications, it is possible to reject the null hypothesis of
no cointegration vectors and accept the alternative of one or more cointegrating vectors. This indicates that
there is one cointegrating vector for all countries except for Italy in specification (a). The λmax statistic
suggests instead that all countries have one cointegrating vector. The null hypothesis of no cointegrating
vector (r=0) against the specific alternative of one cointegrating relationship (r=1) can be rejected at the 5
percent level. Conversely, the null of r=1 against the specific alternative r=2 cannot be rejected.
Table 3 Johansen Cointegration Tests,
Specification (a) Sample period 1980: 1-2012:3
Null
H0
r=0
Alternative H1
Ireland
λ Trace Stat
52.24383*
Italy
λ Trace Stat
42.50460
Portugal
λ Trace Stat
55.71571*
Spain
λ Trace Stat
56.84779*
5% Critical Value**
r>0
Greece
λ Trace Stat
49.36282*
r≤1
r>1
20.71104
15.49968
13.23327
26.96102
27.07850
29.79707
r≤2
r>2
5.093191
3.463291
5.804622
14.16962
10.44170
15.49471
Null
H0
r=0
Alternative H1
λ Maximum
Eigen Stat
36.74415*
λ Maximum
Eigen Stat
29.27133*
λ Maximum
Eigen Stat
28.75469*
λ Maximum
Eigen Stat
29.76930*
5% Critical Value**
r=1
λ Maximum
Eigen Stat
28.65178*
r=1
r=2
15.61785
12.03639
7.428649
12.79140
16.63680
21.13162
r=2
r=3
4.362710
2.435957
4.974241
10.43360
6.344624
14.26460
47.85613
27.58434
r denotes the number of cointegrating vectors. * denotes rejection of the hypothesis at 5 percent level. ** MacKinnon-Haug-Michelis (1999) critvalues. Both the trace test and max-eigenvalue test indicate one cointegrating equation at the 0.05 level. Only Italy’s trace test indicates no
cointegration at the 0.05 level.
Specification (b) Sample period 1980: 1-2012:3
Null
H0
r=0
Alternative H1
r>0
Greece
λ Trace Stat
48.34281*
Ireland
λ Trace Stat
48.96563*
Italy
λ Trace Stat
52.67911*
r≤1
r>1
18.31012
17.07233
25.96009
r≤2
r>2
4.911643
8.045375
Null
H0
r=0
Alternative H1
r=1
λ Maximum
Eigen Stat
30.03269*
r=1
r=2
r=2
r=3
Portugal
λ Trace Stat
53.40140*
Spain
λ Trace Stat
80.12035*
5% Critical Value**
26.37392
26.99516
29.79707
11.14877
11.67032
14.22712
15.49471
λ Maximum
Eigen Stat
31.89330*
λ Maximum
Eigen Stat
47.04093*
λ Maximum
Eigen Stat
39.15339*
λ Maximum
Eigen Stat
53.12519*
5% Critical Value**
13.39848
9.026955
20.69924
17.73367
12.76804
21.13162
4.233362
6.571258
20.69924
7.815602
10.53584
14.26460
47.85613
27.58434
Both the trace test and max eigenvalue test indicate one cointegrating equation at the 0.05 level.
4
The trace statistic of r cointegration relations is a sequence of likelihood ratio tests, computed as
k
λtrace (r ) = −T ∑ ln(1 − λˆi ) ,
i = r +1
where λi is the estimated value of the characteristic roots (also called eigenvalue) obtained from the estimated long-run
Π matrix, and T is the number of usable observations.
5
The max eigenvalue statistic is calculated as λt max (r ) = −T ln(1 − λˆr +1) .
12
Specification (c) Sample period 1984: 1-2012:3
Null
H0
r=0
Alternative H1
r>0
Greece
λ Trace Stat
48.68816*
Ireland
λ Trace Stat
55.91920*
Italy
λ Trace Stat
56.69966*
r≤1
r>1
24.30552
25.06858
29.10061
22.59245
14.00444
29.79707
r≤2
r>2
10.78628
11.65335
12.70871
7.131188
4.608357
15.49471
Null
H0
r=0
Alternative H1
λ Maximum
Eigen Stat
30.85063*
λ Maximum
Eigen Stat
27.59905*
λ Maximum
Eigen Stat
30.90554*
λ Maximum
Eigen Stat
33.87513*
5% Critical Value**
r=1
λ Maximum
Eigen Stat
40.80086*
r=1
r=2
20.12980
13.41523
16.39191
15.46126
19.90549
21.13162
r=2
r=3
7.383743
7.869370
6.434547
9.115237
14.26460
6.648275
Portugal
λ Trace Stat
53.49798*
Spain
λ Trace Stat
58.79933*
5% Critical Value**
47.85613
27.58434
Both the trace test and max-eigenvalue test indicate one cointegrating equation at the 0.05 level.
Ad hoc dummy variables have been included in the test VECM to account for periods of economic
instability6. Table 4 shows the cointegrating vectors (normalised on exports) according to three
specifications.
Table 4 Vector Error Correction Estimations
Dependent ln exp
Cointegrating
vector β
ln reer
ln y and fd
ln rks
c
Speed of
adjustment α
dln real export
index
Greece
(a)
-1.24
(-1.81)
2.46
(5.17)
2.31
(2.63)
17.66
Greece
(b)
-1.37
(-1.87)
1.56
(4.50)
3.27
(2.18)
25.52
Greece
(c)
-1.72
(-3.46)
1.41
(3.90)
3.81
(1.93)
34.29
Ireland
(a)
-3.43
(-3.13)
2.56
(1.82)
2.58
(4.12)
19.93
-0.09
(-2.77)
-0.08
(-2.89)
-0.09
(-2.89)
-0.03
(-2.99)
Ireland Ireland
(b)
(c)
-2.58
-2.56
(-4.44) (-3.42)
2.67
3.70
(8.16)
(6.02)
1.06
2.08
(3.15)
(3.50)
8.93
8.69
Italy
(a)
-0.80
(-4.59)
1.09
(3.41)
2.32
(7.47)
5.59
Italy
Italy
(b)
(c)
-0.99
-0.70
(-2.67) (-3.60)
1.21
1.24
(2.75) (2.52)
4.35
4.32
(3.85) (3.45)
20.85 21.83
Portugal
(a)
-1.56
(-1.81)
1.99
(1.97)
2.95
(3.89)
6.67
-0.04
(-2.18)
-0.16
(-4.55)
-0.11
-0.10
(-3.06) (-2.81)
-0.06
(-1.89)
-0.04
(-1.82)
Portugal Portugal
(b)
(c)
-1.11
-1.11
(-5.14)
(-2.97)
1.09
1.03
(3.97)
(2.46)
1.33
1.01
(2.36)
(3.76)
6.22
3.70
-0.05
(-1.88)
-0.08
(-2.70)
Spain
(a)
-1.01
(-6.11)
1.01
(3.12)
2.61
(10.41)
5.34
Spain Spain
(b)
(c)
-2.28
-2.05
(-9.92) (-4.42)
1.03
1.33
(5.17) (2.52)
3.97
3.28
(9.97) (2.47)
9.21
9.21
-0.27
(-5.76)
-0.22
-0.24
(-3.85) (-4.10)
Regressand: ln real export of goods and services index. A linear trend is included. t stat in brackets. ln stands for logarithm. a) corrected world GDP
(y), reer CPI based, real capital stock (rks) b) foreign demand (fd), reer CPI based, real capital stock (rks); c) foreign demand (fd), reer ULC based,
real capital stock (rks).
The columns of β in Table 4 are interpreted as long-run equilibrium relationships between variables, and the
matrix α determines the speed of adjustment toward this equilibrium. In particular, the cointegration analysis
suggests that real exports are cointegrated with the real effective exchange rate, foreign income and real
capital stock. The estimated speed of adjustment coefficients carries the expected signs and is statistically
different from zero. This means that cointegrating vectors converge toward their long-run equilibrium in the
presence of a shock to the system. The disequilibrium in Spain, Italy, and Greece is eliminated quicker than
in Portugal and Ireland; i.e., it takes an average of approximately 4.2 quarters for Spain (1/0.24), 8.13
quarters for Italy, 11.5 quarters for Greece and about 16.7 and 25 quarters for Portugal and Ireland to restore
the equilibrium after a shock.
6
Two dummy variables relative to 1986 and 1990 enter Greece’s equation, a dummy variable relative to 2002 enters Ireland’s
equation; three dummy variables relative to 1992, 2003, 2009 enter Italy’s equation, two dummy variables relative to 1982 and 2008
enter Portugal’s equation; four dummy variables relative to 1984, 1992, 1997, 2009 enter Spain’s equation. For the third
specification, two dummy variables relative to 1986 and 1990 have been added to Greece’s equation, a dummy variable relative to
2002 has been added to Ireland’s equation; two dummy variables relative to 1992 and 2003 have been added to Italy’s equation, two
dummy variables relative to 2000 and 2008 have been added to Portugal’s equation; three dummy variables relative to 1987, 1992,
1997 have been added to Spain’s equation.
13
Table 4 provides evidence that higher real effective exchange rates lead to a decrease in exports as predicted
by the imperfect substitute model. Explicitly, the long run price elasticity varies between -3.43 in Ireland and
-0.80 for Italy in specification (a), between -2.58 in Ireland and -0.99 in Italy in specification (b), and
between -2.56 in Ireland and -0.70 for Italy in specification (c). This indicates that despite the model used,
Italy has the lowest price elasticities, whilst Ireland has the highest values among the GIIPS. Considering the
three specifications, a real depreciation of 10 percent brings about a rise in exports of about 12-17 percent in
Greece, 26-34 percent in Ireland, 7.0-10 percent in Italy, 11-15 percent in Portugal and 10-22 percent in
Spain. Therefore, the Marshall-Lerner condition, which states that a real depreciation improves the current
account if exports and imports are sufficiently elastic to the real exchange rate, has certainly been met for
Greece, Ireland, Portugal and Spain. A euro depreciation would help ameliorate the Spanish, Irish,
Portuguese and Greek current account (or trade) balance. This implies the importance of real effective
exchange rates in influencing current accounts. In this context, the higher the price elasticity, the more
competitive the international market for exports of a particular country, and therefore a real devaluation will
be in upgrading export receipts. The highest price elasticity of Ireland, could be likely explained by the fact
that the significant number of multinational that operate in the country and make up a large part of exports,
are both price setters, so that the influence of competitive measures are larger, and they are highly responsive
to changes in demand. For Italy, price competitiveness would seem less effective in ameliorating the current
account, and exchange rate policies may not be very successful in promoting export growth. The results for
Italy are in line with those obtained in other studies regarding the inelastic values, and more similar to the
findings by OECD (2000) and Hooper et al. (2000).The results in this case could suggest an increase in nonprice competitiveness in order to push exports.
Table 4 indicates that exports are very sensitive to fluctuations in foreign demand. The income elasticity
ranges between 1.01 for Spain and 3.70 for Ireland. A growth in world income produces an increase in
demand for foreign products and pushes GIIPS exports up. The higher the elasticity of foreign demand for
GIIPS products, the stronger the exports will be as an engine of growth. In comparison to other studies, the
estimation for Greece is similar to the results determined by Athanasoglou and Bardaka (2010), mainly
considering specification (a). The smaller income elasticity of Ireland found in this study compared to
Bredin et al. (2003) could reflect that fact that the authors considered only manufacturing trade within the
European Union in a shorter sample.
Also, the variable real capital stock is always positive and significant. This implies that there is a link
between export demand and cumulative investments, supporting the new trade and growth theories by
Krugman (1989), Helpman and Krugman (1989) and Grossman and Helpman (1993), which emphasise the
role played by product differentiation and innovation. The size of the real capital stock elasticity varies
between countries, indicating differences in the effectiveness of changes in the resource base in expanding a
country’s penetration of the world market. The coefficient is never inelastic, indicating that non-price
competitiveness variables are significant as well as price competitiveness and foreign demand. The non-price
competitiveness variable has the strongest effect on exports in Greece, Italy, and Spain, with elasticity
varying from 3.81 to 4.35. In the recent literature the only study that includes real capital for one of the
GIIPS group is that one by Athanasoglou and Bardaka (2010), that found for Greece a coefficient a bit
smaller compared to this study and equal to 1.265.
In a nutshell, foreign demand, price, and non-price comptetitiveness are all relevant drivers of real exports.
Excluding model (a), which does not have a weighted income variable, and is therefore less precise, the
baseline specification is equation (b) based on the smallest values of the Aikaike Information and Swarzh
criterion, and the highest values of the loglikelihood (Tables 6, 7, 8). In addition, specification (b) considers
the real effective exchange rate based on CPI, which is a better gauge for exports of goods and services;
instead real effective exchange rates based on ULC are a more reliable competitiveness measure for
manufacturing exports. The baseline specification suggests that there are some differences among the GIIPS
group: real exchange rates are less effective in adjusting the trade balance for Italy while they have a more
significant impact for Greece, Ireland, Portugal and Spain. This means that price competitiveness is less
valuable for stimulating exports for Italy, but it is able to revert trade deficits in the other GIIPS countries.
For Greece and Italy, foreign demand and non-price competitiveness play a major role in adjusting any trade
14
imbalance. Price competitiveness affects exports to a lesser extent. Indeed, as Amable and Verspagen (1995)
suggested, in the long run a country cannot expect to see its exports grow because of a continuous decrease
in relative price. Therefore, since prices are less important to stabilising current accounts, policy makers
should be aware of the impact of incentives on productive investments in exports. A final element that
emerges from the estimates is that compared to the traditional estimations, accounting for quality/variety
through the non-price competitiveness factor leads to relatively higher export price elasticities, generally
superior to 1. This is in line with Crozet and Erkel-Rousse (2004) according to whom adding a quality
variable into a trade equation would enable one to suppress the (positive) indirect effect of product quality
through prices from the (negative) overall relative price effect. The relative price contribution would thus
become a pure price effect, which has an unambiguous negative impact on market shares, while the overall
positive influence of product quality would be captured by the quality factor.
Tables 6, 7, and 8 report the short-run dynamics for the three specifications. In the short period, the
variations in the real effective exchange rate do not have tendentially any significant influence on changes in
real exports. This may be due to latent J-effects that indicate that a real depreciation does not immediately
ameliorate trade balances. The response of exports to income changes tends to be more immediate; short-run
elasticities have, in fact, the correct sign and are statistically significant. Real capital stock elasticities are
significant only for Italy and Spain. Consequently, in the short run, the GIIPS exports are more dominated by
movements of foreign demand, while changes in price and non-competitiveness take longer to affect export
performance.
The properties of the residuals of the estimated model have been analysed carefully. The system residual
Lagrange-Multiplier test for autocorrelation shows that the null of no residual correlation up to lag 9 cannot
be rejected. The Cholesky test proves that residuals are multivariate normal, and serial correlation is absent
in the residuals. There is also an absence of multivariate heteroskedasticity. Finally, the estimated model is
also “dynamically stable” as confirmed from Chart 4, showing that the inverse roots of the AR characteristic
polynomial lie in the unit circle7.
6. Conclusions
In order to explore the drivers of real export demand in the five countries of the Euro Area’s periphery, the
study has extended the traditional Imperfect substitutes model to include a non-price competitiveness factor.
The latter is proxied with capital stock which mirrors the role of supply-side factors in influencing a
country’s competitiveness and exports. A Vector Autoregressive Error Correction Model based on the
Johansen method was carried out for three specifications using quarterly data ranging from 1980 to 2012.
The long-run price, non-price and income elasticities have the expected signs and are highly significant. The
real effective exchange rate variable mirrors the country’s international competitiveness. It reflects relative
changes in the prices of a country’s export goods and services due to changes in nominal exchange rates and
inflation or labour cost differentials. In particular, the long run price elasticities of the baseline specification
vary between -2.58 and -0.99. In all GIIPS countries, except Italy, the estimated price elasticities are greater
than unity. This implies a fairly large response of exports to changes in relative prices. The higher the price
elasticity, the more competitive the international market for exports of a particular country, and hence the
more successful a real devaluation will be in promoting export revenues. For Italy, a real depreciation is not
sufficient to adjust current account imbalances.
7
The variance decomposition based on Monte Carlo repetitions confirms that there is a long-run relationship among variables for
each specification, and that all the determinants together have a certain power to predict real exports of goods and services.
15
The long-run income elasticity of the baseline specification varies between 1.03 and 2.67. These values fall
in the range identified by the literature. The higher the income elasticity of the export demand, as in the case
of Ireland and Greece, the more powerful exports will be as an engine of growth.
The long run non-price competitiveness elasticities are all above unity and play a key role in driving exports.
Therefore, failure to include non-price competitiveness among the explanatory variables, as in most of the
existing studies, leads to model misspecification.
In the short term, the effects of price and non-price competitiveness on exports are limited, while the reaction
of exports to income changes is immediate. Therefore, in the short term, exports are dominated by
movements of foreign demand, which becomes a crucial determinant of economic performance in the GIIPS,
while changes in competitiveness take longer to affect export performance.
The cointegration equations are robust in terms of autocorrelation, normality and homoskedasticity of
residuals, and the estimated models are remarkably stable despite the sizable fluctuations of exports during
the period of investigation.
In synthesis, our results point to the important role for relative prices, and, above all, non-price
competitiveness factors in determining a country’s export success. The most straightforward policy
implication is that there is a certain responsiveness of traded quantities to relative prices; therefore, policy
measures aimed at reducing relative prices (e.g. easing tax burdens on labour and business income,
simplifying lay-off procedures and streamlining legal procedures) would contribute to reducing a country’s
“external imbalance”, but for its complete resolution, policy manoeuvres should be devoted to ameliorating
non-price competitiveness. In this respect, policy actions could include innovation-promoting activities,
improvements in product variety and quality, the creation of a more efficient investment environment and an
increase in investment and R&D expenditures.
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Appendix
Export modelling variables
Export Index 2005=100
Foreign Demand 2005=100
Real Effective Exchange Rates Index - Cpi Based 2005=100
Real Effective Exchange Rates Index – Unit labour cost 2005=100
Real Capital Stock 2005=100
Exports of goods and services, in real terms, reference year
2005, US dollars,
Codes: GRXXTR$.D IRXXTR$.D ITXXTR$.D PTXXTR$.D
ESXXTR$.D
Two types of proxy are used to evaluate foreign demand.
1) The real world GDP in US dollars minus GIIPS’ real GDP in
US dollars adjusted for purchasing power parity (PPP) is
considered.
Codes: WDXGDPP.C GRXGDPP.C IRXGDPP.C
ITXGDPP.C PTXGDPP.C ESXGDPP.D
2) Foreign demand is gauged as a weighted average of the
import volumes of main trading partners, weights defined as the
share of each destination in total exports. The weighting system
is drawn from the ECB which uses Eurostat Quarterly National
Accounts database “Exports and imports by Member States of
the EU/third countries” to construct weights.
REER based on consumer price index, seasonal adjusted
International Financial Statistics IMF, via Datastream
Codes: GRQ..RECE, IRQ..RECE, ITQ..RECE, PTQ..RECE,
ESQ..RECE
REER based on unit labour costs, seasonal adjusted
International Main Economic Indicator, OECD via Datastream
Codes: GROCC021F, IROCC021F, ITOCC021F, PTOCC021F,
ESOCC021F
Capital stock in real terms, Oxford, via Datastream
Codes:
GRXKCAP.C,
IRXKCAP.C,
ITXKCAP.C,
PTXKCAP.C, ESXKCAP.C
Table 5 Descriptive statistics
Mean
Maximum
Minimum
Std. Dev.
Skewness
Sum Sq. Dev.
Observations
lgr_exp
4.070515
4.788437
3.309828
0.435046
0.073414
24.22596
129
lgr_reer
4.522933
4.685090
4.301630
0.094461
-0.162508
1.142123
129
Mean
Maximum
Minimum
Std. Dev.
Skewness
Sum Sq. Dev.
Observations
lir_exp
3.568422
4.830771
2.044443
0.960443
-0.129534
118.0736
129
lir_reer
4.539817
4.747017
4.385022
0.079731
0.235263
0.813694
129
lgr_reer_ulc
4.493847
4.669365
4.230477
0.137078
-0.354794
2.123323
114
lir_reer_ulc
4.493847
4.669365
4.230477
0.137078
-0.354794
2.123323
114
18
lgr_fd
3.775696
4.837611
2.322306
0.791765
-0.369144
80.24207
129
lgr_y
4.314670
4.843142
3.848502
0.292332
0.119513
10.93860
129
lgr_rks
4.397071
4.763132
4.072278
0.204131
0.355078
5.333666
129
lir_fd
3.948750
4.880441
2.768824
0.627590
-0.222470
50.41532
129
lir_y
4.315597
4.842162
3.851475
0.291362
0.121218
10.86614
129
lir_rks
3.977666
4.847173
3.031832
0.608619
-0.136804
47.41336
129
Mean
Maximum
Minimum
Std. Dev.
Skewness
Sum Sq. Dev.
Observations
lit_exp
4.211903
4.765520
3.516473
0.392273
-0.299871
19.69641
129
lit_reer
4.599865
4.762259
4.415099
0.071174
0.512297
0.648421
129
lit_reer_ulc
4.567855
4.780299
4.200505
0.153217
-0.385278
2.652727
114
lit_fd
3.868616
4.849091
2.552051
0.709418
-0.273514
64.41898
129
lit_y
4.310395
4.848586
3.839408
0.296534
0.131325
11.25534
129
lit_rks
4.296453
4.702244
3.748910
0.290350
-0.216639
10.79083
129
Mean
Maximum
Minimum
Std. Dev.
Skewness
Sum Sq. Dev.
Observations
lpt_exp
4.129405
4.862219
3.157645
0.520102
-0.393708
34.62475
129
lpt_reer
4.486667
4.641599
4.241758
0.118316
-0.481821
1.791845
129
lpt_reer_ulc
4.521623
4.626736
4.309456
0.076974
-0.783171
0.669520
114
lpt_fd
3.891596
4.856979
2.566343
0.693432
-0.328606
61.54846
129
lpt_y
4.314632
4.842459
3.849371
0.292102
0.121862
10.92145
129
lpt_rks
4.211668
4.646976
3.637879
0.329778
0.014003
13.92043
129
lsp_reer_ulc
lsp_exp
lsp_reer
lsp_fd
lsp_y
lsp_rks
Mean
3.980108
4.556631
4.543331
3.995194
4.311892
4.245598
Maximum
4.852349
4.703476
4.752037
4.917571
4.845524
4.787041
Minimum
2.933397
4.343416
4.399744
2.746738
3.849530
3.675700
Std. Dev.
0.598102
0.084567
0.091918
0.665990
0.290818
0.348186
Skewness
-0.108066
-0.279552
0.541749
-0.252646
0.136165
-0.045398
Sum Sq. Dev.
45.43117
0.908251
0.954738
56.32997
10.74102
15.39662
Observations
129
129
114
129
129
129
Notes: Table 5 provides information on the mean, minimum and maximum values of the considered variables. It gives then the
dispersion of the variables with respect to their mean. Skewness measures how symmetric the data is, in other words is there a
tendency for the data to be positive or negative. For a symmetric distribution, like the normal, the median is the average and so the
skewness is zero. High standard deviation suggests that a variable is considerably volatile. l=logs, exp= real exports of goods and
services reer=real effective exchange rate CPI based, reer_ulc=real effective exchange rate ULC based, fd=foreign demand, y=real
world income, rks=real capital stock, gr=Greece, ir=Ireland, it=Italy, pt=Portugal, sp=Spain.
Table 6 VECM System short-run coefficients, Specification (a)
Variables
∆ ln real export index t -1
∆ ln real export index t-2
∆ ln real export index t-3
∆ ln real export index t-4
∆ ln CPI reert -1
∆ ln CPI reer-2
∆ ln CPI reert-3
∆ ln CPI reert-4
∆ ln y t -1
∆ ln yt-2
∆ ln yt-3
∆ ln yt-4
∆ ln rks t -1
∆ ln rkst-2
∆ ln rkst-3
∆ ln rkst-4
Rsquared
S.E. equation
AIK
SC
Log Likelihood
Greece
-0.212 (-1.89)
-0.309 (-3.88)
Ireland
0.091 (1.04)
-0.130 (-1.48)
0.180 (2.11)
Italy
-0.001 (-0.02)
-0.034 (-0.42)
0.172 (0.68)
-0.134 (-0.54)
-0.141 (-1.20)
-0.064 (-0.52)
-0.065 (-0.56)
-0.121 (-0.85)
0.016 (0.12)
2.271 (2.22)
4.124 (4.05)
0.321 (0.68)
-0.307 (-0.61)
2.150 (3.71)
1.740 (3.91)
1.006 (2.15)
-0.462 (-0.51)
0.927 (0.99)
0.035 (0.71)
-0.051 (-1.04)
-0.071 (-1.47)
1.201 (2.06)
1.208 (2.08)
51.28
0.05
-2.72
-2.35
187.0111
54.38
0.02
-4.41
-4.01
293.7810
53.12
0.02
-4.48
-4.18
295.0439
Notes: The symbol ∆ is the difference operator. Figures in brackets are t-statistics.
19
Portugal
0.135 (1.34)
-0.018 (-0.17)
0.072 (0.76)
0.037 (0.40)
-0.137 (-1.21)
-0.174 (-1.51)
-0.116 (-0.99)
-0.078 (-0.66)
1.003 (2.42)
0.409 (0.99)
-0.258 (-0.63)
-0.204 (-0.49)
-1.061 (-0.44)
-0.135 (0.04)
-0.518 (-0.14)
1.042 (0.42)
49.62
0.02
-4.78
-4.26
314.9683
Spain
-0.223 (-2.85)
-0.046 (-0.63)
0.108 (0.77)
-0.109 (-0.74)
1.189 (2.59)
1.828 (3.88)
5.393 (1.86)
-2.569 (-0.90)
55.01
0.02
-4.45
-4.13
292.2705
Table 7 VECM System short-run coefficients, Specification (b)
Variables
∆ ln real export index t -1
∆ ln real export index t-2
∆ ln real export index t-3
∆ ln real export index t-4
∆ ln CPI reert -1
∆ ln CPI reer-2
∆ ln CPI reert-3
∆ ln CPI reert-4
∆ ln fd t -1
∆ ln fdt-2
∆ ln fdt-3
∆ ln fdt-4
∆ ln rks t -1
∆ ln rkst-2
∆ ln rkst-3
∆ ln rkst-4
Rsquared
S.E. equation
AIK
SC
Log Likelihood
Greece
-0.237 (-2.10)
-0.374 (-4.11)
Ireland
0.087 (1.04)
-0.205 (-2.40)
Italy
-0.203 (-2.26)
-0.006 (-0.07)
0.132 (0.26)
-0.139 (-0.54)
-0.061 (-0.51)
-0.016 (-0.14)
-0.245 (-2.07)
0.012 (0.10)
0.281 (1.82)
0.032 (0.15)
0.248 (2.32)
-0.035 (-0.30)
0.357 (3.68)
0.091 (0.90)
-0.662 (-0.72)
0.715 (0.76)
0.074 (1.46)
-0.040 (-0.79)
1.330 (2.26)
1.252 (2.18)
53.72
0.06
-2.70
-2.62
175.5134
53.69
0.03
-4.36
-4.09
286.6395
52.48
0.02
-4.53
-4.24
298.63
Portugal
0.064 (0.62)
-0.026 (-0.26)
0.081 (0.83)
0.001 (0.01)
-0.151 (-1.32)
-0.137 (-1.18)
-0.197 (-1.71)
-0.128 (-1.13)
0.244 (2.72)
-0.058 (-0.60)
0.144 (1.56)
0.559 (1.00)
-0.268 (-0.11)
0.343 (0.09)
-2.084 (-0.56)
1.921 (1.78)
51.65
0.01
-4.82
-4.29
314.5027
Spain
-0.334 (-3.77)
-0.077 (-0.86)
Portugal
-0.004 (-0.30)
-0.060 (-0.47)
0.002 (0.02)
-0.045 (-0.36)
-0.245 (-1.27)
-0.055 (-0.27)
-0.163 (-0.81)
0.103 (0.51)
0.106 (4.57)
0.153 (4.63)
-0.849 (-1.21)
0.559 (1.00)
-0.115 (-0.04)
2.691 (0.64)
0.363 (0.09)
-2.487 (-0.60)
51.74
0.02
-4.67
-3.82
260.7157
Spain
-0.237 (-1.75)
-0.187 (-1.31)
-0.100 (-0.63)
-0.187 (-1.12)
0.211 (1.81)
0.087 (0.73)
1.805 (1.95)
0.966 (0.30)
54.25
0.03
-4.35
-3.94
281.2842
Notes: The symbol ∆ is the difference operator. Figures in brackets are t-statistics.
Table 8 VECM System short-run coefficients, Specification (c)
Variables
∆ ln real export index t -1
∆ ln real export index t-2
∆ ln real export index t-3
∆ ln real export index t-4
∆ ln ULC reert -1
∆ ln ULC reer-2
∆ ln ULC reert-3
∆ ln ULC reert-4
∆ ln fd t -1
∆ ln fdt-2
∆ ln fdt-3
∆ ln fdt-4
∆ ln rks t -1
∆ ln rkst-2
∆ ln rkst-3
∆ ln rkst-4
Rsquared
S.E. equation
AIK
SC
Log Likelihood
Greece
-0.254 (-2.77)
Ireland
0.167 (1.64)
0.243 (2.46)
Italy
-0.118 (-1.16)
-0.017 (-0.16)
-0.107 (-1.08)
0.155 (0.79)
0.051 (0.43)
0.069 (0.57)
-0.043 (-0.46)
-0.026 (-0.27)
-0.054 (-0.55)
0.227 (1.76)
0.297 (2.32)
-0.104 (-0.77)
0.546 (4.66)
0.005 (0.04)
0.195 (1.58)
-0.252 (-0.51)
0.079 (1.39)
-0.036 (-0.63)
1.663 (2.26)
2.693 (2.15)
1.011 (1.34)
52.80
0.06
-2.65
-2.43
157.2028
51.56
0.03
-4.13
-3.89
241.3467
53.40
0.03
-4.32
-3.90
254.6114
0.016 (0.10)
-0.302 (-1.90)
0.290 (1.84)
0.021 (0.11)
0.903 (0.86)
1.281 (1.66)
50.02
0.03
-4.04
-3.37
230.2428
Notes: The symbol ∆ is the difference operator. Figures in brackets are t-statistics.
Table 9 VEC Residual Serial Correlation LM, Specification (a)
Greece
LM-Stat
Ireland
Prob
LM-Stat
Italy
Prob
LM-Stat
Portugal
Prob
1
20.32224
0.2061
21.61471
0.1200
22.65266
0.1233
2
17.16629
0.3749
26.43925
0.0482
22.43035
0.1298
3
20.99202
0.1788
20.34841
0.2049
28.33474
0.0288
4
22.75196
0.1000
22.18621
0.1104
24.88778
0.0900
5
30.26525
0.0167
21.19855
0.1710
20.11214
0.2152
6
25.97999
0.0543
37.23361
0.0019
11.37853
0.7855
7
28.27989
0.0293
28.43814
0.0280
26.52639
0.0471
8
17.97595
0.3200
23.45241
0.1022
23.83598
0.1000
9
19.90168
0.2247
8.873575
0.9185
30.35391
0.0163
Null Hypothesis: no serial correlation at lag order h Probs from chi-square with 16 df.
20
Spain
LM-Stat
Prob
LM-Stat
Prob
19.96912
15.02938
25.77244
17.22299
18.91296
23.17137
18.37478
19.03136
22.74871
0.2400
0.5225
0.0573
0.3713
0.2732
0.1070
0.3024
0.2670
0.1206
25.58052
21.51551
23.21207
22.99221
32.91693
38.26187
22.51090
26.98624
24.37494
0.0712
0.2010
0.1082
0.1200
0.0076
0.0014
0.1274
0.0870
0.0816
Table 10 VEC Residual Serial Correlation LM, Specification (b)
Greece
LM-Stat
Ireland
Prob
LM-Stat
Italy
Prob
Portugal
LM-Stat
Prob
1
11.30663
0.7902
22.78979
0.1195
29.01386
0.0238
2
22.18631
0.1373
19.83725
0.2276
19.14189
0.2614
3
20.55861
0.1961
9.260784
0.9023
23.48192
0.1014
4
22.61092
0.1245
31.74501
0.0108
15.19697
0.5103
5
17.65320
0.3446
14.54828
0.5579
17.41363
0.3593
6
21.98941
0.1435
16.32026
0.4308
12.25144
0.7265
7
16.16873
0.4412
17.18060
0.3740
15.45905
0.4913
8
18.29711
0.3068
30.52879
0.0154
23.00334
0.1174
9
10.10541
0.8611
6.891042
0.9753
22.31279
0.1334
Null Hypothesis: no serial correlation at lag order h Probs from chi-square with 16 df.
Spain
LM-Stat
Prob
LM-Stat
Prob
16.68984
9.692831
25.64302
13.29415
13.09098
12.70663
19.80468
10.41361
20.87663
0.3001
0.8822
0.0593
0.6511
0.6661
0.6941
0.2291
0.8442
0.1833
24.01916
21.60637
10.77662
18.26840
21.31016
23.11737
14.62664
20.12477
10.10843
0.0940
0.1520
0.8231
0.4400
0.1669
0.1003
0.5521
0.2147
0.8609
Table 11 VEC Residual Serial Correlation LM Tests, Specification (c)
Greece
LM-Stat
Ireland
Prob
LM-Stat
Italy
Prob
Portugal
LM-Stat
Prob
1
12.33523
0.7206
8.762412
0.9229
24.98528
0.0701
2
14.30244
0.5762
30.69630
0.0147
20.07131
0.2170
3
15.51644
0.4872
7.531023
0.9616
9.869930
0.8733
4
17.71925
0.3406
22.47039
0.1286
20.03939
0.2185
5
15.34283
0.4997
16.66214
0.4078
27.24718
0.0388
6
16.46633
0.4209
8.956203
0.9152
13.54932
0.6322
7
13.90135
0.6061
22.34039
0.1325
10.73899
0.8253
8
13.79301
0.6141
31.23138
0.0126
34.66168
0.0044
9
12.33523
0.7206
8.762412
0.9229
24.98528
0.0701
Null Hypothesis: no serial correlation at lag order h Probs from chi-square with 16 df.
Spain
LM-Stat
Prob
LM-Stat
Prob
19.50994
33.23022
18.39597
10.04268
16.20827
10.40830
14.28422
31.69045
19.50994
0.2431
0.0069
0.3012
0.8644
0.4385
0.8445
0.5775
0.0110
0.2431
19.40233
11.05044
17.73558
13.08429
22.84019
21.10221
11.95133
21.02852
19.40233
0.2484
0.8064
0.3396
0.6666
0.1181
0.1746
0.7473
0.1705
0.2484
Table 12 VEC Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)
Specification (a)
Chi-sq
Joint
243.1629
Greece
304.6744
Ireland
260.3914
Italy
309.5803
Portugal
238.1902
Spain
Null Hypothesis: no heteroskedasticity
Prob
0.6098
0.4141
0.2060
0.3752
0.3216
Specification (b)
Chi-sq
Joint
208.3395
287.3308
272.3610
378.7127
254.8460
21
Prob
0.3283
0.1111
0.3339
0.6496
0.5786
Specification (c)
Chi-sq
Joint
146.3438
294.5024
262.2214
522.2424
246.9548
Prob
0.1551
0.1463
0.2760
0.9957
0.4025
Chart 4 Inverse Roots of AR Characteristic Polynomial, specification (a)
Ireland
Greece
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
-1.0
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
-1.5
1.5
-1.5
-1.0
-0.5
1.0
1.5
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
-1.0
-1.0
-1.0
-0.5
0.0
0.5
1.0
-1.5
1.5
-1.5
0.5
1.0
1.5
-1.0
Spain
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5
0.5
Portugal
Italy
-1.5
-1.5
0.0
-1.0
-0.5
0.0
22
-0.5
0.0
0.5
1.0
1.5
Inverse Roots of AR Characteristic Polynomial, specification (b)
Ireland
Greece
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
-1.0
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
-1.5
1.5
-1.5
-1.0
-0.5
Italy
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
-1.0
-1.0
-1.0
-0.5
0.0
0.5
1.0
-1.5
1.5
-1.5
-1.0
Spain
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Portugal
1.5
-1.5
-1.5
0.0
0.5
1.0
1.5
23
-0.5
0.0
0.5
1.0
1.5
Inverse Roots of AR Characteristic Polynomial, specification (c)
Ireland
Greece
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
-1.0
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5
-1.5
-1.0
-0.5
Italy
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
-1.0
-1.0
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5
-1.5
-1.0
-0.5
Spain
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Portugal
1.5
-1.5
-1.5
0.0
0.5
1.0
1.5
24
0.0
0.5
1.0
1.5