The impact of Oil Shocks on the Algerian Export Performance (1998

Transcription

Colloque sur : Les politiques d’utilisation des ressources énergétiques : entre les exigences du
développement national et la sécurité des besoins internationaux
The impact of Oil Shocks on the Algerian
Export Performance (1998-2017)
Kamel Si Mohammed
Le premier colloque sur
Les politiques d’utilisation des ressources énergétiques: Entre les exigences du développement national et la sécurité des besoins internationaux
Ph.d Economist , Ain Temouchent centre
Algeria. email: [email protected]
Abstract
The goal of this study is to investigate the effect of the External Shocks on
Algerian exports. In the empirical analysis we use VAR Model (Vector Autoregressive
Model) over the years (1998-2017) of annually data for the main of this study. Results
show that the external shocks (GDP world, oil prices, financial crises variable), as
explanatory variables affected on Algerian exports performance. However, cointegration
test indicate that there exists short and long term relationship between the studies series
and Granger causality tests made it clear that two directional flow, at 5% significance
level, for oil prices and financial crisis to Algeria‟s exports. The estimation of an error
correction model shows a slow adjustment speed of the Algeria‟s‟ exports its long-term
target (with a 0.14 % of deviations of about 5 Quarterlies). The variances decomposition
(VDCS) Analysis showed percentage change of Algeria‟s exports is explained about 58%
by oil shock against 42% of the rest variables in long term.
The Impulse responses (IFR) analysis of Algerian exports concludes are impact
negative for the global financial crisis Including the oil shock which implies that a rise in oil
prices leads to a depreciation in oil price in the late 2014 leads to a depreciation in the
Algerian exports performance.
Key Words:, Algerian exports, oil shocks, financial crises, VECM Model.
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I.
INTRODUCTION
Oil and gas revenues constitute the dominant income of the Algerian economy.
This sector accounted, between “2002 – 2011”, for 97% of exports, 32 to 45 % of GDP
and 46 to 70 % of government revenue, see Figure 1, while trade openness, see Table 1,
exhibits a high figure of 60% in the same period. As far as the Algerian exchange rate is
concerned, the central bank adopted, since 1996, a managed floating exchange rate after a
long experience with the former regime (1974-1995)1 that was built upon a strong
concentration of the US dollar that played an important role due to its 98% in
hydrocarbon export receipts. Between January 2003 and January 2013, the Algerian
exchange rate has varied continuously; from January 2003 to September 2008, the U.S
dollar depreciated monthly against the Algerian Dinar by about 19%, followed by a
depreciation of 6% during the financial crisis. Between January 2010 and January 2013,
the Algerian dinar depreciated against the U.S. dollar by 4.2%. Oil price showed during
these periods‟ remarkable changes with +152%, -9%, +37% (See: Figure N°2).
The goal of this study is to investigate the effect of the oil shocks on Algerian
exports performance upon annually data for the period 1998-2017 through an
empirical analysis using a VAR Model (Vector Autoregressive Model). The rest of the
paper is organized as follows. In section 2 we present a Literature Review on the
relationship; Section 3 presents the Model and the Methodology, followed by the results
and discussion showed in Section 4, and finally, Section 5 presents the main conclusion.
II.
Revue Literature
The oil price and the US dollar are the most attractive indices in the financial
market. As the Algerian economy is highly vulnerable to oil price and US dollar
fluctuations, we shall investigate, in this section, the dynamic relationship between oil
price and exchange rates.
Firstly, Oil price plays a strategic role in the global economy. Many studies have
highlighted its different impacts on macroeconomic variables such as GDP growth,
unemployment rates, inflation, Stock market...(see: Rasche, R. H. and J. A. Tatom
(1977),Darby (1982), Hamilton (1983, 1996, 2003), Lee et al. (1995)Rotemberg and
Woodford (1996),Eltony and Al-Awadi (2001),
Brown and Yücel (2002,
2010),Blanchard and Gali (2007), Bjørland (2008), Chongfeng Wu and Li Yang
(2012), Basher and al. (2012)).
1
Algerian exchange rate was based upon a basket of 14 currencies.
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In Nigeria, many studies have used different types of empirical methods and
examined the impact of oil price in Nigeria economy. While, Olomola and Adejumo
(2006) observed a positive impact where the oil price Shocks led to a macroeconomic
variables. Korhonen et al. (2007) estimated the real exchange rate in OPEC countries
from 1975 to 2005 and three oil-producing Commonwealth Independent States (CIS)
from1993 to 2005 using panel co-integration methods. Their results show that real oil
price has a direct effect on the equilibrium exchange rate in oil-producing countries.
Habib & Kalamova (2007) investigated whether the real oil price has an impact on the
real exchange rates of three main oil-exporting countries: Russia (1995-2006), Norway
and Saudi Arabia (1980-2006). In the first country, the authors found a positive long-run
relationship between the real oil price and the real exchange rate. On the Contrary, for
Norway and Saudi Arabia, results show that there is no impact between the two variables.
Secondly, the U.S. dollar is the most important currency in the world economy. It
plays a major role in the pricing of oil and other commodities in the financial market. The
domination of the US dollar in international trade as a currency commodity lets this
currency serve as a central currency in the exchange rate arrangements of many countries
in each area (Linda S. G 2010).
In the past years, particularly before 2002, oil price and US Dollar were moving
in the same direction, when the US dollar rises, the price of oil is pushed up, and
conversely, when the oil price increases, the US Dollar is appreciated. Since this period,
the relationship between the two variables has changed because of the advent of many
factors such as oil companies‟ targets, the role of the Euro currency, geopolitics,
alternative sources of energy, speculators and Federal Reserve policy, and so forth…
In contrast, oil prices have risen while the dollar continued to weaken against
other major currencies and the depreciation of the dollar could explain, therefore, the
increase in oil prices. Since 2002, the price of a barrel of oil has increased fourfold,
moving from $26 in 2002 to $107 in 2012. On the other hand, the U.S Dollar/Euro
declined annually from 0.944 $US to $1.43 in 2010. Hence, many studies believe there
are negative reverse causality between the U.S dollar and oil price during the last period
(See, Coull, 2009, Verleger (2008), Setser (2008) ,Virginie (2008), Akram.f (2008),
,Sadek and Michel Terraza (2007), Bénassy-Quéré, et al. (2007)). Koranchelian
(2005) finds that in the long-run, Algeria‟s real exchange rate is time varying, and
depends on movements in relative productivity and real oil price. Issa et al. (2006)
pointed out in their study the depreciating effect of the energy price on the Canadian
dollar before 1993 and the appreciation of the Canadian currency after this year.
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III.
Model and Methodology
1. Data source
In our analysis we make use of four macroeconomic variables: Algeria‟s exports
(exp), oil prices (oil) and GDP world. The sample comprised annually observations for
the 1998 - 2017 periods.
The sources of the data over the period reel (1998-2013) are International financial
Statistics different issues, IMF and world development indicator.
For oil prices and GDP World d scenario over the period (2014-2017) we use data of
IMF staff calculation, where we estimate Algeria„s exports data in same years.
Definition of the VAR Model
The Vector Auto Regression (VAR) is commonly used for forecasting systems
of interrelated time series and for analyzing the dynamic impact of random
disturbances on the system of variables. The VAR approach sidesteps the need for
structural modeling by treating every endogenous variable in the system as a function
of the lagged values of all of the endogenous variables in the system.
The mathematical representation of a VAR is:
yt = A1yt-1 + … +Apyt-p+ Bxt + εt…………(1)
Where yt is a k vector of endogenous variables, xt is a d vector of exogenous
variables, A1, Ap and B are matrices of coefficients to be estimated, εt and is a vector
of innovations that may be contemporaneously correlated but are uncorrelated with
their own lagged values and uncorrelated with all of the right-hand side variables.
2. Econometric approach
The model is:
exp0 = f (oil0, gdpw, crises) ………………….scenario optimist (1)
exp1 = f (oil1, gdpw, crises) ………………….scenario pessimist (1)
Converting this economic relationship into an econometric model gives;
logexp= a0+ a1logoil+ a2loggdpw + a3crises+εt
Where:
Logexp
= logarithm of the Algeria‟s exports
loggdpw = logarithm world GDP
logoil= logarithm of oil price
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Crises = dummy variable (1= period of global financial crises, 0= period before and after
financial crises).
a0= Intercept of the function
εt = Random error
a0, a1, a2, a3, a4are parameter estimates.
IV.
Results and Comment
Before datable result empirical Var model, we will begin by following steps
econometric:

Test the stationary of the time series data by Augmented Dickey-Fuller&
Philips and Perron.

Analysis co-integration tests (Granger,1986)

Causality tests if we find the stationary in the series.

Vector autoregressive Model (Var)

The Impulse responses and The variance decomposition analysis

Stationarity and Cointegration tests
Most estimation econometrics classic asleast squaremethod(GLS) based on nonstationary time series produce spurious regression and statistics may simply indicate only
correlated trends rather than a true relationship (Granger and Newbold, 1974).
Augmented Dickey-Fuller (1979, 1981) and Philips and Perron, (1988)tests can be avoid
false results cases and test stationary of times series.
our results of stationarity tests represent in table (2), (3) reject null hypothesis in first
difference that signify no stationarty in all our series bat it‟s accept at a level that signify
integration the variables series at order 1.
Table 2: Augmented Dickey Fuller (ADF) Unit Root test
Variables
ADF
Level
Logexp0
Logexp1
logdpw
Logoil0
Logoil1
crisis
First difference
intercept
Trend and
intercept
intercept
-2.521
-2.443
-0.033
-2.442
2.491
-2. 163
-1.823
-1.318
-1.824
-1.259
-1.177
-4.370
-4.805***
-4.335***
-3.686***
-4.178***
4.178***
-6.092***
*show values are significant at 5 % level with MacKinnon (1996).
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Trend and
intercept
-8.442***
4.728*
-3.538*
-4.887***
-5.842***
-5.880***
**show values are significant at 1% level with MacKinnon (1996).
***show values are significant at 5 % and 1 level with MacKinnon (1996).
Table 3: PhiilipsPerron (PP) Unit Root test
Variables
PP
Level
intercept
Logexp0
Logexp1
logdpw
Logoil0
Logoil1
fcris
-2.521
-2.514
-0.029
-2.44
2.491
-2. 79
Trend and
intercept
-1.8234
-1. 180
-1.934
-1.586
-1.177
-4.370
First difference
intercept
Trend and
intercept
-4.8057***
-8.4420***
-4.3354***
-7.57*
-3.739***
-3.61*
-4.457***
-6.385***
4.178***
-5.842***
-8.315***
-7.793***
*show values are significant at 5 % level with MacKinnon (1996).
**show values are significant at 1% level with MacKinnon (1996).
***show values are significant at 5 % and 1 level with MacKinnon (1996).
 Analysis co-integration tests
Johansen develops two test statistics: Trace statistics ((λtrace) and
maximum eigen statistic (λmax). The results oftrace tests and Max-eigenvalue
indicate twocointegrating at the 0.05 level (Table 4, 5).
Table 4, 5 :Cointegration test
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
None *
Atmost 1 *
Atmost 2
Atmost 3 *
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
0.932793
0.785178
0.472156
0.252988
93.03393
44.43434
16.75133
5.250131
47.85613
29.79707
17.49471
3.841466
0.0000
0.0005
0.0522
0.0219
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None *
Atmost 1 *
Atmost 2
Atmost 3 *
0.932793
0.785178
0.472156
0.252988
48.59959
27.68302
11.50119
5.250131
27.58434
21.13162
14.26460
3.841466
0.0000
0.0052
0.1308
0.0219
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
Causality test
The short-run causality is based on a standard F-test statistics to test jointly the
significance of the coefficients of the explanatory variable in their first differences. The
long-run causality is based on a standard t-test. Negative and statistically significant
values of the coefficients of the error correction terms indicate the existence of long-run
causality.
Granger causality test suggest a tree directional flow, at 5% significance level, for
variables explanatory to Algeria‟s exports, (see table 7),In addition, Granger causality test
suggest a most relationship between variables the study as the directional flow for euro-us
dollar exchange rates to oil prices, relationship bi-directional between oil prices and GDP
world.
Table (7) Causality test
NullHypothesis:
LOGGDPW
Causality
LOGEXP
LOGEXP
LOGGDPW
LOGEXP
LOGOIL
LOGOIL
LOG EXP
CRIS
LOGEXP
LOGEXP
CRIS
LOGOIL
LOGGDPW
LOGGDPW
LOGOIL
CRIS
LOGGDPW
LOGGDPW
CRIS
CRIS
LOGOIL
LOGOIL
CRIS
No
No
No
yes
yes
No
yes
No
No
yes
No
No
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F-Statistic
Prob.
0.47717
0.6253
1.16669
0.3256
0.21565
0.8073
_8.3091
0.0016
3.39762
0.0472
1.75290
0.1911
15.11243
0.014321
0.0004
0.9858
0.43439
0.6518
2.74047
0.1232
0.16198
0.8512
1.99345
0.1545
The Impulse responses

The impulse responses present the dynamic responses of the exogenous
variables in relation with the time of variation of endogenous variable,
(See Doan 1992, Sims and Zha (1999)).

The Impulse responses of optimist scenario
The impulse responses present the dynamic responses of the variables exogenous in
relation with the time that variation of endogens variable. It shows the responses of the
exportation to a one-standard deviation of GDP world; oil prices and financial crises
variables (figure 3).

Responses analyses Shows the results of all period, oil prices
increase Algeria exports about 0.5 to 0.1 a standard deviation over the
reel period then its begins decrease about 1% deviation in years of
optimist scenario 2013-2017,that mean the decrease of oil prices in five
next years will lead to decrease Algeria‟s receipts.

A one-standard deviation shock of GDP world causes Algeria
exports to decrease in two period due by Asian crisis in 1998 after to
increase about 0.3 to 0,2 a standard deviationover the years 2000 to
2007, We also note that in years of optimist scenario, Algeria‟s exports
have negative response to GDP world, that to explain what the
importance Algeria‟s economy to decrease after hydrocarbon era.

Response of Algeria exports to financial crises show there are
negative impact for financial crisis since Asiatic crisis, Enron and
subprime crisis and area zone shocks subsequently.
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Response to Generalized One S.D. Innovations
Response of LOGEXP0 to LOGGDPW
Response of LOGEXP0 to LOGOIL0
.08
.08
.04
.04
.00
.00
-.04
-.04
-.08
-.08
2
4
6
8
10
12
14
16
18
2
20
4
6
8
10
12
14
16
Response of LOGEXP0 to CRISIS
.08
.04
.00
-.04
-.08
2
4
6
8
10
12
14
16
18
20
The Impulse responses of pessimist scenario

Responses analysis of second scenario shows more negative impact than
the first. This scenario shows that Algeria receipts would worsen about 1
to 4 % in future five years. Same scenario show the relationship of
Algeria economy with economy of world would be to decline and would
be more negative relationship after that our oil supply going to finish as
still concern among various geologists and analysts for finding.
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18
20
Response to Generalized One S.D. Innovations
Response of LOGEXP to LOGOIL1
Response of LOGEXP to LOGGDPW
.08
.08
.04
.04
.00
.00
-.04
-.04
-.08
-.08
2
4
6
8
10 12
14
16 18
20
2
4
6
8
10 12
14
Response of LOGEXP to CRISIS
.08
.04
.00
-.04
-.08
2
4
6
8
10 12
14
16 18
20
The Variance Decomposition
The variance decomposition tables show that importance of oil prices to explain
exports variation in short and long term, percentage change of Algeria exportation is
explained about %20 to %35by Oil prices. This analysis shows what the role of Algeria
economy decline in long term, where the GDP world explained about 20 % to 25 %
importance change of Algeria‟s exports in the first period and less a 6 %in a long term
.This resultant determined show external shock of Asian crisis are more affected that
subprime and area zone, where the direction of variance decomposition decrease in time
horizon.
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16 18
20
Table 8: The Variance decomposition
Period
1
2
3
4
5
6
7
8
9
10
11
12
13
14
S.E.
LOGEXP0
LOGOIL0
LOGGDPW
CRISIS
0.056752
0.113250
0.135502
0.146674
0.157896
0.168751
0.183086
0.195746
0.207264
0.216111
0.224345
0.231074
0.236848
0.241261
100.0000
41.20471
48.05027
52.68604
55.62632
59.29072
58.44895
58.61078
57.39207
57.36684
56.35432
55.52151
52.34710
49.33634
0.000000
20.19885
24.94850
23.75406
23.38192
22.15127
24.93386
25.72727
27.53848
28.20719
29.40206
30.29470
32.15918
35.88395
0.000000
12.06779
8.464900
7.245714
6.470197
5.683185
5.224440
4.938730
4.907095
4.783725
4.880033
5.005056
5.299895
5.583475
0.000000
26.52865
18.53633
16.31418
14.52156
12.87483
11.39275
10.72322
10.16235
9.642239
9.363584
9.178738
9.193826
9.196237
Conclusion
In this paper, we investigated if the effect of oil prices and GDP world. However,
the estimation of a VAR model indicates that a 1% increase in oil price would lead the
Algerian GDP to depreciate about 1 to 4 % in future five years (2013-2017). This analysis
shows what the role of Algeria economy decline in long term, when the GDP world
explained about 20 % to 25 % importance change of Algerian exports in the first period
and less a 6 %in a long term Our empirical analysis help explain how the Algerian
policymaker choose his strategy to serve the ever expanding public spending. .
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ANNEXE
Figure 1: Trade (billions of dollars)
90
80
Exports
70
Imports
60
50
Trade Balance
40
30
Non-hydrocarbon
exports
20
10
0
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Source: World Development Indicators.
Table (1): GDP and government revenue dependency on oil
[email protected]
Share of oil in GDP (%)
government expenditure
(billions of dollars)
Trade Openness (%)
2004
2005
2006
2007
2008
2009
2010
2011
2012
35.5
45
45.4
43.3
45.4
31.6
32.5
39
31.7
44.4
46.1
50.8
57.6
73.9
67.4
79.5
9<,5
:8,<
:8,=
:8,:
:=,8
:,,6
:5,5
81
71
Source:* IMF Country Report of Algeria from 2004-201
GDP and government revenue dependency on oil
120
100
80
60
40
20
03
04
05
06
07
08
09
10
Government expenditure (billions $)
Oil
US-DZ
[email protected]
11
12
91.4
97,=

The impact of Oil Shocks on the Algerian Export Performance (1998

Transcription

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