Sources of economic fluctuations in France: A structural VAR model


European Journal of Government and Economics 
Volume 1, Number 1 (June 2012) 

ISSN: 2254-7088 

66

Sources of economic fluctuations in France: A 
structural VAR model 

Nabil Ben-Arfa, University of Nice Sophia Antipolis, France* 

Abstract 

This paper studies the economic fluctuations of an open economy such as the 
French economy. A system of variables containing output, price level, trade 
balance, real exchange rate and oil prices is analyzed by applying the structural 
vector autoregressive (SVAR) methodology initiated by Sims (1980). This set of 
variables allows to evaluate the main sources of impulses of the French economy 
fluctuations. The results show that five structural shocks are identified using the 
long-run constraints implemented by Blanchard and Quah (1989). From the SVAR 
dynamic properties, impulse response functions and variance decomposition, the 
French economy is shown to be particularly vulnerable to supply and oil price 
shocks, where these two shocks respectively contribute to 40% and 35% of the 
economic disturbance. France is also hit by important external shocks which 
damage its trade balance position. Finally, it is found that shocks related to 
economic policy (demand shocks) have a quite limited impact on the economic 
activity. 

JEL Classification 

E32; F41; C22 

Keywords 

economic fluctuations; external shocks; internal shocks; oil price shock; SVAR 
model 

*
Address for correspondence: Nabil Ben Arfa, CEMAFI, University of Nice Sophia-Antipolis, Résidence

les Imperators, immeuble le Constantin A, Chemin de la Lauve, 83700 Saint Raphaël, France. E-mail : 
nabil_ar@yahoo.fr. 

DOI: https://doi.org/10.17979/ejge.2012.1.1.4277 



European Journal of Government and Economics 1(1) 

67

Introduction 
The 1980s have operated a methodological and a theoretical revival on the 
economic fluctuations analysis. The aim of this paper is to deal with empirical 
treatment of economic disturbances. Sims (1980) was the pioneer of the 
fluctuations analysis within the vectorial autoregressive model, where impulses are 
apprehended as innovations in a statistical term. These VAR models were 
introduced as an alternative to the traditional econometric models. Sims proposed 
a new form of modeling based on no a priori and where no distinction is made 
between exogenous and endogenous variables. 

Since pioneer work of Sims (1980), the main empirical work dealing with the 
sources of economic fluctuations lay on autoregressive vectorial model. These 
canonical VAR models however posed some problems related to the shocks 
identification, they faced a lot of criticisms, qualifying them as “atheoretical” 
models. These criticisms lead to the birth of structural VAR models, models in 
which shocks identification is conducted by the imposition of constraints drawn 
from economic theory. It is this methodology of structural VAR which will be applied 
to the French economy. We will apply a structural VAR model to the French 
economy in order to identify the main shocks which are the origin of the economic 
activity fluctuations. 

In the second section we will reconsider the theoretical and the methodological 
revival of fluctuations analysis. In the third and the fourth part of the paper we 
explore the data used and estimate the structural VAR model. Finally, we interpret 
the results. 

The methodological and theoretical revival of the 
fluctuation analysis: 
The 1980s has operated a methodological and a theoretical revival on the analysis 
of economic fluctuations. The methodological revival was initiated by Sims (1980); 
it was inscribed on the line of the impulse-propagation approach suggested by 
Frisch (1933) and Slutsky (1927). 

On a theoretical level, real business cycle theory constitutes a true theoretical 
revival on the fluctuations analysis:  it proposes to explain the main part of the 
economic fluctuations within the neo-classic growth model disturbed only by 
shocks affecting the total factor productivity. This marks the abandonment of the 
debate on the relative importance of monetary versus fiscal shocks. The debate on 
the relative importance of supply and demand shocks emerges. 

Real Business cycle theory 

At the beginning of the eighties, the relevance of the equilibrium monetary theory 
was rejected in a theoretical as in an empirical level. It is in this context that 
appears the real business cycles theory, or RBC1, with the pioneers’ models of 
Kydland and Prescott (1982) and Long and Plosser (1983) in closed economy.  

The real business cycle theory considers economic fluctuations as the optimal 
response of economic agents to shocks on the total factor productivity. The models 
of real business cycle thus conceive the evolution of economic aggregates as the 
decision result of a great number of agents seeking to maximize their utility and 
only constrained by technological resource. The real business cycle theory 
attributes an insignificant role, even no role, to the monetary policy 

1
 For Real Business Cycles. 



European Journal of Government and Economics 1(1) 

 

 
68

These basic models were followed by many extensions: extensions to open 
economies, with the international real business cycle of Backus, Kehoe and 
Kydland (1992, 1994, and 1995). Extensions to others shock in addition to the 
technological shock, by borrowing theoretical assumptions from the Keynesian 
theory.  

Criticisms addressed to the basic real business cycle models lead to the 
development of an abundant literature, with increasingly sophisticated models. 
Results of these developments were not always satisfactory especially concerning 
the reproduction of the stylized facts. 

The methodological contribution of the real business cycles theory is however 
admitted by a large part of economists. Parallel to this movement within the real 
business cycle theory, a new school of thought was emerging; it is the new 
Keynesian macroeconomics. The New Keynesian (NK) shares with the partisans of 
the real business cycle theory the fact that macroeconomic requires more 
microeconomic bases. However, NK economists believe that market imperfections 
are the key to understanding the real-world. The introduction of NK ideas into RBC 
models seems to make results definitely more satisfactory, in the sense that these 
models are accepted by economics profession and that their empirical results are 
more realistic. 

The introduction of the prices rigidity was sufficient to join again with the monetary 
policy, neutral and without effect in basic RBC models. Some economists saw in 
this “marriage” between RBC and Keynesian, the birth of “the New Neo-classical 
Synthesis” (Goodfriend and King 1997). 

Nowadays, macroeconomic models incorporate the principal theoretical elements 
of RBC models. They adopt their general structure; seek to identify the impulses 
response function of agent in a general equilibrium structure. On the other hand, 
the way in which the models define and identify the cycles is substantially different 
from the original contributions, various types of imperfections and rigidities are 
introduced. These imperfections proposed by New Keynesian are related to the 
imperfect nature of competition on goods market, the specificity of financial market 
exchange, etc. 

During last decades, RBC initials disappeared gradually and those of DSGE 
appear (dynamic stochastic general equilibrium). 

The methodological revival: VAR model 

Sims (1980) proposed a tool for fluctuations analysis based on impulses, defined 
as statistical innovations. Since Sims contribution of 1980, the mains empirical 
work on the economic fluctuations sources lay on autoregressive vectorial 
methodology. 

The purpose of Sims consists in evaluating the contribution of various innovations 
of a system to the dynamics of each variable. To distinguish the impulses response 
from the propagation mechanisms, he proposes the Choleski method of 
orthogonalization. Following criticisms and in particular those concerning the 
impossible interpretation of shocks economically through the Choleski 
decomposition, many authors suggest to base the orthogonalization of shocks on 
structural model of innovations, the structural VAR model.  

Shapiro and Watson (1988), Blanchard and Quah (1989) and Gali (1992), 
proposed to identify structural impulses, which are interpretable economically: 
supply shocks, demand, economic policy…Their methods of identification are 
based on restrictions drawn from the economic theory. From an econometric point 
of view the structural impulses are estimated as a function of the canonical 
innovations, obeying to constraints resulting from the economic theory. The 
imposed restrictions can be of different kind and their economic implications 



European Journal of Government and Economics 1(1) 

 

 
69

diametrically opposite. One distinguishes two types of restrictions used in the 
recent literature: short term restrictions and long run restrictions. 

The short run constraints relate to the instantaneous answers of variable to shocks. 
Long run restrictions are related to the long term shocks responses. Those 
developments make the birth of the structural VAR model, i.e VAR models where it 
is possible to give economic definition to various shocks. 

Data characteristics and VAR model estimation 
The purpose of this section is to analyze the economic disturbances in an open 
economy, the French economy. Empirical study presented in this section is based 
on the VAR methodology incited by Sims (1980). 

These recent developments on time series econometrics are applied to a system of 
variable including output, prices, trade balance, real exchange rate and oil price. 
This system of variables makes possible the evaluation of the main source of 
disturbance in the French economy. So, in an autoregressive vectorial model 
including these variables, five structural shocks are identified with the help of the 
Blanchard and Quah (1989) method of decomposition. 

Long term characteristics of the data 

Before the model estimation, we must preliminary check the order of integration 
and test the possible presence of cointegration relationship between variables. 

We use quarterly data extending from 1978Q1 to 2007Q4:2 

-     y: GDP logarithm   

p: logarithm of consumer price index 

-   se  : logarithm of trade balance  

-  tc:  logarithm of the real effective exchange rate 

- pp:  logarithm of the oil price 

Tests of stationarity 

To analyze the long-term properties of the data, we use three different methods: 
Augmented Dickey-Fuller test (1979), Phillips-Perron test (1988) and Kwiatkowski-
Phillips-Schmidt-Shin test (1992). 

                                                                                                           
2
 Our Data comes from the INSEE (Institut National de la Statistique et des Etudes Economiques) 

database and the IFS (International Financial Statistics) from the IMF (International Monetary Fund). 

- Q for quarter. 

- All series are seasonally adjusted. The oil price is in US dollars. The real effective exchange rate is 
computed with ULCs. 



European Journal of Government and Economics 1(1) 

 

 
70

Table 1. Unit root tests 

Augmented Dickey 
Fuller (ADF)  

Phillips-Perron 
(PP) 

Kwiatkowski-Phillips-Schmidt-Shin 
(KPSS) 

 
Variables 

 
 
 
 
 

Statistics 
of the test 

Critical 
value 
(5%) 

Statistics 
of the test 

Critical 
value 
(5%) 

Statistics 
of the test 

Critical value (5%) 

GDP  5.63 -1.95 13.17 -1.95 1.30 0.46 

∆ GDP -4.92 -2.89* -7.55 -2.89* 0.05 0.46 

Trade 
balance 

-0.17  1.95 -0.03  1.95 0.60 0.46 

∆ (trade 
balance) 

-5.35 -1.95 -10.60 -1.95 0.19 0.46 

Real foreign 
exchange 

rate 
-1.10 -1.95 -1.10 -1.95 1.20 0.46 

∆ (real 
foreign 

exchange 
rate) 

-9.66 -1.95 -9.66 -1.95 0.07 0.46 

CPI -0.05 -1.95 -0.05 -1.95 0.54 0.46 

∆ (CPI) -8.72 -1.95 -8.72 -1.95 0.05 0.46 

The oil price  1.05 -1.95  1.01 -1.95 0.87 0.46 

∆ (Of the oil 
price) 

-8.57 -1.95 -8.60 -1.95 0.15 0.46 

Notes: - * This critical value is relating to the model with constant and without trend. 

      - The character ∆, indicates the first difference of the variable. 

      - All the variables are in logarithm.  

According to unit root tests, it appears that all the variables of the model are 
nonstationary; they are integrated of order one.  

Cointegration relationship 

To test the possible existence of cointegration between variables, we use the test 
implemented by Johansen (1991) and Johansen and Juselius (1990).3 

So we suppose the vector X of dimension (5×1): 

X  =   pptcsepy ,,,,  
The general representation of the model in VECM4 form is given by the following 
expression: 

 tX = C +  ttptpt
XXX   11111 ...  

Where matrices i


 (I = 1,…, p) are of size (N×N). 

The method suggested by Johansen and Juselius is based on two assumptions: on 

one hand the vector X  must be I (1) and in addition the vector of the residual 
  must be a white noise. The strategy of the test consists in analyzing the rank of 

                                                                                                           
3
 The advantage of this method is that it allows for the identification of multiple cointegrating vectors. 

The Engle-Granger cointegration methodology (Engle and Granger, 1987) is limited to testing only for 
one cointegrating vector. 

4
 VECM for Vector Error Correction Model. 



European Journal of Government and Economics 1(1) 

71

the matrix  . If the rank of   is zero then there is no cointegration between the
variables. If the rank of the matrix   is r, there exist two matrices of dimension
(n×r),  and   as:

 = 
'

' is a matrix which contains the r vectors of cointegration.
  is a matrix which contains the weights associated to each vector of
cointegration. 

To determine the number of vectors of cointegration r, Johansen proposes two 
statistics: the trace test and the maximum eigenvalue test. 

The trace statistic is the following: 

TR = - T 

)1log(
1







N

qi
i

The Ho hypothesis is: r ≤ q, i.e. there are at least r vectors of cointegration. This 

test is equivalent to test the rank of the matrix   since testing the existence of r
vectors is equivalent to test the following null assumption: 

Rg (  ) = r
Three cases can be presented: 

Rg (  ) = 0, this means that r = 0: there is no cointegration, in other words tX  is 
integrated of order 1 but not cointegrated. It is then possible to estimate a VAR 

model on  tX .

Rg (  ) = r, with r<N: tX  is cointegrated with r rank, thus r relations of 
cointegration exist. A VECM can be estimated. 

Rg (  ) = N, in this case the matrix   is full rank, tX  is stationary, and there is no 

cointegration. A VAR model can be estimated directly on t
X

.

The maximum eigenvalue test statistic is: 

max 1
log(1 )

q
VP T 




  

The Ho hypothesis is r= q, the alternative one is r= q+1. 

The results of these tests are conditional to the VAR estimation, and consequently 
to the lag choice. We have used AIC criterion, the selected lag number is equal to 
3. We carry out tests by supposing the absence of trend and constant in the
relationship of cointegration and in the VECM model. 



European Journal of Government and Economics 1(1) 

 

 
72

Table 2.  Cointegration test: trace test 

Assumption Eigenvalue Trace Statistic Critical value: 5% Probability 

r = 0 0.193010 48.02 69.81 0.7207 

r ≤ 1 0.082414 23.15 47.85 0.9585 

r ≤ 2 0.075934 13.17 29.79 0.8835 

r ≤ 3 0.024574 4.01 15.49 0.9024 

r≤4 0.009677 1.12 3.84 0.2882 

According to the results appearing in the table 2, we accept the null hypothesis of 
absence of cointegration (48.02< 69.81). Max-eigenvalue test (table 3) also 
indicates no cointegration at the 0.05 level. 

Table 3. Cointegration test: Maximum Eigenvalue test 

Assumption Eigenvalue 
Max-Eigen 

Statistic 
Critical value: 5% Probability 

r = 0  0.193010  24.87  33.87  0.3935 

r ≤ 1  0.082414  9.97  27.58  0.9852 

r ≤ 2  0.075934  9.16  21.13  0.8193 

r ≤ 3  0.024574  2.88  14.26  0.9540 

r≤4  0.009677  1.12  3.841  0.2882 

Finally, the various tests carried out, we conclude by the rejection of the stationarity 
and cointegration of the five series in level, so we specify a model in first 
difference. The VAR model will be consequently built on the growth rate of GDP 

( y ), on the first difference of real effective exchange rate ( )tc , on the first 
difference of trade balance rate ( se ), on the inflation rate ( p ) and on the first 
difference of oil price (∆pp). 

VAR model identification 

Basing on results found below, the VAR model in matrix form is the following: 

tX  = 
tit

p

i
iXA 




1  

tX  = (∆pp, se , y , tc , p ,)
'
, the column vector of the explained variables 

which depends on its p lags. 

iA , are the square matrices of the coefficients to be estimated. 

t , is the vector of the residuals. 

t  = ( pp , se , y , tc , p ,)
'
represents at each moment T, the value of 

tX  which is not explained by its past. These residuals are also regarded as 
innovations or impulses. 

From the vector of variables t
X

, we will define five shocks: a supply shock, a real 
demand shock, a nominal demand shock, an external shock (shock on the trade 
balance) and an oil price shock. 

We consider the residual resulting from the first equation as an oil price shock, the 
one resulting from the second equation as an external shock, that resulting from 



European Journal of Government and Economics 1(1) 

 

 
73

the GDP equation as a supply shock and finally residuals resulting from the fourth 
and the fifth equation are supposed to be demand shocks (real and nominal). 

However, this definition of the shocks is likely to be false since the residuals 
resulting from a canonical VAR model are often correlated. Consequently, we will 
adopt the structural VAR method in which shocks identification is based on 
constraints resulting from the economic theory. 

Identifying constraints 

We adopt the structural VAR methodology developed by Blanchard and Watson 
(1986), Bernanke (1986), Shapiro and Watson (1988), Blanchard and Quah 
(1989). 

Two types of restrictions are mentioned in the literature; short-run restrictions and 
long-run restrictions. To identify the structural shocks, we choose the identification 
of Blanchard and Quah (1989) imposing long run constraints.5 In other words we 
will constrain certain variables not to have long-term effects.  

We attempt to estimate the following structural VAR model: 





























p

tc

y

se

pp

 = )(LD      































p

tc

y

se

pp









 

The left part of this equality describes the vector of variables entering into the VAR 

system. The right-hand side, is a mixture of the structural shocks,   (exogenous 
forces of the system), and of the matrix D (L) which describes the coefficients 
associated to these shocks. 

The identification of the structural shocks is done by imposing long run constraints 
using the matrix D (1). 

The long-term answers to shocks are defined by this matrix: 

)1(D  = 






















)1()1()1()1()1(

)1()1()1()1()1(

)1()1()1()1()1(

)1()1()1()1()1(

)1()1()1()1()1(

5554535251

4544434241

3534333231

2524232221

1514131211

ddddd

ddddd

ddddd

ddddd

ddddd

 

The identification of shocks in a system of 5 variables requires 10 constraints: 

First constraints are drawn from the open economy assumption. It results from this 
hypothesis that domestic shocks do not affect the variables generating the external 

shocks:
0)1()1()1()1()1()1( 252423151413  dddddd . We can then 

identify 6 constraints. 

                                                                                                           
5
 Our choice is justified; this kind of restriction is applied only in the case of integrated variable which is 

the case of our model. 



European Journal of Government and Economics 1(1) 

74

Following constraints are drawn from the theoretical assumption commonly 
accepted since the work of Blanchard and Quah (1989). It is about the distinction 
between supply and demand shocks. Indeed, economic theory supposes that 
supply shocks can affect economic activity on the long term. Whereas demand 
shocks affect economic activity only on the short term. It results two additional 

constraints: 
0)1()1( 3534  dd

Concerning the constraints that allow the distinction between the two demand 
shocks, real demand shock (generally assimilated to a fiscal shock or a foreign 
exchange rate shock) and the nominal demand shock (monetary shock for 
example). A real demand shock is supposed to have an effect on real foreign 
exchange rate whereas a nominal shock does not affect the foreign exchange rate. 

It results an additional constraint: 
.0)1(45 d  

Finally the last constraint is related to the oil price: an oil price shock is supposed 
to be the only shock which can have long-term effects on the oil price. This 
assumption gives us a last constraint: 6 

.

Thus the matrix representing the effects of structural shocks on the model 
variables is the following one: 

)1(D  = 






















)1()1()1()1()1(

0)1()1()1()1(

00)1()1()1(

000)1()1(

0000)1(

5554535251

44434241

333231

2221

11

ddddd

dddd

ddd

dd

d

After the constraints identification, the estimation of the VAR model requires the 
determination of the lag. We use the criterion test of information.  

Lag selection criteria 

The results of AIC, SC and HQ are exposed in the following table. 

Table 3.  Lag selection criteria of the VAR model 

 Lag LogL LR FPE AIC SC HQ 

0  1497.285 NA   1.45e-18 -26.88801  -26.76596*  -26.83850* 

1  1531.364  64.47411  1.23e-18 -27.05160 -26.31930 -26.75453 

2  1559.340   50.40804*   1.17e-18*  -27.10523* -25.76267 -26.56059 

3  1570.737  19.50838  1.50e-18 -26.86013 -24.90732 -26.06793 

4  1589.531  30.47622  1.70e-18 -26.74831 -24.18524 -25.70855 

* indicates lag order selected by the criterion

6
 This assumption allows the identification of these four 

constraints: 0)1()1()1()1( 15141312  dddd . We take the first one, 0)1(12 d , since
the three remaining ones were already taken into account using the   assumption of independence 
between  internal shocks and  external shocks. 

 0 ) 1( 
12 

 d 



European Journal of Government and Economics 1(1) 

 

 
75

The results of AIC tests, SC and HQ, give different conclusions: the order of the 
VAR model is equal to 1 according to criteria SC and HQ, it is equal to 3 according 
to the criterion of AIC, FPE and LR 

The majority of lag length criteria suggest the use of a lag of 3 in the analysis. We 
then take p = 3. 

Estimation of the structural VAR model: impulse 
response functions and variance decomposition 

Impulse response functions and variance decomposition 

SVAR model estimation is conducted by estimating the ordinary VAR model7, then 
we apply the long term identifying constraints previously identified, and finally we 
determinate the structural shocks, their impacts and their contributions to the 
French economy fluctuations. 

Sources of growth rate fluctuations 

Chart 1 represents the response impulse functions of growth rate to the various 
shocks. It shows the effects of the five shocks on the growth rate. We notice a 
positive and significantly persistent effect of supply shocks in short-term us in the 
long run. However, the real and nominal demand shocks affect the economic 
activity only transitorily.  

                                                                                                           
7
 Main validation tests of the VAR model are reported in the appendix. 



European Journal of Government and Economics 1(1) 

 

 
76

Chart 1. Impulse response functions of the growth rate 

-.006

-.004

-.002

.000

.002

.004

.006

.008

5 10 15 20 25 30

Response of the growth rate to external shock

-.006

-.004

-.002

.000

.002

.004

.006

.008

5 10 15 20 25 30

Response of the growth rate to supply shock

-.006

-.004

-.002

.000

.002

.004

.006

.008

5 10 15 20 25 30

Response of the growth rate to real demand shock

-.006

-.004

-.002

.000

.002

.004

.006

.008

5 10 15 20 25 30

Response of the growth rate to nominal demand shock

 

-.0002

.0000

.0002

.0004

.0006

.0008

.0010

.0012

5 10 15 20 25 30

Response ot the growth rate to oil price shock

 

Indeed, as it was predicted by economic theory, a positive supply shock involves 
an improvement of the activity level, this improvement remains rather bearable in 
long-term. However, demand shocks have insignificant impact, their impacts tend 
to be equal to zero in long term. 

Concerning the external shocks, the impulse response functions show a negative 
effect of these shocks on the trade balance. This negative effect can be explained 
by the degradation of price competitiveness of France resulting from the 
progressive appreciation of the euro compared to the dollar, which leads to a fall in 
exports and to an economic activity decrease. This degradation can also be 
explained by the rise of the oil price or other reasons. 

Moreover and basing on the model results we notice the importance of the oil price 
effect on economic activity. The GDP impulse response function to shocks shows 
an important effect of oil price shock on the activity level. The growth rate responds 
negatively to oil price shock. The initial response is the largest one; in long run the 



European Journal of Government and Economics 1(1) 

 

 
77

effect tends to be insignificant. Jalles (2009) adopts several oil price specifications 
and also found a significant impact of oil price shock on French economic activity. 

Table 4. Variance decomposition of the growth rate 

Quarters 
Oil price 
shock 

External 
shock 

Supply shock 
Real demand 

shock 
Nominal 

demand shock 

1  42.28251  22.08729  21.31262  2.140815  12.17676 

4  39.02417  15.70284  34.68763  2.843572  7.741792 

8  35.08653  14.36379  41.12939  2.342595  7.077686 

12  34.60134  13.57045  43.22037  2.148490  6.459353 

16  34.42554  13.13336  44.23477  2.048563  6.157772 

20  34.33957  12.85764  44.77958  1.996534  6.026670 

24  34.29383  12.67877  45.06698  1.968955  5.991462 

28  34.25937  12.56120  45.22237  1.954872  6.002194 

30  34.24477  12.51787  45.27105  1.950650  6.015659 

The table above reveals the contribution of each shock to the growth rate 
fluctuations. We notice the prevalence of supply and oil price shocks in the 
explanation of the growth rate dynamics. Indeed, whatever the chosen horizon, 
short or long run, oil price shock explains between 40% and 35% of the activity 
variability, the supply shock explains between 21 % and 45% of the variability. 
France, as an importer of oil energy, is particularly vulnerable to oil price shock. 

Concerning demand shocks, it arises from the variance decomposition, that these 
shocks have a limited contribution on the long-term economic activity. This 
consolidates our theoretical assumptions, stipulating that demand shocks do not 
have a permanent effect on the GDP. Demand shocks (real or nominal) contribute 
to less than 6 % in the economic activity variability. 

In addition, the introduction of the assumption of an open economy into our model 
enables us to evaluate the contribution of external shocks apprehended in this 
case by shocks on the trade balance. This shock has a considerable effect on the 
economic activity; its contribution turns around 12% in the long term and exceeds 
22% in the short term.  

To summarize, the variance decomposition of GDP shows a prevalence of supply 
and oil price shocks in the economic fluctuation explanation. 

Sources of price fluctuations 

Chart 2 indicates a positive impact of nominal demand shocks. These nominal 
demand shocks reflect the evolution of the money supply and highlight the narrow 
correlation between the price level and the monetary aggregates. Chart 2 
highlights the fact that a nominal demand shock leads to an increase in the general 
prices level. The raising of prices remains constant on the long term. 

In addition, the impulse response functions reveal the importance of supply shocks 
contribution in the variation of the price level. A positive supply shock 
(technological shock for example) will make the production more efficient, leads to 
an increase in output and thus lower the price level as it shown in the chart below. 
The effects of supply shocks are also more important than those of the real 
demand shocks.  

This result can be explained by the limited effects of the French fiscal policy since 
the European Pact of Stability constraints the budget deficit to only 3% of the GDP. 



European Journal of Government and Economics 1(1) 

 

 
78

Chart 2. Impulse response functions of the inflation rate 

-.016

-.012

-.008

-.004

.000

.004

.008

.012

.016

5 10 15 20 25 30

Response of inflation rate to oil price

-.016

-.012

-.008

-.004

.000

.004

.008

.012

.016

5 10 15 20 25 30

Response of inflation rate to external shock

-.016

-.012

-.008

-.004

.000

.004

.008

.012

.016

5 10 15 20 25 30

Response of inflation rate to supply shock

-.016

-.012

-.008

-.004

.000

.004

.008

.012

.016

5 10 15 20 25 30

Response of inflation rate to real demand shock

-.016

-.012

-.008

-.004

.000

.004

.008

.012

.016

5 10 15 20 25 30

Response of inflation rate to nominal demand shock

 



European Journal of Government and Economics 1(1) 

 

 
79

Table 5. Variance decomposition of the inflation rate 

Quarters 
Oil price 
shock 

External 
shock 

Supply shock 
Real demand 

shock 
Nominal 

demand shock 

1  0.013192  1.025571  58.29633  14.97440  25.69051 

4  4.960914  3.455207  48.21400  11.05525  32.31463 

8  7.848685  5.708743  38.26513  8.535835  39.64161 

12  10.09025  5.150766  36.00894  7.568884  41.18117 

16  11.47424  4.805916  35.80208  7.048559  40.86920 

20  12.38600  4.537079  36.00211  6.705005  40.36980 

24  13.03210  4.340852  36.28109  6.471194  39.87476 

28  13.48659  4.200097  36.54344  6.309716  39.46016 

30  13.66247  4.145376  36.65760  6.247748  39.28680 

The variance decomposition of inflation rate shows a very substantial contribution 
of impulses conducted by the economic policy. The contribution is more important 
for nominal demand shocks, about 40%. This prevalence of nominal demand 
shocks remains stable whatever the time horizon. In addition, as we have notice 
when dealing with impulse functions, supply shocks contribute to a significant part 
on inflation rate variation, a contribution which turns around 35%.  

Finally, considering the importance of the French economy openness degree, it 
would be interesting to highlight the external shocks impact on the price level. 
Through the variance decomposition, we notice a very weak contribution of the 
trade balance shock on prices disturbances. This contribution is approximately 
about 4% in the long term, less than 1% in the short term.  

We also notice that an oil price shock is accompanied by an increase in the price 
level essentially in long term. In the short term the relative contribution of the oil 
price shock is almost equal to zero, however in the long term it turns around 13%. 
This increase (even if it remains controlled enough) cuts down the purchasing 
power of households, decreases consumption and consequently the growth rate. 
The oil prices rise of these last years, combined with an unstable geopolitical 
environment, seems to be durable not temporally.  One can expect a continuous 
rise of oil prices; the economic policies should take into account this new 
international evidence by defining more rigorous energetic policies. 



European Journal of Government and Economics 1(1) 

 

 
80

Sources of foreign exchange rate fluctuations 

Chart 3. Impulse response functions of the foreign exchange rate 

-.015

-.010

-.005

.000

.005

.010

.015

5 10 15 20 25 30

Response of the foreign exchange rate to oil price shock

-.015

-.010

-.005

.000

.005

.010

.015

5 10 15 20 25 30

Response of the foreign exchange rate to external shock

-.015

-.010

-.005

.000

.005

.010

.015

5 10 15 20 25 30

Response of the foreign exchange rate to supply shock

-.015

-.010

-.005

.000

.005

.010

.015

5 10 15 20 25 30

Response of the foreign exchange rate to real demand shock

-.015

-.010

-.005

.000

.005

.010

.015

5 10 15 20 25 30

Response of the foreign exchange rate to nominal demand shock

 



European Journal of Government and Economics 1(1) 

 

 
81

Table 6. The variance decomposition of the foreign exchange rate 

Quarters 
Oil price 
shock 

External 
shock 

Supply shock 
Real demand 

shock 
Nominal 

demand shock 

1  4.913600  8.474808  36.02216  50.33014  0.259295 

4  6.114626  10.06832  34.76513  47.35129  1.700633 

8  6.894016  11.43277  33.69233  45.72826  2.252634 

12  7.018748  11.34274  33.99508  45.26362  2.379807 

16  7.080580  11.33318  34.02144  45.18350  2.381303 

20  7.098915  11.32942  34.04914  45.14051  2.382014 

24  7.111069  11.32563  34.06474  45.11766  2.380897 

28  7.117800  11.32325  34.07302  45.10537  2.380558 

30  7.120032  11.32233  34.07584  45.10117  2.380622 

From the table above, we notice a prevalence of domestic shocks in the 
explanation of exchange foreign rate fluctuations; particularly the real demand 
shock, which is assimilated in our model to a fiscal shock or to an adjustment of 
foreign exchange rate. Indeed, they contribute to approximately 50 % of the foreign 
exchange rate disturbance.  

Concerning the nature of the impact, theoretically a real demand shock due to the 
aggravation of budget deficit involves an appreciation of the real foreign exchange 
rate and a deterioration of the external position.  

Our empirical results are in conformity with this theoretical assumption. Impulses 
response functions show an appreciation of the real foreign exchange rate when 
the economy is hit by a real demand shock which is probably caused by the 
overvaluation of the euro/dollar exchange rate since the fiscal policy in France is 
relatively controlled (the European pact of stability). 

Concerning the remainder of domestic shocks, we notice a quite high contribution 
of supply shocks and a very limited contribution of nominal demand shocks; they 
respectively contribute to 36% and 2% of disturbances. Impulses response 
functions reveal that the effects of nominal demand shocks are unstable and close 
to zero. In other hand a positive supply shocks involves a depreciation of the real 
foreign exchange rate. 

Finally concerning the contribution of external and oil price shocks to foreign 
exchange rate fluctuations; it appears from the variance decomposition that these 
two shocks have a significant contribution but still lower in comparison to internal 
shocks. Indeed, the external shocks contribute to approximately 11% of the foreign 
exchange rate variability, oil price shocks contribute to 7%. 

Taking into account these results, we can conclude that foreign exchange rate 
disturbance is due primarily to real demand shocks; they contribute to 
approximately 50%. 



European Journal of Government and Economics 1(1) 

 

 
82

Sources of trade balance fluctuations 

Chart 4. Impulse response functions of the trade balance 

-.004

.000

.004

.008

.012

.016

.020

.024

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Response of the trade balance to oil price shock

-.004

.000

.004

.008

.012

.016

.020

.024

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Response of the trade balance to external shock

-.004

.000

.004

.008

.012

.016

.020

.024

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Response of the trade balance to supply shock

-.004

.000

.004

.008

.012

.016

.020

.024

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Response of the trade balance to real demand shock

-.004

.000

.004

.008

.012

.016

.020

.024

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Response of the trade balance to nominal demand shock

 



European Journal of Government and Economics 1(1) 

 

 
83

Table 7. Variance decomposition of the trade balance 

Quarters 
Oil price 
shock 

External 
shock 

Supply shock 
Real demand 

shock 
Nominal 

demand shock 

1  0.482441  50.32980  6.364164  7.407492  35.41610 

4  4.023561  46.26756  7.478640  10.91904  31.31119 

8  6.074570  42.74174  8.056011  11.02374  32.10393 

12  6.318011  42.39966  8.081979  11.24891  31.95144 

16  6.328384  42.36155  8.091934  11.25460  31.96354 

20  6.329172  42.35446  8.094050  11.25585  31.96647 

24  6.329163  42.35283  8.093642  11.25604  31.96833 

28  6.329101  42.35188  8.093429  11.25587  31.96971 

30  6.329075  42.35161  8.093417  11.25587  31.97003 

Based on the response impulse functions and the Variance decomposition table, 
one notices the importance of nominal demand shocks; they contribute to 
approximately 30% of the trade balance variation in short as in long term. The 
remainder shocks have an insignificant effect on the trade balance. However, it is 
important to underline that an increase in oil prices contributes to the appreciation 
of real exchange rate in the short term and to its depreciation in the long term.  

Since the EMU, appreciation of the euro/dollar exchange rate worsens the French 
external position. The improvement of the French external balance position should 
be made by the amelioration of terms of trade, by the diversification of its business 
partners especially by the acquisition of new market shares (emergent market), by 
reducing labour cost, or by investing in research and innovation, etc. 

Conclusion 
The results founded in this paper underline the French vulnerability to internal as to 
external shocks. They especially highlight the importance of supply and oil price 
shocks in economic fluctuations. Indeed, through the two VAR models instruments 
(impulse response function and variance decomposition), we clearly notice the 
prevalence of supply shocks in the explanation of GDP fluctuations (between 21% 
and 45% depending in time horizon).  

Oil prices shocks explain between 40% and 35% of economic disturbance. France, 
as a net importer of oil energy, is particularly vulnerable to oil price shocks; oil 
shock has a negative and durable effect on the economic activity. So it is important 
to find alternative solutions to reduce the economy dependence on oil energy. 

Concerning demand shocks, particularly those relating to monetary and fiscal 
policies. Results show that their effects are quite limited; this is probably due to the 
restrictive European economic policies adopted since the 1990s, and which is 
strongly controlled since the 1st January 1999 with the establishment of the 
European Monetary Union. 

The improvement of the French economic activity requires a better coordination, a 
better governance of the European economic policies. It also requires the 
improvement of the energy policies in order to attenuate the French economy 
dependence. 

Regarding the French external position, a position to be taken seriously into 
account, France must encourage firms to invest and innovate to improve the 
competitiveness. France should benefit from the strong growth of emergent 
countries like China or India by orientating its exports towards sectors in 
expansion. It should also follows the German example by encouraging investments 
in small and medium-size companies. 



European Journal of Government and Economics 1(1) 

84

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