Paper

Exchange Rate and Trade Balance: An Empirical Analysis
for France, Germany and the Netherlands
Aichat NASSIROU∗
February 2015
Abstract
The J-curve phenomenon reflects a negative effect of a devaluation of the exchange
rate on the current account in the short run with improvement following in the long run.
This paper investigates whether there is a J-curve in bilateral exchange rate between
three countries in the Euro area namely France, Germany and the Netherlands; and their
major partner the United States using a vector error correction model (VECM). The gen-
eralized impulse response functions are used to better illustrate the current account re-
sponses du to a shock in the exchange rate. The VECM suggests a long run relationship
between the variables both for France and Germany. In Germany, we find two curves
that resemble to that of a J-curve each year. However in France, there is no evidence for a
J-curve phenomenon because a devaluation worsen the current account in the long run.
In fact, for the Netherlands, we find no long run relationship among the variables so we
estimate with a Vector Autoregressive (VAR) model. There is a curve that thus resembles
to that of the J-curve even if the Netherlands does not exhibit a statistically significant
J-curve phenomenon.
Key words: Bilateral exchange rate, current account, J-curve, VECM approach, VAR.
∗
Master 2 Monnaie, Finance et Gouvernance at Universty Lumière Lyon 2
1

Contents
1 Introduction 4
2 Literature Review 5
3 Methodology 7
3.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Methods of analyzing the J-curve phenomenon . . . . . . . . . . . . . . . . . 10
4 Data description and Sources 13
4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Results on the unit root test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5 Empirical model and results 18
5.1 France . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.2 Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.3 The Netherlands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6 Summary and conclusion 30
7 References 32
8 Appendices 32
List of Figures
1 Consumer price index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Real bilateral exchange rate and trade ratio in levels: France . . . . . . . . . . 15
3 Real bilateral exchange and trade ratio in levels: Germany . . . . . . . . . . . 16
4 Real bilateral exchange and trade ratio in levels: the Netherlands . . . . . . . 16
5 Real income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6 Impulse RER and response LTBFR . . . . . . . . . . . . . . . . . . . . . . . . . 21
7 Impulse LYFR, response all . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
8 Impulse LY ∗, response all: France . . . . . . . . . . . . . . . . . . . . . . . . . 23
9 Impulse RER, response all: Germany . . . . . . . . . . . . . . . . . . . . . . . . 25
10 Impulse in the national income: Germany . . . . . . . . . . . . . . . . . . . . . 26
11 Impulse DRER, response all: the Netherlands . . . . . . . . . . . . . . . . . . . 27
12 Impulse DLYHOL, response all . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
13 Impulse DLTBHOL, response all . . . . . . . . . . . . . . . . . . . . . . . . . . 28
14 Impulse DLY ∗, response all: the Netherlands . . . . . . . . . . . . . . . . . . . 29
15 Current Account and RER: in ﬁrst difference . . . . . . . . . . . . . . . . . . . 36
16 Real income: in ﬁrst difference . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
17 Resids Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
18 Resids France . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
19 Resids for the Netherlands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2

List of Tables
1 Augmented Dickey-Fuller unit root test: probability . . . . . . . . . . . . . . . 17
2 LM tests of Breusch-Godfrey: France . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Long run estimation: France . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 Dynamics relationship: France . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5 LM tests of Breusch-Godfrey: Germany . . . . . . . . . . . . . . . . . . . . . . 22
6 Trace test: Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
7 Maximum Eigenvalue: Germany . . . . . . . . . . . . . . . . . . . . . . . . . . 23
8 Long run estimation: Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
9 Dynamic estimations: Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
10 Estimated VAR for the Netherlands . . . . . . . . . . . . . . . . . . . . . . . . . 26
11 Unit root test for Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
12 Unit root test for France . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
13 Unit root test for the Netherlands . . . . . . . . . . . . . . . . . . . . . . . . . . 33
14 Johansen Test for the Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
15 Johansen Test for France . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
16 Lag length criteria for the Netherlands . . . . . . . . . . . . . . . . . . . . . . . 34
17 Granger Causality: DLTBHOL . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
18 Granger Causality: DRER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
19 Granger Causality: DLYHOL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
20 Granger Causality: DLY ∗ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3

1 Introduction
The trade balance is the difference between exports and imports of a country. When exports
are higher than imports in a country, it is said that the country has a trade surplus. Oth-
erwise, it has a trade deficit. The exchange rate of a currency has a direct impact on the
trade balance of a country. According to the approach of the balance of payments, when the
exchange rate of a country depreciates, exports volume increase (for domestic products be-
come cheaper for foreigners) and imports volume decrease (can buy less products foreigners
because they are very expensive). Then it seems possible to conclude that ”the depreciation
of the exchange rate improves exports while decreasing imports and therefore a deprecia-
tion necessarily improve a country’s trade balance.” Furthermore this viewpoint, there is the
Marshall-Lerner condition which is based on how a reduction in the value of a national cur-
rency does not immediately causes an improvement in the trade balance. His condition of
the critical elasticities conditions states that a depreciation will improve the balance of pay-
ments if the sum of the elasticities of exports and imports is higher than unity. If this is true,
then we can infer that a real depreciation in the long run has a positive effect on the current
account of a country. This condition is rather that of a long-run analysis because agents take
time to adjust their behavior to the fluctuations in the exchange rate. We are not going to
focus on the Marshall-Lerner condition but rather that of the J-curve. Contrary to what pre-
dicted the approach of the balance of payments, depreciation worsens the trade balance of
a country: the over-time phenomenon J-curve. A depreciation in the presence of a J-curve
may have more than a perverse effect on the trade balance.
On 1 January 1999, the Euro became the currency of 300 million Europeans. During the
first three years, the Euro was an invisible currency, which was used only for accounting
purposes, for example for electronic payments. Banknotes and coins were introduced on
1 January 2002, when they replaced the fixed conversion rate, the banknotes and coins de-
nominated in national currencies (Belgian franc, Deutsch mark). The Euro zone includes the
countries that have the single currency the Euro. It is formed by 18 countries. Several criteria
are important to join the Euro area: a public deficit below 3% of GDP; public debt not ex-
ceeding 60% of GDP; an inflation targeting objective and independent monetary authority.
Since the 2008 crisis, the countries of the Euro area have increased their propensity to export
but their market shares are reduced.
The Euro area has suffered in the year 2008 from a crisis that has affected the level of
growth but also its balance of payments. This has created a deterioration following by the
fluctuations of the current accounts of the Member States. The Euro area is characterized
by an heterogeneity between the countries which have formed it and this is partly a cause
of imbalances in the current account of the zone. Germany and the Netherlands are the
only countries that have maintained the current account surpluses closed to their pre-crisis
level. These two countries are among the leading exporters of the area. Germany is the third
largest exporter of goods after the United States and China. It is the first global commercial
power in 2009. In 2006, the trade surplus is partly due to the quality of products it offers and
the relocation of its products. The trade balance of the Euro area and surplus increased with
the amount of Germany. Collectively, the Euro area is one of the largest economies in the
world with a contribution of 13.1% of global GDP. French economy is an economy increas-
ingly open that plays an important role in international trade. However, since 2003, France
exported fewer goods than it imports. Unlike these two countries, France has a trade deficit.
The trade deficit since 2004 has become a weight on GDP. The France experienced multiple
4

periods during the 20th century when inflation levels were too strong, have downgraded its
economic competitiveness while remaining fixed its currency vis-à-vis other currencies in a
fixed exchange rate. Devaluations were then required.The Netherlands have a prosperous
and open economy, which depends heavily on foreign trade.
The purpose of this paper is to examine whether and how exchange rate affects trade
balance in three European countries: France, Germany and the Netherlands. What will
be the different effects of the depreciation on the trade balance of these countries? Can a
country with a trade surplus such as Germany and the Netherlands, could face a J curve
phenomenon in that a devaluation will have an effect of improving its current account in
the long run? Or this phenomenon will point instead for a deficit country like France? To
analyze this phenomenon, we use quarterly time series data over the period 1999 : 1−2014 :
1. Compared to most of the above mentioned papers, our sample includes years in recession
after the crisis of 2008. In order to answer these questions, we will first do a review of
the existing literature on the subject. Secondly, we will develop a methodology through
cointegration model to analyze the dynamics of the short and long run of our different series.
Thirdly, we will analyze the data collected and on which we will perform our study and;
finally bring out the different results and provide conclusions that would allow us to confirm
or deny the existence of a J-curve phenomenon for the countries in our sample.
2 Literature Review
The theoretical foundations for analyzing the impact of currency depreciation on trade cen-
tered on the J-curve effect and the Marshall-Lerner condition. Given these implications of
the J-curve, its empirical estimation has been subject of interest. Several empirical studies
have been conducted on whether a depreciation leads to long-run improvement in the trade
balance or if so whether a J-curve pattern occurs. Magee (1973) was not the only article
published in the 1970s, on the J-curve subject. Bahmani-Oskooee (1994), Jörg Clostermann
(1996), Mariarosaria and Jeroen (2014), to mention a few, also provided research.
Theoretical studies found no evidence of a J-curve phenomenon. Maryline and Jane
(2010) studied the impact of the exchange rate on the trade balance of China, Euro Area
and the United States in the agricultural and manufacturing sectors to better understand the
long-term effects. According to their results, they found a much greater effect on the impact
of the exchange rate on the trade balance between the United States and China than in the
US-Euro Area or Euro Area-China. They found a short-term impact of these two variables
yet the interpretation is a little more complex because it can be either negative or positive.
Their analysis didn’t confirm the existence of a J-curve. They have confirmed other results
in the literature according to which national income is a key factor in the current account.
Also, foreign income plays an important role in the determination of national exports. Pavle
Petrovic and Mirjana Gligoric (2009) didn’t use the foreign income because it is not sig-
nificant. Magee (1973) joined the idea of Maryline and Jane (2010) that national income is
decisive and we should expect a positive impact of the latter on the current account. In this
case, if national income increases, domestic production may grow faster than consumption,
and this will reduce imports volumes. In contrary, Jacob Jorlén (2011) found that there is no
J-curve in the bilateral merchandise trade between Sweden and Germany. Rose and Yellen
(1989) found no statistically reliable indications of a J-curve for the Us Bilateral trade with
respect to the G-7 countries or for aggregate US trade.
5

Working on the same country (Germany) as Jorlén, Jörg Clostermann (1996) noted that
the balance of trade curve thus resembles a J-curve. In the long-run, importers like exporters,
pursue a policy of exchange rate pass-through. Since, in the short term, movements in im-
port prices exceed the change in the volume of exports and, on the other hand, this ratio is
reversed in the long run, exchange-rate-induced movements in Germany’s trade balance are
characterized by an initially anomalous reaction which subsequently gives way to abnormal
balance of trade reaction. Then Eric Ben Kamoto (2006) found an evidence of the J-curve on
the South Africa trade balance by using a vector error correction model (VECM), however,
Malawi does not exhibit a statistically significant J-curve phenomenon. Mohsen Bahmani-
Oskooee (1985) found the existence of a J curve phenomenon in developing countries such
as Greece, India, Korea, using quarterly data from 1973 to 1980, but not for Thailand. A
devaluation has the same short and long term effect on the balance of payments. In another
article, Bahmani-Oskooee and Alse (1994) considered the relationship between the import-
to-export ratio and the real effective exchange rate for nineteen developed countries and
twenty developing countries using 1971:1 to 1990:4 data. In only six cases they found an evi-
dence for cointegration: Brazil, Costa-Rica, Ireland, the Netherlands, Singapore, and Turkey.
For the Netherlands, and Turkey, a depreciation results in short-run balance deterioration.
For R. Hacker and Abdulnasser (2003), there is a J-curve effect in five northern European
countries, namely Belgium, Denmark, the Netherlands, Norway, and Sweden using the gen-
eralized impulse response functions from a vector error-correcting model. This effect implies
that there will be a dip in the export-import ratio within le first half-year partner after the
depreciation. Arghyrou et al’s (2006) findings showed that a negative relationship exists
between the movement of the real effective exchange rate and the current account in the
EMU-member countries after controlling for the role of income growth. Pavle Petrovic and
Mirjana Gligoric (2009) suggested the existence of a J-curve effect due to devaluation in Ser-
bia using both Johansens and autoregressive distributed lag approach which gave similar
long-run estimates showing that real depreciation improves trade balance.
Several studies have been carried out by taking the total trade to analyze the impact of
the exchange rate on the current account. Beside these, there are many authors who use
literature on bilateral trade. The reason for this is that patterns of trade with one country can
even out patterns of trade with another country (Irandoust and al., 2006); called “aggregation
bias”. For more illustration of this, Bahmani-Oskooee and Harvey (2009) have investigated
the J-curve effect for Indonesia and her major trading partners to avoid the aggregation bias
in using trade data between Indonesia and the rest of the world. They found evidence of
the J-curve in five out of 13 trading partners. Alban Pllaha(2013) evaluated the effects of the
exchange rate on the bilateral trade between Albania and its main partners, namely the Euro
Area, China, Greece, Germany, Italy, Kosovo and Turkey. The study was conducted over a
period from 1998 to 2012 based on an error correction model. As results, we found that the
effect of the J-curve exists for Italy and Turkey. In the long-run, depreciation positively affect
the current account even if the effect is not significant in the short term.
However, it may be noted that the bilateral real exchange rate marks a failure in the
analysis of the current account. Pentti Kouri J. (1976) highlights the relationship of stability
and reliability of the exchange rate. The exchange rate between two countries is not stable
because it is the relationship between two relative prices of goods in different countries. One
of the conditions to make a stable exchange rate is to increase the stock of foreign assets to
reduce the surplus in the current account.
As can be seen, the results vary with each article and by each country. To summarize
6

the findings on the theoretical level, the effect of the exchange rate on the current account
is ambiguous: the impact may be positive or negative depending on the assumption and
sample of study. In the table bellow, we summarize the key points of the literature on the
J-curve phenomenon.
Authors Entitled Sample Methods Results
Alban Pllaha (2013) Effect of exchange rate Albania vs Euro area, China, EC model Yes for Italy and
on the bilateral trade Germany, Italy and Turkey Turkey
Bahmani-Oskooee Short-run versus 19 developed countries and EC modeling For the Netherlands:
and Alse (1994) long-run effects of devaluation 20 developing countries Short run deterioration balance
Jörg Clostermann(1996) The impact of the exchange rate Germany Mark-up model J-curve phenomenon occurs
on the balance of trade EC modeling
Maryline and To what extent do exchange rates China, Euro area and GARCH National income:
Jane (2010) and their volatility affect trade? the United states volatily key factor in the CA
Pavle Petrovic and Exchange rate and trade balance: Serbia Cointegration Yes. the foreign
Gligoric (2009) J-curve effect ARDL approach income: not significant
R-Hacker and Is the J-Curve Effect? Small North European VECM Yes: in 5 countries
Abdulnasser (2000) Economies? as the Netherlands
CA= Current Account; EC= Error Correction; ARDL= Autoregressive distributed lag
We then construct the following hypothesis for this study and we will test it: there is a
long-run relationship between the real exchange rate and the trade balance and an evidence
of a J-Curve phenomenon in France, Germany and the Netherlands. To test this hypothesis,
firstly, a cointegration analysis will be made and it will help answer part of our hypothesis
about the long-run relationship. If we find that this is true, then a devaluation will result in
a long-run in a positive impact on the current account. If it is not true, then there is no long
run relationship between them. On the other hand, the existence of a J-curve will be tested
using the generalized impulse response functions.
3 Methodology
3.1 Theory
3.1.1 The trade balance: basis
In this section, we will explain the basics of the current account balance, how it works and
what can affect it.
The current account is a Balance of Payment (BOP) component of a country. It is the
sum of exports and imports; where imports are recorded as negative sign. We have done
our analysis by focusing on Eric Ben Kamotos paper (2006) 1 in which he considered the
domestic income and import prices as the main determinants of the demand for imported
goods. We can then write the expression of the import demand as follows,
Dm = Dm(Y, Pm, Pd)
Where Dm, the domestic demand for imported goods, is positively dependent on the domes-
tic real income, Y , and negatively on the country’s relative price of imported goods, Pm;and
Pd is the general price level in the domestic currency.
1
Evidence on the trade balance on Malawi and South Africa
7

In the same way, we can write the expression of the supply for domestically produced
goods (equivalent to export demand by foreigners) to the rest of the world as:
Sx = Sx(Y ∗
, Px, E, Pf )
Where Sx is the quantity for exported goods to the rest of the world, Y ∗ is the foreign real
income, Px is the foreign price paid by domestic importers, Pf is the general price level in
the foreign country and E is the nominal exchange rate defined as the number of units of
foreign currency per unit of local currency (price of domestic currency in terms of foreign
currency).
We can notice that exports and imports do not depend on their respective prices. So we
will take into consideration their value relative to prices of same or similar products in the
importing country that affects trade flows. Then we have:
Dm = Dm(Y, RPm) (1)
Sx = Sx(Y ∗
, RPx) (2)
Where RPm and RPx are respectively the relative price of imports and exports. By this,
the relative price of imports can be defined as:
Dm =
Pm
Pd
=
EP∗
x
Pd
(3)
=
EPf
Pd
P∗
x
Pf
(4)
Dm = (RER)P∗
x (5)
Where P∗
x is the real foreign price of export and RER is the real exchange rate: an increase
means a depreciation of the domestic currency.
At equilibrium, the trade quantities and the relative price can be determined accordingly,
Dm = S∗
x, Sd = D∗
m (6)
Where S∗
x and D∗
m are foreign export supply and import supply respectively.
Then, we can deduce the expression of the trade balance (TB) as follows:
TB = P∗
x Sx − (RER)Dm (7)
In the Eric Ben Kamoto (2006) paper based on the bilateral trade model, the Eq.1 can be used
to rewrite the expression of the trade balance in a reduced form:
TB = TB(Y, Y ∗
, RER)
With the expected signs for the variation as follow: ∂TB
∂Y <0, ∂TB
∂Y ∗ >0, ∂TB
∂RER >0. This is the
traditional Keynesian function for the trade balance.
8

3.1.2 The J-curve phenomenon
One of the most cited authors on the phenomenon of the J-curve is the economist Anne O.
Krueger (1983). In her book untitled ”Exchange rate determination”, she said that the J curve
occurs because at the time an exchange rate change occurs, goods in transit and in contracts
were already purchased causing a lag time in the effect of exchange rate changes. Once those
transactions that had already been in progress prior to the rate adjustment are concluded,
subsequent commercial activity reflects the new competition environment, allowing the bal-
ance of trade to begin to improve. For Krueger, there are three conditions under which we
have the J-curve: firstly, the extent to which trade takes place under pre-existing contracts (as
contrasted with purchases made in spot markets); secondly, the degree to which there may
be asymmetric use of domestic currency and foreign currency in the making of contracts;
and finally, the length of the lags in the execution of contracts.
An early study by Stephen Magee (1973) distinguished three period following a deval-
uation: the currency contract period, the pass-through period and the quantity adjustment
period.
• The Currency Contract Period:
This marks the period during which the old contracts were signed before the devaluation fall
due after the devaluation. Since the effect of the exchange rate on the contracted trade can
be negative or positive, exporters prefer to sign contracts exchange on exports in a currency
that is likely to appreciate and importers on the contrary, in a currency likely to depreciate.
It is a short-run effect of the devaluation.
• The Pass-Through Period:
This refers to the behavior of international prices on contracts agreed upon after the deval-
uation has taken place but before it has effected significant changes in quantities. It is also a
short-run effect of the devaluation.
Depending on how the prices change, buying patterns will adapt, and this is, in turn, af-
fected by how much of the devaluation exporters are willing to pass through on their prices,
measured in the buyers currency. There are two possible reasons as to why the quantities
have not adjusted during this period: the first one is due to a perfectly inelastic supply since
exporters are not able to instantly change their sales abroad, and a second reason could be
that the demand throughout the period is perfectly inelastic since importers cannot instantly
find substitutions for the imported goods. the domestic-currency price of imports increases
as a consequence of the devaluation but the demand does not change, so that the outlay for
imports increase. The foreign- currency price of exports decreases but the demand remains
the same, so that the foreign currency receipts will decrease and their domestic currency
value will not change. This implies a deterioration of the trade balance.
• The Quantity Adjustment Period:
In this period, both prices and quantities can change. What happened in the Pass-through
period would affect the adjustment period. If the Marshall-Lerner condition is fulfilled, the
trade balance will improve. If quantities do not adjust as fasts as prices (frictions, reaction
lags, etc) the balance of payment may deteriorate before improving towards the new equi-
librium point.
9

Magee (1973) concludes that “there is no logical necessity for a country’s trade balance to
deteriorate, any more than for it to improve or remain constant ”2. Krueger seems to agree
with Magees conclusion in theory but added that, the short-run decline in trade balance
following a currency devaluation has become part of the J curve Hypothesis more as a result
of actual observation than theory.
3.2 Methods of analyzing the J-curve phenomenon
To test the existence of a J-curve phenomenon, we have several tools. We proceed as follows.
First, we analyze the time series properties of the data. Then, empirical tests to validate the
existence of a J-curve phenomenon will be on the basis of a Vector Error Correction model
(VECM) of Johansen in order to see if or not there is a vector cointegration between our dif-
ferent series. The variables in this case are non-stationary. If the results of the VECM show
that there is no cointegration in a series, then the estimation by a VAR (Vector Autoregres-
sive) model is necessary.
3.2.1 Unit root test
The economic and financial variables are rarely achievements of stationary processes. An es-
timate of these non-stationary variables have no meaning or gives ”spurious results.” Apply
to standard econometric methods can lead to estimate regressions that look very statistically
correct between variables which actually have no link between them. This leads to under-
stand that the identification and characterization of nonstationarity are really important.
What then characterizes a stationary process? its average must be constant, reflecting the
stability of its behavior over time. It has a property of homoscedasticity insofar its variance
is independent of time; means constant too. The covariance between observations depends
only on the length of time between them and not on the point of time at which they are
studied.
There are a large number of unit root test. The pioneering work in this field are those of
Dickey and Fuller (1979, 1981). Dickey-Fuller tests are parametric tests based on the estima-
tion of an autoregressive process. Because of their great simplicity, they are used despite the
various criticisms assigned.
Consider a series Yt, t = 1, .., T. We test the stationarity conditionally to a specification
used by defining three basic models:



model 1 : ∆Yt = φYt−1 + εt
model 2 : ∆Yt = φYt−1 + c + εt
model 3 : ∆Yt = φYt−1 + c + βt + εt
The strategy is to test from the most general model (model 3), the null hypothesis of unit
root φ = 0 (Xt is integrated order 1, that is to say, not stationary) against the alternative of no
unit root φ < 0 (Xt is stationary) and removing, if necessary, the non-significant additional
elements. The decision rule is that if the calculated value of t-statistics associated with φ is
less than the critical value of the Dickey-Fuller table, then we reject the null hypothesis of
2
For more description, see Kishore Kulkarni and Andrew Clarke (2009): Testing the J-curve Hypothesis: Case
studies from Around the World 2009;International Economics Practicum
10

non-stationarity; otherwise, it is accepted. But long before that, we must verify the signifi-
cance of the trend. If it is not significant, we move to the significance of the constant. If it
is not, then it goes to the estimation of the model without trend nor constant (model1). But
if the trend is significant, we just have to estimation the model with trend and without the
constant(model 2).
3.2.2 Cointegration
In this section, we are going to analyze the relationship between our different series. To
see if there is a long-term relationship between our variables, we need to test the cointegra-
tion between them. The theory of cointegration means that if there exists a stationary linear
combination of non stationary random variables, the variables combined are said to be coin-
tegrated. This notion of cointegration concerns most of the time spurious regressions. It
allows us to make a long-term analysis of the different variables while having a short-term
dynamics. The theory of cointegration was introduced by Granger (1981) and subsequently
developed by many authors including Engle and Granger (1987)3.
At a general level, the regression equation is:
Y1t = α + βY2t + εt (8)
where Y1t is the dependent variable, Y2t is the independent variable and εt is the white
noise. We then assume that Y1t and Y2t are integrated in one order and can write the Error
Correction model. We derive within this general equation, the expression of the residuals
that will be estimated later. The estimated residuals are: εt = Y1t −α−βY2t. This is said to be
stationary. Based on this regression, we want to test the null hypothesis of no cointegration
against the alternative hypothesis of cointegration. The decision rule is: if the value of the
t-statistic is less than the critical value, we reject the null hypothesis, then the series are
cointegrated. Otherwise, they are not cointegrated. To interpret our result, we are going
to use the table of Engle and Yoo (1987) or McKinnon (1991). Because the residuals are
stationary, we can write the Error Correction model as follow:
∆Y1t = γ∆Y2t + δ(Y1t−1 − Y2t−1 − α) + µt (9)
with δ < 0. δ , the error correction term should be negative and its absolute value need not
to be always less than unity, implying that, at times, overshooting is possible. This model
allows both to identify the dynamics of the short and long term.
This method of Engle and Granger (1987) has a limit which is that of not being able to
take into account the dynamics of the relationship between several variables. It only allows
one to obtain long-term relationship. To overcome this problem, we will estimate a model
written by Johansen (1988) 4.
• Cointegration between many variables: Johansen approach
3
They have done the work to establish the link between cointegration and error correction models. In order
to have more details on the concept of co-integration and error correction model, it could refer to the paper of
Robert F. Engle, C. W. J. Granger, Co-Integration and Error Correction : Representation, Estimation , and Testing
; Econometrica, Volume 55, Issue 2 (Mar.,1987), 251-276
4
this is a multivariate approach based on the method of maximum likelihood method
11

The model is estimated on the basis of a model VAR (Vector Autoregressive). This is called a
Vector Error Correction model (VECM). Estimating a VAR (p) is equivalent to an estimation
of a VECM (p-1).
Let us consider a vector of N variables Yt which are all integrated in one order. The
VECM (p-1) equation of Yt is:
∆Yt = b1∆Yt−1 + ... + bp−1∆Yt−p+1 + ΠYt−1 + εt (10)
where Π is a matrix that contains all the speeds of adjustment for each cointegrating vectors
and cointegration relationships. What matters is the rank (r) of this matrix because it is the
basis of whether or not there is a cointegration relationship between the variables. In the
Johansen test, we are interested in the null hypothesis which is that there exist r cointegra-
tion relationships between the N variables. In other words, under the null hypothesis, Yt is
cointegrated of the rank r. To determine the number of cointegration vectors r, Johansen has
used a method of maximum likelihood and proposed a test based on testing of the Trace. The
objective is to test the null hypothesis of the existing of at most r cointegration relationship
(there exist r eigenvalues different from zero). Its t-statistic is:
TR = −T
N
i=r+1
log(1 − λi) (11)
Where N is the number of variables in the vector Yt, T is the number of observations. We
will compare this to its critical value obtained from the Johansen table and rejected the null
hypothesis if the value of the t-statistic is higher than it critical value. We can observe three
cases. The ﬁrst case is that if Rg(Π)= 0 =⇒ r= 0: there is no cointegration relationship, then Yt
is integrated of order 1 but non-cointegrated. An estimation with a VAR model is necessary.
The second case is that if Rg(Π)= r with 0 < r < N, then Yt is cointegrated with r and
there exist r cointegration relationships. An estimation with an Error Correction model is
necessary. Finally, if Rg(Π)= N =⇒ r= N, then Yt is stationary and there is no cointegration
relationship; a VAR model is necessary.
Testing the maximum eigenvalues, we denote the t-statistic which gives the expression
as follow:
MEmax = −T log(1 − λq+1)
The rule of decision based on this test is to test the null hypothesis r = q against the alterna-
tive hypothesis r = q + 1.
In several works, the test of the Trace is the most used.
3.2.3 A VAR model
We will estimate with a VAR model, if the estimation of the Error Correction model provides
us with results that show that there is no co-integration relationships between our different
series (Y1t and Y2t). As we have no long-term relationship, it is important to analyze short
term relationship. It is estimated on stationary variables that is to say, in ﬁrst difference. As
we said previously, the method for making this estimation is Johansen’s one. It allows to
take into account the issues of exogeneity and causality. To better illustrate this analysis, we
consider a vector Yt containing N variables all integrated in order 1. The representation of a
VAR(p) of Yt is given by:
Yt = δ + Φ1Yt−1 + Φ2Yt−2 + ... + ΦpYt−p + εt (12)
12

where εt is the white noise and Φi(i = 1, ..., p), the matrix of parameters.
• Granger causality test
We will do the Granger causality test before estimate a VAR model because we have many
coefficient to estimate. Consider a VAR(p) model with Y1t and Y2t stationary:
Y1t = δ1 + α11Y1t−1 + α12Y1t−2 + · · · + α1pY1t−p + β11Y2t−1 + β12Y2t−2 + · · · + β1pY2t−p + ε1t
Y2t = δ2 + α21Y1t−1 + α22Y1t−2 + · · · + α2pY1t−p + β21Y2t−1 + β22Y2t−2 + · · · + β2pY2t−p + ε2t
(13)
This is to set two assumptions: the first one is to say that Y1t does not cause Y2t (all α = 0)
and Y2t does not cause Y1t (all β = 0) .
Overall, what is important in a VAR model is to choose the optimal number of lags, make
sure the VAR model is stable and be able to represent the response functions of the different
shocks to each variable. These response functions 5 represent the effect of an impact of an
innovation on current and future values of the endogenous variables. With them, we will
check if there is or not a J-curve effect in the short run.
4 Data description and Sources
4.1 Data
In this section, we will focus on the one hand on our different variables and data analysis.
Based on time series data, we will realize a descriptive and detailed analysis that will allow
us to provide economic interpretations and draw conclusions. On the other hand, it is neces-
sary to list the various difficulties encountered in collecting these data. Finally, we will give
the different description of our variables.
We focus primarily on three countries in the Euro Area namely France, Germany and
the Netherlands. For these three countries, with reference to the existing literature, the vari-
ables that will be used to test the effect of a devaluation on the current account are mainly
the bilateral real exchange rate, the national income and the foreign income of the USA; all
variables are in logarithm form. These series are quarterly data, adjusted seasonally from
1999Q1-2014Q4. We use quarterly data because the trade balance data are only available in
quarterly frequency. This help us to increase the number of observations in order to have
a long run effect. We are also interested in this period of sample because the Euro was cre-
ated in 1999 and compared to most of the above mentioned papers, our sample includes
years in recession after the crisis of 2008. On the data sources, we will primarily use the data
provided by the OECD (Organization for Economic Cooperation and Development) and by
Eurostat-European Commission. For the data on the current account of a country, we have
constructed it in taking into account the data on exports and imports volumes. The cur-
rent account can be defined as the ratio between exports and imports in millions of dollars.
Data on imports and exports are retrieved from the OECD’s website according to the dataset
Balance of payment (MEI) and are measured in US Dollar index converted.
The exchange rate is the price of one currency in terms of another currency. It can either
be fixed or be flexible depending on the exchange rate regime in which a country is. The
Euro Area countries are in a fixed exchange rate regime.There are thus two types of exchange
5
It is the basis of a VAR process. Also the analysis of variance decomposition is very important, we will make
an illustration in our estimation
13

rate, the nominal and the real. In our study, we will look at the real exchange rate, defined
as the nominal exchange rate between two currencies deflated by price. It measures the
competitiveness of a country and involves the nominal exchange rate adjusted by inflation
differentials.
The real exchange rate expression is given by:
RER =
EP∗
P
(14)
Taking the logarithm, it can be expressed as:
rert = et + p∗
t − pt (15)
As long run determinants of the exchange rate, we can first mention the Law of One Price
and Purchasing Power Parity: the same good should sell for the same amount in different
countries. The exchange rate compensates for differences in price level among countries.
Secondly, we have the Balance of Payments approach: the exchange rate changes eliminates
international trade imbalances. And finally, we can mention the Asset-market approach
models (Monetary and Portfolio Approaches): the exchange rate adjusts to equilibrate inter-
national trade in financial assets.
Data on the bilateral real exchange rate were not directly accessible so we also had to con-
struct this variable from the data collected on the nominal exchange rate and relative prices
harmonized between the eighteen countries in the Euro Area and the United States. The
database on the Harmonized nominal exchange rate is the Monthly Monetary and Financial
Statistics (MEI) expressed in national currency per US dollar for these countries. Regarding
the national and foreign income for these countries, the data set is therefore obtained from
the Quarterly National Account on the OECD-stats. They are in millions of US dollars and
estimated in volume.
In order to have good results and to be consistent in our analysis, all our variables must
be in the same unit. We then considered as primary unit of analysis, the US dollar. We notice
that the consumer prices index have the same trend: continue to grow since the 90s. To over-
come the problems of the crisis, the states of the country have implemented an expansionary
policy, lower interest rates to ease credit. This ended up creating inflation which peaked on
the Fig.1. On average, France is running a deficit. Significant part of the trade ratio is below
zero except for 1999 where we have detected a surplus. Since the creation of the Euro in
1999, it depreciated until 2000 and then we see fluctuations in the evolution of this series
in the following years. The periods of depreciation are still more frequent than those of an
appreciation of the Euro vis--vis the Dollar. Unlike France, the Fig.3 shows that in Germany
the ratio of the current account remained high reflecting the fact that this country is in the
trade surplus. It is one of the world’s leading country exporters. The Fig.4 shows that the
Netherlands as Germany is characterized by a trade surplus since the creation of the Euro
and despite the existence of significant fluctuations in the recent years. Before the crisis of
2008, income in all countries was at a relatively low level. In 2008, it is growing by up to a
peak on the Fig.5 and then decreases abruptly at the beginning of 2009, the small population
is seen to be the most affected. This is the same phenomenon observed in the United States.
This is explained by the fact that inequality between people in a country have widened, the
rich get richer and the poor poorer. They are increasing since 2009.
14

Figure 1: Consumer price index
Figure 2: Real bilateral exchange rate and trade ratio in levels: France
4.2 Results on the unit root test
In this part we will give the results for the unit root test. All the variables are in first dif-
ference. Table 1 summarize the most of our results in these terms. We see the significance
of the trend and the intercept. Regarding the results in Table 1, the foreign income (Y ∗) has
an intercept which is significant as 3.193 > 2.54. The variable is stationary around a con-
stant (−5.3713 < −2.91) with the statistical threshold of 5%. For the harmonized consumer
price index in the Euro Area, the estimated t-statistics of the intercept is equal to 3.0649. It
exceeds the critical value of the Dickey-Fuller table (2.54) provided by estimating a model
without trend with a constant for 5% threshold. We reject the null hypothesis that the inter-
cept is not significantly different from 0. So the value of the ADF t-test statistic is equal to
-3.3697; it is less than the critical value (-2.91) at the 5% level. We conclude that this series
15

Figure 3: Real bilateral exchange and trade ratio in levels: Germany
Figure 4: Real bilateral exchange and trade ratio in levels: the Netherlands
is stationary around a constant. Like the series P; P∗ is stationary around a constant too.
This is explained by the fact that the constant is significant (6.4679 > 2.54) and the value of
the ADF t-statistic is −8.8323 < −2.91 at the 5% level. The real bilateral exchange rate (rer)
is stationary because the estimated value of the ADF statistic (Augmented Dickey-Fuller) is
equal to -5.7486. This value is lower than the critical value of -1.946 at 5% level. We therefore
reject the null hypothesis of unit root. The DLTBFR series is stationary because the results
show that the t-statistic value of ADF (-8.0340) is lower than critical (-1.94) at the 5% level.
The series is stationary in first differences as −3.4691 < −1.94.
For the German trade balance, the test carried out on the model with constant and no
trend in levels (with null hypothesis LTBall has a unit root) tells us that the constant is
significant (2.5547 > 2.54) at the 5% but the unit root test ADF will provide the following
results: the t-statistic is equal to -2.5725; it is greater than the critical value which is equal to
-2.91. We conclude then that is not stationary in levels. Then runs to make first difference sta-
tionary series. The results show that neither trend nor the constant are statistically significant
at the level of 5%. The DLTBALL series is stationary because the statistical value of the ADF
(-9.1781) is lower than critical (-1.94) at the 5% level. To level the series LYALL admits a sig-
nificant trend at the 5% threshold (2.9525 > 2.79), but is not stationary (−3.3056 < −3.4852).
16

Figure 5: Real income
Variables Deterministic Test statistic Critical values
term at 5%
p c -3.369 -2.91
p∗ c -8.832 -2.91
rer - -5.748 -1.94
TBf - -8.034 -1.94
TBg - -9.178 -1.94
TBn - -10 -1.94
Yf - -3.469 -1.94
Yg - -4.564 -1.94
Yn - -3.612 -1.94
Y ∗ c -5.371 -2.91
c = intercept; ’-’= no intercept
Table 1: Augmented Dickey-Fuller unit root test: probability
We then take the first difference to make it stationary. We find that the series DLYALL is
stationary (−4.5644 < −1.94).
Like the lTBALL series lTBHOL admits a significant constant (2.65 > 2.54) according to
the test of the model with constant without deterministic trend in level but is non-stationary
(−2.63 > −2.91). First difference, the series becomes stationary (−10.0040 < −1.94). It is
17

stationary in difference because −3.6127 < −1.94 depending on unit root test ADF. The esti-
mated value of the ADF statistic (Augmented Dickey-Fuller) is equal to -5.7486. This value
is lower than the critical value at the 5% (-1.946) . We therefore reject the null hypothesis of
unit root: the series is stationary DRER that is to say integrated of order 0.
This allows us to conclude that all our variables are differentiable only once in order to
make them stationary.
5 Empirical model and results
The Balance of payment approach is considered as a flow model because it assumes that
the exchange rate is determined largely by a country’s current account performance means
by trade flows. The Balance of Payment is a cash balance of the country relative to the
rest of the world. It is equal to the Current Account. Previously, we have considered the
current account as the difference between exports and imports in volumes. In this section,
we will consider it as a ratio between these two components. All variables are expressed in
logarithms. The trade balance (TBt) is a function of the real bilateral exchange rate(RERt)
and a measure of domestic (Yt) and foreign Y ∗
t income respectively.
TBt =
Xt
Mt
(16)
TBt = α + βYt + γY ∗
t + δRERt + εt (17)
The purpose of this study is to investigate on the long-run relationship existing between
the trade balance and the real bilateral exchange rate. We will estimate this model for three
countries: France, Germany and the Netherlands.
With reference to 3, we expect a depreciation of domestic currency, following an increase
in domestic prices, a decrease in foreign prices, or a decrease in foreign income. We expect
the coefficient on real domestic income (β < 0) to be negative, the coefficient on real foreign
income (γ > 0) to be positive and the coefficient on the real exchange rate (δ > 0) to have a
positive sign.
On this model, we will recover the residuals and estimate them. Based on the regression
for the residuals, we will test the null hypothesis of no cointegration. The Engle and Granger
(1987) method will help us doing that and we will use the table of Engle and Yoo (1987)
for the interpretation of the results. Because the residuals are stationary, we will estimate a
Vector Error Correction model (VECM) based on the Johansen’s approach. Consider a matrix
or a vector Yt which contains all our variables in levels. The VECM (1) equation is as follow:
∆Yt = b1∆Yt−1 + ΠYt−1 + εt (18)
where Π is the matrix that contains all the speed of adjustment for each cointegration re-
lationship. If the results show that the series are cointegrated then we can analyze a long
term relationship, but if not we will estimate a VAR model. We consider, a vector Yt which
contains all our variables in first difference.
Yt = δ + Φ1Yt−1 + εt (19)
where εt is the white noise and Φi(i = 1, ..., p) are matrice of parameters, p the delay is equal
to one. This will allow us to see the short term impact and see if a J-curve phenomenon
occurs.
18

5.1 France
The Engle and Granger (1987) test based on the cointegration show us that the series are
not cointegrated because the value of the t-statistic is higher than the critical value of the
Engle and Yoo (1987) table, −2.7895 > −4.35. So to test the robustness of our model, we
will estimate a VECM. This will help us to see the J-curve phenomenon. Before starting
any estimation of the Error Correction model, we will determine the lags number of our
model VAR (3), which is the basis for the construction of the VECM (2) model using the
information criteria of Akaike. Regarding the VAR lag order selection criteria, we can retain
two (2) lags. After choosing the number of lags according to the results provided by the
information criteria, we will check whether our residuals are good or not. This is to make an
autocorrelation LM test with the null hypothesis as no serial autocorrelation at lag h order.
This test allows us to say that our residuals are not autocorrelated because the p-values are
all above 0.05%: we do not reject the null hypothesis. We can summarize the results in the
table2 below.
Lags 1 2 3 4 5 6 7 8
LM - Stat 19.017 20.069 16.301 16.059 12.479 18.353 9.632 18.19
Prob 0.267 0.217 0.432 0.448 0.710 0.303 0.885 0.312
Probs from chi-square with 16 df.
Table 2: LM tests of Breusch-Godfrey: France
We can thereby implement the Johansen test that allows to deduce the number of cointe-
gration relationships between our variables. To estimate the cointegration relationship, we
will rely on two types of tests namely, that of the Trace and that of the maximum eigen-
value. These tests allow us to determine the number of cointegration relationships between
the different series. This is necessary because it allows us to identify the shape of the Error
correction model to use. To perform the test of the Trace, the specification that we will retain
depends on: the absence or presence of constancy in the error correction model; the absence
or presence of constant and trend in the cointegrating relationships. We perform the test of
the Trace assuming no trend in the cointegration relationship and the presence of a constant
in the error correction model. We take into account consistency in our regression because
our series (Trade balance and the real exchange rate) in logarithms are all characterized by a
linear trend downward. It shows us that there is a cointegration relationship. We can then
estimate a Vector Error Correction model. Here, we normalized the values of the coefficient
of LTBFR, means that LTBFR is our endogenous variable and D(RER), LYFR, LY ∗ are our
exogenous variables. In other words, the equation to estimate is:
LTBFRt = 36.467 − 0.3755RERt − 3.646LYFRt + 0.986LY ∗
t + εFRt
where εFRt is the residual term.
From this equation, we have the long term coefficients that are supplied in the table 3.
Our VECM has four equations because we have four basic variables. The results of estimat-
ing the VECM are deferred specified in the table 4. The estimated Vector Error Correction
model provides the results in Tables 3 and 4. In the short run, the impact of the exchange
rate on the current account is positive. The coefficient is not significant at the 5% implying
that a depreciation of the bilateral real exchange rate has no short-term effect on the current
19

Long run
Variables elasticities
(LTBt−1) (RERt−1) (LYt−1) (LY ∗
t−1)
D(LTBFr) 1 -0.375 -3.646 0.986
(3.724) (3.006) (-1.112)
D(RER) 1 -0.375 -3.646 0.986
(3.724) (3.006) (-1.112)
D(LYFr) 1 -0.375 -3.646 0.986
(3.724) (3.006) (-1.112)
D(LYUSA) 1 -0.375 -3.646 0.986
(3.724) (3.006) (-1.112)
lag=1; t-student in (.)
Table 3: Long run estimation: France
account. National income has a short run negative impact on the current account in the
sense that when increases the purchasing power of the French population, people prefer to
import rather than exporting. This will worsen the current account level. Foreign income
is not significant at the 5% level . None of our short-term variables explain the level of for-
eign income. In the long term, all the coefficients are significant except the foreign income
at the 5% level. The long-term impact of depreciation on the current account is negative, or
we expected it to be positive. This is not consistent with the expected results. An increase
in the purchasing power of the population has a negative impact on the current account as
well as the short term. The national income is significant and with the expected sign. And
the foreign income although it is not significant, allows us to obtain the expected sign that
is to say that its effect is positive on the current account of France. Let us consider H which
is a matrix that contains the coefficients of the speed of adjustment of DLTBFR, D(RER),
DLYFR and DLY ∗ in the VECM regression respectively.
H =




0.097
−0.477
−0.0251
−0.0254




The results show that the Error correction term is negative and significant in relative rela-
tionship to the bilateral real exchange rate. In the equation for the current account, this term
is positive, the economic interpretation is difficult. We can therefore conclude that there is
no J-curve phenomenon for France. The response functions shown below Fig.6, 7, 8, allow
us to confirm the results we obtained earlier about our different variables. An increase in
national income leads to an increase in the value of domestic imports; this is due to the rise
of purchases of domestic consumers. A devaluation improve the current account both in the
short and long run in France.
Our results are contrary to the one found by Magee (1973) for whom one might expect a
positive sign of national income on the balance of payments because imports are determined
by the difference between consumption and domestic production.
20

Figure 6: Impulse RER and response LTBFR
Figure 7: Impulse LYFR, response all
21

Short run
D(LTBt−1) D(LTBt−2) D(RERt−1) D(RERt−2) D(LYt−1) D(LYt−2) D(LY ∗
t−1) D(LY ∗
t−2)
D(LTBFr) -0.34 -0.17 0.03 0.025 -0.596 -1.29 0.154 -0.06
(-2.314) (-1.271) (0.516) (0.436) (-0.937) (-2.284) (0.349) (-0.129)
D(RER) 0.483 0.056 0.267 -0.20 -1.85 1.75 -0.275 1.457
(1.605) (0.20) (2.202) (-1.692) (-1.424) (1.516) (-0.304) (1.470)
D(LYFr) 0.020 0.046 -0.012 0.013 0.328 0.185 0.318 -0.11
(0.591) (1.424) (-0.861) (0.978) (2.18) (1.38) (3.03) (-0.96)
D(LYUSA) 0.072 0.045 -0.01 -0.006 0.37 0.077 0.116 0.095
(1.298) (0.879) (-0.476) (-0.286) (1.542) (0.363) (0.697) (0.523)
Lags=2; t-student in (.)
Table 4: Dynamics relationship: France
5.2 Germany
We have retained 5 lags for a VAR model for Germany. This implies that we have a VECM (4)
if there is a cointegration relationship between our series. As for France, we notice that there
is no cointegration relationship between our series based on the Engle and Granger(1987)
estimation because the value of the t-statistic is higher than the critical value of the Engle
and Yoo (1987) table, −2.95 > −4.22. We proceed as in the case of France. We have checked
if or not the residuals are autocorrelated. This is to make an LM test and we deduce that they
are not autocorrelated because the p-values are all above 0.05%. We do not reject the null
hypothesis. We can summarize the results in the table 5. We can now estimate the Johansen
Lags 1 2 3 4 5 6 7 8
LM - Stat 15.181 12.92 16.345 8.99 8.18 19.11 25.85 14.23
Prob 0.511 0.678 0.429 0.913 0.943 0.262 0.056 0.581
Probs from chi-square with 16 df.
Table 5: LM tests of Breusch-Godfrey: Germany
model which is based on the Trace test. This gives us the results below in table 6. Results
from the Trace test show that we reject the null hypothesis that there is no cointegration
relationship between our variables. Therefore, we then test the null hypothesis that there
is only one relationship against the alternative that there is more than one cointegration
relationship. The associated p-value is 0.016 and the value of the t-statistic (33.74) is higher
than the critical value (29.79) at the 5% level. It concludes that according to the Trace test,
we have two (2) cointegration relationships between our series. This result provided by the
Trace test is not robust. To make robust our model, we will perform the test of the maximum
eigenvalue. It presents the results which is summarized in the table 7. He recommends
us that there is only one cointegration relationship. In our study, we will instead rely on
the maximum eigenvalue test for reasons of robustness. For Germany, there exists only one
cointegration relationship between its trade balance, national income, the real exchange rate
and the foreign income.
22

Figure 8: Impulse LY ∗, response all: France
Hypothesized Eigenvalue Trace 0.05 Prob.∗∗
No. of CE(s) statistic Critical value
None∗ 0.610 86.50 47.85 0.0000
At most1∗ 0.285 33.74 29.79 0.0167
At most2 0.227 14.92 15.49 0.0608
At most3 0.008 0.457 3.84 0.4989
(∗
) rejection of the hypothesis at the 0.05 level; (∗∗
) p-values
Table 6: Trace test: Germany
Hypothesized Eigenvalue Max-Eigen 0.05 Prob.∗∗
No. of CE(s) statistic Critical value
None∗ 0.61 52.75 27.58 0.0000
At most1 0.285 18.82 21.13 0.10
At most2∗ 0.227 14.47 14.26 0.04
At most3 0.008 0.46 3.84 0.498
(∗
) rejection of the hypothesis at the 0.05 level; (∗∗
) p-values
Table 7: Maximum Eigenvalue: Germany
The VECM equation that we will be estimated is:
LTBALLt = 8.4106 − 0.0912RERt − 1.2163LYALLt + 0.597LY ∗
t + εALLt
where εALLt is the residual term. We have the same values for the long run coefﬁcients for the
estimation of this equation. In the short run, in order to have a J-curve phenomenon, we need
23

Long run
(LTBt−1) (RERt−1) (LYt−1) (LY ∗
t−1)
D(LTBAll)
D(RER) 1 -0.091 -1.216 0.597
(1.868) (6.968) (-3.799)
D(LYAll)
D(LY ∗
t )
lag=1; t-student in (.)
Table 8: Long run estimation: Germany
to have a negative sign of the coefficient of the real exchange rate on the current account. And
in our study, we have founded the sign expected. In the short run, a devaluation deteriorates
the current account in the first two quarters with a coefficient associated to the first quarter
which is significant. In the third quarter, the coefficient is positive but insignificant. While in
the last quarter, it becomes negative. There is then on this point of view, a shape resembling
that of the J-curve.
Short run
D(RERt−1) D(RERt−2) D(RERt−3) D(RERt−4)
D(LTBALL) -0.287 -0.035 0.095 -0.134
(-3.74) (-0.422) (1.123) (-1.58)
Lags=2; t-student in (.)
Table 9: Dynamic estimations: Germany
Let us consider G which is a matrix that contains the coefficients of the speed of adjust-
ment of DLTBALL, D(RER), DLYALL and DLY ∗ in the VECM regression respectively.
G =




−0.526
−0.856
−0.168
−0.129




The speeds of adjustment are all negative and significant, showing that even if there
are imbalances in the current account of Germany, these imbalance will always be met by
movements in the exchange rate. There will always be a return to the steady state. The speed
of adjustment of the real exchange is not only significant but close to the unit in absolute
value. This proves that our VECM is good and robust.
A depreciation does not improve the current account of the Germany in the very long run
as we expected, rather it worses as in the case of France. The signs of the variables are the
same as in France, therefore it can be concluded that there is no J-curve effect in Germany.
But a depreciation induces an increase in the trade balance in the third and five quarters. We
have two phenomenon of J-curve each year. The J-curve phenomenon occurs for Germany.
24

Figure 9: Impulse RER, response all: Germany
The Fig.9 and 10 are the representations of our VECM estimation and allow us to con-
firm the results we have obtained above. The impact of the national income on the current
account in Germany is negative and significant. The impact of the foreign income is positive
but became negative in the long run.
We find the same results as in Jörg Clostermann (1996) article insofar as it has found a
curve which resembles that the curve J.
5.3 The Netherlands
As for France and Germany, there is no cointegration relationship based on the Engle and
Granger (1987) test: −3.80 > −4.35. For more robustness of our model, we do a Johansen
test. The Var model allows one hand to analyze the effects of one variable on another through
shocks, and also to make an analysis of causality in the Granger sense. In this model, each
variable is endogenous and is function not only of lags the other variables but also of its
own lags. We will first perform a VAR model in level to check whether or not there is a
cointegration relationship between our variables. If this relationship exists, then we will
estimate a VECM model, but if not, then it will be a VAR in first differences ie on stationary
variables. Determining the number of lags for our VAR model will be based on the Akaike
or Schwartz information criteria. We note that the Akaike criterion leads us to choose a VAR
(2) in level. This retention of a delay of 2 for the VAR level leads us to estimate a VECM (1) on
the basis of which we will carry out the test of no cointegration Johansen. This test reveals
that there is no cointegration relationship between the current account, the real bilateral
exchange rate, the level of domestic and foreign income in the Netherlands.
With that, we can then move on to the second stage which is that of estimating a VAR
in difference. This will help us to see if there is a J-curve phenomenon in the short run
25

Figure 10: Impulse in the national income: Germany
for the Netherlands. For the VAR model, information criteria allowed us to retain a single
delay: this means that we will estimate a VAR(1) model. We will then do an LM test for
autocorrelation of residuals. The results of this test help us to conclude that our residuals are
not autocorrelated.
The VAR(1) estimation is postponed in the table 10. The results show that the growth
Variables D(LTBt−1) D(RERt−1) D(LYt−1) D(LY ∗
t−1)
D(LTB) -0.323 -0.06 -0.39 0.165
(-2.47) (-1.44) (-1.49) (0.59)
D(RER) 0.029 0.31 0.52 0.08
(0.07) (2.35) (0.64) (0.09)
D(LY ) 0.006 -0.016 0.45 0.39
(0.11) (-0.94) (4.17) (3.39)
D(LY ∗) 0.05 -0.001 -0.007 0.38
(0.83) (-0.08) (-0.05) (2.72)
t-statistics in (.)
Table 10: Estimated VAR for the Netherlands
rate of the current account depends negatively on the real exchange growth rate and the
growth rate of national income. But positively on the foreign income growth rate. We now
consider the impact of shocks on our different variables. The response function is the effect
of an impact of an innovation on current and future values of the endogenous variables; this
is one of the biggest uses of VAR models. It focuses on the effects of the shock over a period
equal to 8.
26

Figure 11: Impulse DRER, response all: the Netherlands
A shock of DRER has no immediate impact on the current account in the Netherlands.
The effect of a depreciation of the exchange rate has no significant impact on national and
foreign income, or on the current account. It has a significant impact on itself during one
semester (two quarters), subsequently, the impact becomes zero. Note that even if we have
a curve that has a shape of the J-curve in the short run, it remains insignificant. Following a
shock in national income or current account or the exchange rate have a significant response
to this shock. DLYHOL responds positively and significantly to its own shock in the third
quarter. But the consequences of this shock disappear after three quarters. Foreign income
responds positively in the first quarter to a shock in the national income. The effects wear
off after 2 years. This is because the Netherlands is a small open economy. A shock in the
level of the current account in the first quarter is significant on itself. The other variables
are responding but are not significant. DLY ∗ shock has a positive and significant impact
on DLYHol until 1 year. This can be explained by the fact that as the Netherlands is a small
country, it suffered from the influence of the foreign country (the United States) that it is a
great country. So any change in US income will impact the level Netherlands income.
The analysis of the different response functions can be completed by an analysis of the
decomposition of the variance of prediction errors. The objective of the variance decom-
position of forecast errors is to calculate the contribution of each of the innovations to the
variance of errors. This leads to write the variance of the forecast error at a horizon h from
1 to 8 depending on the error variance attributed to each variable. Then, to obtain the rela-
tive weight percent, the ratio is calculated between each of these variances and the overall
variance.
27

Figure 12: Impulse DLYHOL, response all
Figure 13: Impulse DLTBHOL, response all
28

Figure 14: Impulse DLY ∗, response all: the Netherlands
The variance of the forecast error of DLTBHOL is due for 94.5% to its own innovations,
for 2.56% to DRER, for (1.91%) to DLYHOL, and for 1.03% to DLY ∗. So we can say that
DLTBHOL is only explained by itself. The variance of the forecast error of DRER is due to
its own innovations to 95.6% , for 0.98% to DLYHOL, for 0.29% to those of DLY ∗. So we can
say that DRER as DLYHOL is only explained by itself. As for the DLYHOL variable, it is
explained by 20.51% by DLY ∗. These results are consistent with the explanations provided
by the fact that Holland is a small country that is inﬂuenced by a great country.
After that, we will conduct a study of causality in the Granger sense based on the test
of non causality. The variable Y1t causes under Granger Y2t if the quality of the prediction
based on knowledge of the common past of Y1 and Y2 is better than that based solely on the
past of Y2. We reject the null hypothesis of no causality if the p-value is below the threshold
of 0.05. The law is that of the chi-square.















DLTBHOL
DRER
DLYHOL
DLY ∗






























DLTBHOL
•
2.07
⊗
2.22
⊗
0.35
⊗






























DRER
0.005
⊗
•
0.41
⊗
0.008
⊗






























DLYHOL
0.012
⊗
0.88
⊗
•
11.47
=⇒






























DLY ∗
0.68
⊗
0.006
⊗
0.003
⊗
•















Where ⊗ means the variable Y 1 do not Granger cause the variable Y 2; =⇒: the variable Y 1
29

do Granger cause the variable Y 2 and •: there is no causality between a variable and itself.
First, we test the null hypothesis that DRER, DLYHOL and DLYUSA do not Granger
cause DLTBHOL. The associated probabilities are respectively 0.1506, 0.1360 and 0.5553:
they are all above the statistical threshold of 0.05, in this case we accept the null hypothesis:
the growth rate of the national and foreign income, and the bilateral real exchange rate do
not Granger cause the growth rate of the current account. Based on this logic, we see that
neither the growth rate of the real exchange rate does not Granger cause other variables. It is
the same remarks for the national income growth rate. Contrary to these previous variables,
the results show that DLYUSA Granger cause DLYHOL: it can be explained by the reasons
provided above.
It is only the growth rate of the foreign income that Granger cause the growth rate of the
Netherlands income and none of the other variables cause the others at the statistical thresh-
old of 5%. The results here are consistent with those obtained by the response functions.
6 Summary and conclusion
This paper aims to let us know if there are cointegration relationships and to deduce the
existence of a J-curve phenomenon. In order to achieve this, we use different methods of an-
alyzing the cointegration and use the VECM model for empirical estimation. As an overall
conclusion, the effect of a depreciation on the current account has an ambiguous effect on
whether the country has a surplus or a deficit. The probability of obtaining a J-curve phe-
nomenon for a country that has a trade surplus is higher than the probability of a country
that has a trade deficit. This can be illustrated by the examples of countries in the Euro area
we have targeted: France has increased deficit; Germany, surplus and large exporting coun-
tries in the world and the Netherlands, surplus country and classified in terms of exports
after Germany.
In France, the short run variables effects are not significant except that of the bilateral
real exchange rate. In the long run, all the coefficients are significant except for the foreign
income. It was expected that a devaluation should improve the current account in the long
run but we have obtained contrary results from the estimated VECM. A 1% increases in
the national income leads to an increase in the value of domestic imports; this is due to
the rise of domestic consumers purchases. Our results are contrary to the one found by
Magee (1973) for whom one might expect a positive sign of national income on the balance
of payments because imports are determined by the difference between consumption and
domestic production. There is no J-curve when there is a devaluation in France.
For Germany, we have estimated a VECM (4) and our residuals are not autocorrelated:
results provided by the LM test. Because the Trace test offers two(2) cointegrating relation-
ships, we perform rather a test of the maximum eigenvalue for reasons of robustness of our
estimation. As short run effect, a devaluation of the exchange rate has a significant and neg-
ative impact in the first quarter, negative but insignificant in the second quarter and 1 year.
Thus, we see that the current account deteriorates. A depreciation does not improve the
current account of the Germany in the very long run as we expected, rather it worses. We
have two phenomenon of J-curve each year. The J curve phenomenon occurs for Germany:
the prices of imported goods rise, which leads to an increase of imports in short run. There
will be a loss of competitiveness of Germany therefore the quantity effect, means that the
imports of goods in volume will be adjusted downward while they produced more locally
30

to meet demand. This implies an improvement in the long run. We obtain a negative and
significant effect of the national income on the current account.
From a Var(2) chosen by the information criteria, we have estimated a VECM(1) which
provides findings that our series are not cointegrated under Johansen for the Netherlands.
Then we moved to the estimation of a Var model in first difference. For this estimation, the
information criteria allow us to choose only one delay. The effect of a devaluation of the
exchange rate has no significant impact on national and foreign income or on the current
account. Note that even if we have a curve that has a shape of the J-curve in the short run, it
remains insignificant.
As main limitation of our model, it does not take into account the balance of payments in
all its integrity. This means that we have not considered international capital movements in
our study despite the fact that we know that international capital flows are very important
and dominate the international market. This limit, then we can expand our study taking into
account the role of the capital account in the balance of payments: asset models of exchange
rate determination. This may also be a new search. To better understand the net effect
of a real depreciation on the economy as a whole, is to try to study the response of other
variables on a real depreciation. Instead of considering the consumer price index, we can
take into account labor costs.
In conclusion, the countries that would like to implement exchange rate targeting pol-
icy must take their precaution because each reacted differently when their is a devaluation:
it can be different not only in terms of the economic environment but also the weight of
countries in international trade.
31

7 References
Alban Pllaha (2013). ”The J-curve effect in bilateral trade: the impact of currency deprecia-
tion on trade balances between Albania and its main trading partners”, Working paper.
Anne O.Krueger (1983). ”Exchange Rate determination”, Cambridge Surveys of Economic
Literature.
Arghyrou, Michael G., Chortareas, Geogios (2006). ”Current account imbalances and real
exchange rates in the Euro area”, Cardiff Economics Working Papers, No. E2006/23.
Burnstein, Ariel, Martin Eichenbaum, and Sergio Rebelo (2004). ”Large Develuations
and the Real Exchange Rate”.Working Paper No. 513.
Eric Ben Kamoto (2006). ”The J-curve effect on the trade balance in Malawi and South
Africa”, The University of Texas at Arlington,April 26, 2006; Working paper.
IRANDOUST, M., EKBLAD, K. and Parmler, J. (2006). ”Bilateral trade flows and ex-
change rate sensitivity: Evidence from likelihood-based panel cointegration”. Economic Sys-
tems, 30, 170-183.
Jacob Jorlén (2001). ”Is there a J-curve in the bilateral trade between Sweden and Germany?-
A product level study” Master programme in Economic Growth, Innovation and Spatial Dynam-
ics.
Jörg Clostermann (1996). ”The impact of the exchange rate on Germanys balance of
trade”, Discussion paper 7/96, Economic Research Group of the Deutsche Bundesbank.
Mariarosaria Comunale and Jeroen Hessel (2014). ”Current account imbalances in the
Euro area: Competitiveness or financial cycle?”, NB Working Paper, No. 443.
Maryline Huchet-Bourdon, Jane Korinek (2010). ”To what extent do exchange rates and
their volatility affect trade?”, OECD Trade Policy Papers, No. 119, OECD Publishing.
Mohsen Bahmani-Oskooee (1985). ”Devaluation and the J-Curve: Some Evidence from
LDCs”, The Review of Economics and Statistics, Vol. 67, No. 3, pp.500-504.
Mohsen Bahmani-Oskooee and Hanafiah Harvey (2009). ”The J-curve: Indonesia vs. Her
Major Trading Partners”, Journal of Economic Integration; 24(4), 765-777.
Mohsen Bahmani-Oskooee and Janardhanan Alse (1994). ”Short-run versus long-run ef-
fects of devaluation: error-correction modeling and cointegration”, Eastern Economic Journal,
Vol. 20, No. 4.
Pavle Petrovic and Mirjana Gligoric (2009). ”Exchange rate and trade balance: J-curve
effect”, panaeconomicus, pp. 23-41.
Pentti J. K. Kouri (1976). ”The Exchange Rate and the Balance of Payments in the Short
Run and in the Long Run: A monetary Approach”, The Scandinavian Journal of Economics, Vol.
78, No. 2, pp. 280-304.
R. COTT HACKER and ABDULNASSER HATEMI-J (2003). ”Is the J-Curve Effect Ob-
servable for Small North European Economies?”, Open economies review, 2003-Springer.
Ronald MacDonald (1997). ”What determines real exchange rates? The long and short
of it”, International Monetary Fund.
Rose and Yellen (1989). ”Is there a J-Curve?”, Journal of Monetary economics.
Stephen P. Magee (1973). ”Currency Contracts, Pass-through, and Devaluation”, Brook-
ings Papers on Economic Activity, 303-325.
8 Appendices
32

Null Hypothesis: RESIDALL has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.9559 0.0038
Test critical values 1 % level -2.6040
5 % level -1.9463
10 % level -1.6132
*MacKinnon (1996) one-sided p-values.
Table 11: Unit root test for Germany
Null Hypothesis: RESIDF R has a unit root
Exogenous: None
t-Statistic Prob.*
5 % level -1.9463
10 % level -1.6132
Table 12: Unit root test for France
Null Hypothesis: RESIDHOL has a unit root
Exogenous: None
t-Statistic Prob.*
5 % level -1.9463
10 % level -1.6132
Table 13: Unit root test for the Netherlands
Sample: 1999Q1 2014Q4
Included observations: 56
Series: LTBALL RER LYALL LYUSA
Selected (0.05 level*) Number of Cointegrating Relations by Model
Data trend: None None Linear Linear Quadratic
Test Type No Intercept Intercept Intercept Intercept Intercept
No Trend No Trend No Trend Trend Trend
Trace 3 2 2 2 4
Max-Eig 1 1 1 1 1
*Critical values based On Mackinnon-Haug-Michelis (1999).
Table 14: Johansen Test for the Germany
33

Sample: 1999Q4 2014Q1
Included observations: 58 after adjustments
Trend assumption: Linear deterministic term
Series: LTBF R RER LYF R LYUSA
Lag Interval (in ﬁrst differneces): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue statistic Critical value Prob.**
None* 0.37 53.02 47.85 0.015
At most1 0.27 26.09 29.79 0.12
At most2 0.118 7.60 15.49 0.5086
At most3 0.004 0.26 3.84 0.6038
Trace test indicates 1 cointegration eqn(s) at 0.05 level
*denotes rejection of the hypothesis at the 0.05 level
** MacKinnon-Haug-Michelis (1999) p-values
Table 15: Johansen Test for France
Var Lag Order Selection Criteria
Endogenous variables: DLTBHOL DRER DLYHOL DLYUSA
Exogenous variable: C
Lag LogL LR FPE AIC SC HQ
0 -332.21 NA 4.85 12.93 13.08** 12.99
1 -304.62 49.88* 3.115* 12.48* 13.23 12.77*
2 -298.52 10.08 4.61 12.86 14.21 13.38
3 -284.78 20.60 5.18 12.95 14.90 13.70
4 -274.92 13.27 6.94 13.18 15.74 14.16
5 -267.32 9.06 10.54 13.51 16.66 14.72
6 -256.08 11.67 14.72 13.69 17.44 15.13
7 -239.73 14.46 18.26 13.68 18.03 15.35
8 -218.75 15.33 21.21 13.49 18.44 15.38
*indicates lag order selected by the criterion
LR: sequential modiﬁed LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
Table 16: Lag length criteria for the Netherlands
34

VAR Granger Causality/Block exogeneity Wald tests
Dependent variable: DLTBHOL
Excluded Chi-sq df Prob.
DRER 2.066 1 0.1506
DLYHOL 2.222 1 0.1360
DLY ∗ 0.347 1 0.5553
All 4.1208 3 0.2487
Table 17: Granger Causality: DLTBHOL
Dependent variable: DRER
DLTBHOL 0.005 1 0.941
DLYHOL 0.415 1 0.5192
DLY ∗ 0.008 1 0.9273
All 0.569 3 0.9035
Table 18: Granger Causality: DRER
Dependent variable: DLYHOL
DLTBHOL 0.012 1 0.9095
DRER 0.889 1 0.3455
DLY ∗ 11.47 1 0.007
All 12.97 3 0.0047
Table 19: Granger Causality: DLYHOL
Dependent variable: DLY ∗
DLTBHOL 0.6858 1 0.4076
DRER 0.006 1 0.9334
DLYHOL 0.003 1 0.9552
All 0.765 3 0.8576
Table 20: Granger Causality: DLY ∗
35

Figure 15: Current Account and RER: in ﬁrst difference
Figure 16: Real income: in ﬁrst difference
Figure 17: Resids Germany
36

Figure 18: Resids France
Figure 19: Resids for the Netherlands
37

Paper

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (8)

Similar to Paper

Similar to Paper (20)

Paper