http://ijms.uum.edu.my
IJMS 23 (2), 27–44 (2016)
PETROLEUM CONSUMPTION AND
FINANCIAL DEVELOPMENT IN MALAYSIA
JEN-EEM CHEN
Fakulti Pengurusan Perniagaan
Universiti Teknologi Mara
YAN-LING TAN
University Technology MARA
Johor Branch, Malaysia
CHIN-YU LEE
Faculty of Business and Finance
Universiti Tunku Abdul Rahman
LIM-THYE GOH
Faculty of Economic and Management
Universiti Putra Malaysia
Abstract
This paper aims to contribute to the existing literature by examining the dynamic
relationship among petroleum consumption, financial development, economic
growth and energy price. The sample of this study is based on the Malaysian
annual data from 1980 to 2010. The model specification was examined in the
Autoregressive Distributed Lag (ARDL) framework and the results revealed
the existence of a long-run equilibrium. The findings indicated that financial
development and economic growth cause a demand for energy to escalate in the
long run. The Toda-Yamamoto (TYDL) non Granger-causality test provides
evidence that there is unidirectional Granger-causality running from financial
development and economic growth to energy consumption in the long run.
This suggests that Malaysia is not an energy-dependent country. Hence, the
government could implement energy conservation policies to reduce the waste of
energy use. Given that development in the financial sector, and economic growth
increase petroleum consumption in Malaysia, the policies pertaining to energy
consumption should incorporate the development of the financial sector and
economic growth of country.
Keywords: Petroleum consumption, financial development, non-renewable
energy, Autoregressive Distributed Lag (ARDL), Toda-Yamamoto (TYDL) non
Granger-causality test
Received: 24/11/2015 Revise: 27/09/2016 Accepted: 05/10/2016 Publish: 15/12/2016
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IJMS 23 (2), 27–44 (2016)
Introduction
Studies on the association between energy consumption and
economic growth have been widely examined in the past decades.
Indeed, energy can be classified into two types, namely renewable
and non-renewable. Most of these studies focused on renewable
energy such as electricity consumption1. On the contrary, the debate
on non-renewable energy is not as extensive as renewable energy
as in the investigation of the nexus between coal consumption and
growth by Apergis and Payne (2010a) and Wolde-Rufael (2010),
natural gas consumption and growth by Apergis and Payne (2010b),
and fossil fuel-growth nexus by Ishida (2013). Petroleum, one of the
non-renewable resources, is categorized as fossil fuel that neither
can be reproduced nor generated. According to the U.S. Energy
Information Administration (2011), the liquid’s share of the world
marketed energy consumption was 34 per cent in 2008 and will be
reduced to 29 per cent in 2035. Liquid fuel, the most petroleum-based,
nonetheless, still remains as the largest source of energy. Besides,
liquid fuel is an important energy source in the transportation and
industrial sector processes. Despite the rising fuel prices, the use of
liquids for transportation increased by an average of 1.4 per cent in a
year, or 46 per cent overall from 2008 to 2035 (U.S. Energy Information
Administration, 2011). The transportation sector accounts for 82 per
cent of the total increase in liquid fuel use from 2008 to 2035 while
the remaining portion of the growth is attributable to the industrial
sector.
In Malaysia, oil production stood at 862,000 barrels per day in
2004 and declined to 630,000 barrels per day in 2011, of which 83
per cent was crude oil, due to the maturity of the reservoirs (U.S.
Energy Information Administration, 2011). This percentage will keep
decreasing if there is no new discovery or new technology to increase
the production from the existing fields. Ong, Mahlia and Masjuki
(2012) commented that Malaysia’s oil reserves will be exhausted in
21 years if the production rate is consistent at 700,000 barrels per day.
Thus, studies on non-renewable energy are essential to some extent.
On the one hand, the relationship between energy consumption and
financial development was highlighted in recent studies by Shahbaz
and Lean (2012), Islam, Shahbaz, Ahmed and Alam (2013), and Coban
and Topcu (2013). Financial development affects energy consumption
in two ways. Financial markets boost up energy use through
industrial growth by creating more demand for new infrastructures
(Islam et al., 2013). Likewise, financial development could also reduce
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IJMS 23 (2), 27–44 (2016)
energy consumption through the efficient use of energy (Islam et
al., 2013; Mielnik & Goldemberg, 2002). Given that the relationship
between energy consumption and financial development was newly
introduced with limited empirical evidences, this motivates us to fill
the research gap by employing non-renewable energy, in particular
petroleum consumption, and financial development.
The aim of this study is to investigate the long-run association among
petroleum consumption, financial development, economic growth,
and energy price in Malaysia, spanning from 1980 to 2010. This
paper differs from existing works in several ways. First, we focused
on non-renewable energy in comparison to renewable energy that
has been intensively used. Second, in the context of energy use and
financial development, we employed a more specific variable for
non-renewable energy, which is petroleum consumption, instead of
energy use in kilogram of oil equivalent per capita which is commonly
examined in the prevailing literature. In addition, petroleum is also a
major source of energy. Third, in the methodology, we estimated the
long-run relationship by using the ARDL framework which is more
appropriate for a small sample size as in this study. We used TodaYamamoto (TYDL) approach in examining the Granger-causality
pattern of the variables in the system. The advantage of the TYDL
is that it does not require each variable to be integrated in the same
order. This goodness is applied to ARDL as well. Fourth, this study
was based on a single-country sample that has some implications in
designing related policies, particularly when Malaysia is one of the
oil-exporting countries.
Following is the structure of this paper. In section 2, we provide some
reviews on the theoretical and empirical studies. The data and model
specification are presented in section 3. Section 4 reports the empirical
results. Finally, the conclusion and policy implications are discussed
in section 5.
Literature Review
The theoretical and empirical literatures on the nexus between
energy consumption and economic growth have long been discussed
substantially over the past decades. From the theoretical point of
view, the energy consumption-growth nexus can be categorized
into four well-established hypotheses. These hypotheses are growth,
conservation, feedback and neutrality (Apergis & Payne, 2010b;
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IJMS 23 (2), 27–44 (2016)
Apergis & Payne, 2011a & 2011b; Lean & Smyth, 2010a & 2010b). The
growth hypothesis suggests that energy consumption has a significant
impact on economic growth. Alternatively, the conservation
hypothesis exists if there is unidirectional causality running from
economic growth to energy consumption. The feedback hypothesis
asserts the interdependent connection between energy consumption
and economic growth, implying bidirectional causality between
these two variables in either direction. Lastly, the non-existence of
causality between energy and growth is in line with the neutrality
hypothesis where energy consumption has little impact or no impact
on economic growth.
A growing body of empirical studies examines the relationship
between energy consumption, in particular renewable energy, and
economic growth (Apergis & Payne, 2011b; Chandran, Sharma
& Madhavan, 2010; Golam Ahamad & Nazrul Islam, 2011; Lean &
Smyth, 2010a, 2010b). Yet there exists comparatively little work
that investigates the non-renewable energy and growth nexus. For
instance, non-renewable energy consumption includes coal (Apergis
& Payne, 2010a; Wolde-Rufael, 2010), natural gas (Apergis & Payne,
2010b), fossil fuel (Ishida, 2013), and oil (Asafu-Adjaye, 2000). In the
study of Apergis and Payne (2010a), the empirical evidence revealed
that there was bidirectional causality between coal consumption and
growth in 15 emerging market economies from 1980 to 2006 in both
the short run and the long run. Similarly, Wolde-Rufael (2010) also
found a bidirectional causality running between coal consumption
and growth in South Africa and the United States between 1965
and 2005. In the meantime, a unidirectional causality running from
coal consumption to growth was identified in India and Japan while
causality running from growth to coal consumption was found in China
and South Korea. Besides, bidirectional causality between natural gas
consumption and economic growth was discovered in 67 countries
from 1992 to 2005 (Apergis & Payne, 2010b). The findings of Ishida
(2013) also pointed out that fossil fuel and growth depict bidirectional
causality in Japan. On the other hand, Asafu-Adjaye (2000) found
mixed results in Asian developing economies. The findings showed
that there was bidirectional causality between oil consumption and
economic growth in Thailand and the Philippines, while there was
unidirectional causality running from oil consumption to growth in
India and Indonesia in the short run.
To date, some studies have shifted towards exploring the causation
between energy use and financial development. Financial development
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IJMS 23 (2), 27–44 (2016)
is particularly important because it is claimed to improve economic
efficiency of the financial system (Sadorsky, 2010). The Theoretical
literature suggests that a well-developed financial market can affect
energy consumption through three channels (Sadorsky, 2011). These
channels are named direct effect, business effect, and wealth effect.
In direct effect, financial development helps consumers to access
credit facilities more easily in purchasing consumer goods such as
automobiles, houses, and home appliances which consume large
amounts of energy. Improved financial sector also benefits the
businessess where financial capital can be obtained easily at a lower
cost for business expansions. Thus, energy use will be stimulted
following the expansion in business units. Stock market development,
on the one hand, influences businesses as it provides extra sources of
funding and equity financing in expanding the existing business that
boosts the demand for energy. An increase in stock market activity
will create a wealth effect where it affects the confidence level of
consumers and businesses. Subsequently, high economic confidence
contributes to greater demand for energy in propelling growth.
Besides, financial sector development benefits industrial growth as
well by creating demands for new infrastructures, hence boosting
the energy use (Islam et al., 2013). Contrary to the positive impacts
of financial development on energy use, financial development may
reduce energy consumption through the efficient use of energy (Islam
et al., 2013; Mielnik & Goldemberg, 2002).
Despite the importance of financial development in energy
economics, empirical evidences that account for the effect of financial
development is relatively scarce. Sadorsky (2010) investigated
the causal linkage between financial development and energy
consumption in 22 emerging countries in a period of study which
ranged from 1990 to 2006. The results obtained revealed that financial
development and energy consumption were positively associated.
The positive relationship between financial development and energy
consumption was also found in 9 Central and Eastern European
(CEE) economies over the period 1996-2006 (Sadorsky, 2011). Almulali and Sab (2012a, 2012b) concluded that energy consumption
increases financial development and economic growth in 19 selected
countries and 30 Sub-Saharan African countries. Likewise, Coban
and Topcu (2013) found that financial development has no significant
impact on energy use in EU. Nevertheless, financial development
affects energy consumption positively in EU’s old members. Turning
to single-country experience, the empirical findings of Shahbaz and
Lean (2012) indicated the presence of long-run bidirectional causality
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emerging countries in a period of study which ranged from 1990 to 2006. The results obtain
revealed that financial development and energy consumption were positively associated. T
positive relationship between financial development and energy consumption was also fou
in IJMS
9 Central
and Eastern European (CEE) economies over the period 1996-2006 (Sadors
23 (2), 27–44 (2016)
2011). Al-mulali and Sab (2012a, 2012b) concluded that energy consumption increa
financial development and economic growth in 19 selected countries and 30 Sub-Sahar
between
energy Likewise,
demand and
financial
development
in Tunisia
from development h
African
countries.
Coban
and Topcu
(2013) found
that financial
to 2008.Ozturk
andenergy
Acaravci
pointed
out that financial
there wasdevelopment affe
no1971
significant
impact on
use(2013)
in EU.
Nevertheless,
a short-run
unidirectional
causal
linkage
from financial
development
energy
consumption
positively
in EU’s
old members.
Turning
to single-country experien
energy usefindings
in Turkey.
the other
and Tang
(2012),
with
theto empirical
of On
Shahbaz
andhand,
LeanTan
(2012)
indicated
the
presence of long-r
a period ofcausality
study that
rangedenergy
from 1972
to 2009,
found that
energy
bidirectional
between
demand
and financial
development
in Tunisia fro
consumption
Grangerand
causes
financial
development
in that
Malaysia.
1971
to 2008.Ozturk
Acaravci
(2013)
pointed out
there was a short-r
This impliescausal
that energy
an financial
essential development
input for financial
sector
unidirectional
linkage is
from
to energy
use in Turkey. On
development
et al.,
2013).with
Another
evidence
from
other
hand, Tan (Islam
and Tang
(2012),
a period
of study
thatMalaysia
ranged from 1972 to 20
claimed
financial
development
economic
growth
affected in Malaysia. T
found
that that
energy
consumption
Grangerand
causes
financial
development
energy
consumption
1971 to 2009.
implies
that
energy is an significantly
essential inputfrom
for financial
sector Furthermore,
development (Islam et al., 201
evidence
suggests
that
financial
development
could development
reduce energy
Another
evidence
from
Malaysia
claimed
that financial
and economic grow
use by enhancing
energy efficiency.
affected
energy consumption
significantly from 1971 to 2009. Furthermore, eviden
suggests that financial development could reduce energy use by enhancing energy efficienc
Model Specification and Methodology
Model Specification and Methodology
The model specification in this study was modified from the empirical
work of Islam et al. (2013). We focused on petroleum consumption
The
this study
was modified
the empirical
to model
reflect specification
the extent ofinenergy
resources
which from
Malaysia
relies on,work of Islam et
(2013).
We
focused
on
petroleum
consumption
to
reflect
the
instead of aggregate energy data as performed by Islam et al.extent
(2013)of energy resourc
which
Malaysia
relies
on,
instead
of
aggregate
energy
data
as
performed
which added little value to policy makers. The regression equation inby Islam et al. (20
which
added is
little
valueas:
to policy makers. The regression equation in this study is written a
this study
written
ln
PETt   0  1 FDt   2 ln GDPt   3 ln EPt   t
(1)
where α is the intercept, β ,β , and β are the coefficients to be
(1
1 2
3
where
α is the
intercept,time
β1,βperiod,
coefficients PET
to bedenotes
estimated, t denotes ti
2, and βand
3 areε the
estimated,
t denotes
is error-term.
t
period,
and
ε
is
error-term.
PET
denotes
petroleum
consumption
in
a
thousand barrels per d
t
petroleum consumption in a thousand barrels per day to represent
to non-renewable
represent non-renewable
energy.
FD
is
financial
development
which
energy. FD is financial development which is is measured by
domestic
credit
to
the
private sector
cent of
GDP.inGDP
represents
measured by the domestic
creditintothe
theper
private
sector
the per
cent economic grow
and
EP
is
energy
price
which
is
measured
by
the
consumer
price
index
of GDP. GDP represents economic growth, and EP is energy price(CPI). All variab
arewhich
expressed
in natural
logarithm
except
FD.
The(CPI).
data spanned
from 1980 to 2010
is measured
by the
consumer
price
index
All variables
Malaysia
and
has
collected
from
the
World
Development
Indicator
(WDI) and the U.
are expressed in natural logarithm except FD. The data spanned
Energy
Information
Administration
(EIA).
We
postulate
financial
development
and ener
from 1980 to 2010 for Malaysia and has collected from the World
consumption
are
positively
associated.
A
developed
financial
market
stimulates
investm
Development Indicator (WDI) and the U. S. Energy Information
and
economic
activities
through
two
platforms
(Minier,
2009;
Sadorsky,
2010).
The f
Administration (EIA). We postulate financial development and
platform
is
level
effect.
As
financial
market
develops,
the
amount
of
funds
available
energy consumption are positively associated. A developed financial
investment
also increases.
This in turn
to that practice
market stimulates
investment
and promotes
economicregulations
activities through
two better accounti
and
reporting
systems
which
are
crucial
in
gaining
investors’
confidence
platforms (Minier, 2009; Sadorsky, 2010). The first platform is level (Sadorsky, 201
Shahbaz
& financial
Lean, 2012).
The
second the
platform
is of
efficiency
effect. Financial
developm
effect. As
market
develops,
amount
funds available
for
enhances
liquidity
and
asset
diversification
which
directs
investment
to
going
to a high
investment also increases. This in turn promotes regulations to that
return
and
high
risk
projects
(Sadorsky,
2010).
Therefore,
financial
development
genera
practice better accounting and reporting systems which are crucial
4 2010; Shahbaz & Lean,
in gaining investors’ confidence (Sadorsky,
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http://ijms.uum.edu.my
IJMS 23 (2), 27–44 (2016)
2012). The second platform is efficiency effect. Financial development
enhances liquidity and asset diversification which directs investment
to going to a higher return and high risk projects (Sadorsky, 2010).
Therefore, financial development generates greater investment,
economic growth, and energy use. On the other hand, we expect that
economic growth and energy consumption are positively correlated.
High economic growth promotes industrialization and greater
business activities that induce greater use of energy (Halicioglu, 2007;
Shahbaz & Lean, 2012). The expected sign for energy price is negative
based on the energy demand function (Halicioglu, 2007).
Unit Root Test and Cointegration
greater investment, economic growth, and energy use. On the other hand, we expect that
Prior
to and
estimating
the regression,
weOn
first
theexpect
unit
root
greater
investment,
economic
growth,
and are
energy
use.
the conducted
otherHigh
hand,economic
we
that
economic
growth
energy consumption
positively
correlated.
growth
economic
growth
and energy
are
positively
economic
test
on each
of the
variables
using
both correlated.
the
Dickey
Fuller
promotes
industrialization
andconsumption
greater business
activities
that augmented
induceHigh
greater
use of growth
energy
promotes
industrialization
and
greater
business
that
greater
use isofnegative
energy
(Halicioglu,
2007;
Shahbaz
&rest
Lean,
2012).
The
expected1979)
signinduce
for
energy
(ADF)
unit
root
(Dicky
& activities
Fuller,
and
theprice
Philips-Perror
(Halicioglu,
Shahbaz
&
Lean, (Halicioglu,
2012). The expected
sign for energy price is negative
based on(PP)
the 2007;
energy
demand
function
2007).
unit root test (Philips & Perron, 1988) to determine the order of
based on the energy demand function (Halicioglu, 2007).
integration. Both tests were employed to obtain the robustness of the
Unit Root Test and Cointegration
result.
After
the order of integration, we examined the
Unit Root
Test
anddetermining
Cointegration
presence of the long-run relation or cointegration among the variables
Prior to estimating the regression, we first conducted the unit root test on each of the variables
by
applying
theDickey
autoregressive
distributed
lagtest
(ARDL)
bound
testing
Prior
estimating
the regression,
we
first(ADF)
conducted
on Fuller,
each of
the variables
usingtoboth
the
augmented
Fuller
unit the
rootunit
restroot
(Dicky
&
1979)
and
the
using
both
the augmented
(ADF)
unit
root rest
(Dicky
&test
Fuller,
1979)
and the
(Pesaran,
& Smith,
2001).
ARDL
bound
is favorable
Philips-Perror
(PP) Shin
unit Dickey
root
testFuller
(Philips
&The
Perron,
1988)
to determine
the order
offor
Philips-Perror
(PP)
unit properties
root
(Philips
& obtain
Perron,the1988)
to determine
the
order
of
integration.
tests
were test
employed
robustness
of thethe
result.
After
smallBoth
sample
and to
applicable
for
series
with
mixed
I(0)
integration.
Both
tests
were employed
to obtainthethe
robustness
the result.
After
determining the
order
of integration,
we examined
presence
of theoflong-run
relation
or
and
I(1).
Nonetheless,
it
requires
the
dependent
variable
to
be
I(1)
determining
order the
of integration,
examined
presence of distributed
the long-runlag
relation
or
cointegrationthe
among
variables byweapplying
thethe
autoregressive
(ARDL)
whileamong
the independent
variables
can
either
be I(0)
or
I(1). Establishing
cointegration
theShin
variables
by
applying
theARDL
autoregressive
distributed
lag for
(ARDL)
bound testing
(Pesaran,
& Smith,
2001). The
bound
test
is favorable
small
the
long-run
equilibrium
relationship
inI(0)
the
framework,
bound
(Pesaran,
Shin
& Smith,
2001).
The the
ARDL
bound
test
isI(1).
favorable
for smallit
sampletesting
properties
and applicable
for series
with
mixed
andARDL
Nonetheless,
sample
properties
and
applicable
forI(1)
series
the error
mixed correction
I(0)
and I(1).
Nonetheless,
it
requiresinvolves
the dependent
variable
tothe
be
whilewith
the independent
variables
canmodel
either
be(UECM)
I(0) or
estimating
unrestricted
requires
the
dependent
variable
to
be
I(1)
while
the
independent
variables
can
either
be
I(0)
or
I(1). Establishing
the
long-run
equilibrium
relationship
in
the
ARDL
framework,
involves
in the following form:
I(1).
Establishing
the long-run
relationship
framework,
involves
estimating
the unrestricted
error equilibrium
correction model
(UECM)ininthe
theARDL
following
form:
estimating the unrestricted error correction model (UECM) in the following form:
PETt  0  1 PETt 1   2 FDt 1   3GDPt 1   4 EPt 1
PETt  0  1 PET
t 1   2 FDt 1   3GDPt 1   4 EPt 1
n
n
n
n
i 1
i 0
i 0
i 0
n  1i PETt i  
n  2i FDt i  
n  3i GDPt i  
n  4i EPt i   1t

i 1  1i PETt i  
i 0  2i FDt i  
i 0  3i GDPt i  
i 0  4i EPt i   1t

(2)
(2)
(2)
FDt   0  1 FDt 1   2 PETt 1   3GDPt 1   4 EPt 1
FDt   0  1 FDt 1   2 PETt 1   3GDPt 1   4 EPt 1
+
+
k
k
k
k
i 0
i 0
i 0
k  2 i PETt i  
k  3i GDPt i  
k  4i EPt i   2 t
 1i FDt i  

i 1  1i FDt i  
i 0  2 i PETt i  
i 0  3i GDPt i  
i 0  4i EPt i   2 t

k
i 1
(3)
(3)
GDPt   0  1GDPt 1   2 PETt 1   3 FDt 1   4 EPt 1
GDPt   0  1GDPt 1   2 PETt 1   3 FDt 1   4 EPt 1
m
m
m
m
i 1
i 0
i 0
i 0
m  1i GDPt i  
m  2i PETt i  
m  3i FDt i  
m  4i EPt i   3t

i 1  1i GDPt i  
i 0  2i PETt i  
i 0  3i FDt i  
i 0  4i EPt i   3t

(3)
33
(4)
http://ijms.uum.edu.my
k


+
+
k
i 1
i 1
k
k
k
i 0
i 0
i 0
k  PET
k
k
FDt i  
2i
t i    3i GDPt i    4i EPt i   2 t
i 0  2 i PETt i  
i 0  3i GDPt i  
i 0  4i EPt i   2 t
1i FDt i  
1i
IJMS 23 (2), 27–44 (2016)
(3)
(3)
GDPt   0  1GDPt 1   2 PETt 1   3 FDt 1   4 EPt 1
GDPt   0  1GDP
t 1   2 PETt 1   3 FDt 1   4 EPt 1
m
m
m
m
m  GDP  
m  PET
m
m

1i
t i
2i
t i    3i FDt i    4i EPt i   3t
i 1  1i GDPt i  
i 0  2i PETt i  
i 0  3i FDt i  
i 0  4i EPt i   3t

i 1
i 0
i 0
i 0
(4)
(4)
(4)
EPt   0  1 EPt 1   2 PETt 1   3 FDt 1   4 GDPt 1
EPt   0  1 EPt 1   2 PETt 1   3 FDt 1   4 GDPt 1
q
q
q
q
i 1
i 0
i 0
i 0
q  EP
q
q
q

1i
t i    2i PETt i    3i FDt i    4 i GDPt i   4t
i 1 1i EPt i  
i 0  2i PETt i  
i 0  3i FDt i  
i 0  4 i GDPt i   4t

5
5
(5)
(5)(5)
where Δ is the first difference operator and εt is disturbance. The F-test
statistic is used to ascertain the existence the long-run relationship by
examining the lagged level of variables. The null hypothesis of no
cointegration is set as Ho: q1 = q2 = q3 = q4 = q against H1: q1 ≠ q2 ≠ q3
≠q4 ≠ q . The computed F-test statistic under the null hypothesis of
no cointegration are expressed as F(PET|FD, GDP, INF), F(FD|PET,
GNP, INF), F(DGP|PET, FD, INF), and F(INF|PET, FD, GDP) and for
each equation, correspondingly.
The asymptotic distribution of F-statistics is non-standard as it relies
on whether the underlying variables are I(0) or I(1), the number of
independent variables, and whether the intercept and /or a trend is
included in the ARDL model. Based on this ground, Pesaran et al.
(2001) provided two sets of critical values where the first set assumes
the variables to be I(0) that is known as the lower bound critical values
and the other as I(1) which is called the upper bound critical values.
Given that the critical values generated by Pesaran et al. (2001) are
based on a large sample size ranging between 500 to 1000 observations,
Narayan (2005) computed another set of critical values for a small
sample size with 30 to 80 observations. In this study, therefore, we
used Narayan’s (2005) critical values as the sample size covering
only 30 observations. If the computed F-statistics exceeds the upper
bound critical value, we reject the null hypothesis and conclude that
cointegration exists. If the computed F-statistics is below the lower
bound critical value, we fail to reject the null hypothesis which
implies no cointegration. If the computed F-statistics falls within the
upper and lower bound critical values, then the cointegration test is
inconclusive.
34
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IJMS 23 (2), 27–44 (2016)
Granger-Causality
We used the Toda-Yamamoto (1995) approach in examining the
Granger-causality pattern of variables in the system. Toda-Yamamoto
(1995) developed a modified Wald-test (MWALD) which allowed
us to conduct the long-run causality test that does not require the
same order of integration and cointegration properties. However, we
performed the unit root test to gauge the knowledge of the maximal
order of integration (dmax) for the corresponding variables in the
system. The maximal order of integration (dmax) was then augmented
in VAR order and was estimated as k + dmax where k is optimal lag
length that was determined by the Schwarz Bayesian Criteria (SBC).
In estimating the augmented VAR (k + dmax) model, we put dmax = 1 as it
performs better than other orders of dmax (Dolado & Lϋtkepohl, 1996).
Furthermore, to capture the direction of causality, we considered only
the first k VAR coefficient matrix and disregarded the coefficients of
the last lagged dmax vector (Caporale & Pittis, 1999). Following is the
matrix to be estimated in the augmented VAR model:
 PETt   1   11,1 12,1 13,1 14,1   PETt 1 

 PET
   

22
23
24
11,1
,1
12,
,1
13,1
,1
14,1
,1   PET
1
 FDt t11
 FDt t    21    21
32 3121,1,1 3222,1,1 3323,1,1 3424,1,1GDP
FDtt11 
FDtt  
GDP

GDP      


4232,1,1 4333,1,1 4434,1,1GDP
EPt 1t 1
31,1,1
 EPt t  43  41


 EPt   4    41,1  42,1  43,1  44,1   EPt 1 
 11,k

11,,kk
21
 ...  
31
21, k
 ...   ,k
41
 31,,kk
  41,k
 11,q

11,,qq
21
 
3121,q,q

  31,q,q
 41
  41,q
12,k
22
12,,kk
3222,k,k
4232,,kk
 42,k
13,k
23
13,,kk
3323,k,k
4333,k,k
 43,k
14,k   PETt k 


24
14,,kk   PET
 FDt tk k 
FDttkk 
3424,k,kGDP




4434,,kk  GDP
EPt tk k


 44,k   EPt  k 
12,q 13,q 14,q   PETt q   1t 
  FD   
22
23
24
 21tt
12,,qq
13,,qq
14,,qq   PET
t t q q 

  



FD



 3222,q,q  3323,q,q  3424,q,q  GDPttqq  32t t
  

4232,q,q 4333,q,q 4434,,qqGDP
EPt tqq 43tt 
  

 42,q  43,q  44,q   EPt q   4t 
(6)
(6)
(6)
where
is order
oflength
lag length
and
q isoforder
(k + dmax).theWe
set the of causality
where
k isk order
of lag
and q is
order
(k + dof
hypothesis
max). We set
hypothesis
of
causality
direction
as:
direction
as:
where k is order of lag length and q is order of (k + dmax). We set the hypothesis of causality
direction as:
indicating
FD does
not Granger
FD does
not Granger
cause PET.
Ho1 : 12,1  12, 2  ...  12,k  0, indicating
cause
PET.
Ho1 : 12,1  12, 2  ...  12,k  0, indicating FD does not Granger cause PET.
Ho2 :  21,1   21, 2  ...   21,k  0, indicating PET does not Granger cause FD.
Ho2 :  21,1   21, 2  ...   21,k  0, indicating PET does not Granger cause35FD.
Ho3 : 13,1  13, 2  ...  13,k  0, indicating GDP does not Granger cause PET.
Ho3 : 13,1  13, 2  ...  13,k  0, indicating GDP does not Granger cause PET.
http://ijms.uum.edu.my
where k is order of lag length and q is order of (k + dmax). We set the hypothesis of causality
where
k isas:
order of lag length and q is order of (k + dmax). We set the hypothesis of causality
direction
where
k isas:
order of lag length and q is order of (k + dmax). We set the hypothesis of causality
direction
direction as:
IJMS
Ho :23
 (2),27–44
 (2016)
...    0, indicating FD does not Granger cause PET.
Ho11 : 1212,1,1  1212, ,22  ...  1212,k,k  0, indicating FD does not Granger cause PET.
Ho1 : 12,1  12, 2  ...  12,k  0, indicating FD does not Granger cause PET.
indicating
does
not Granger
Ho :   
 ...  
 0, indicating
PET PET
does not
Granger
cause FD.
Ho22 :  2121,1,1   2121,,22  ...   2121,k,k  0, indicating
PET does not Granger cause FD.
cause
FD.
Ho :   
 ...  
 0, indicating PET does not Granger cause FD.
2
21,1
21, 2
21, k
Ho :     ...    0, indicating GDP does not Granger cause PET.
Ho33 : 1313,1,1  1313, ,22  ...  1313,k,k  0, indicating
GDP GDP
does not
Granger
cause PET.
indicating
does
not Granger
Ho3 : 13,1  13, 2  ...  13,k  0, indicating
GDP does not Granger cause PET.
cause PET.
Ho :     ...    0, indicating PET does not Granger cause GDP.
Ho44 :  3131,1,1   3131,,22  ...   3131,k,k  0, indicating PET does not Granger cause GDP.
Ho4 :  31,1   31, 2  ...   31,k  0, indicating
PET does
Granger
cause GDP.
indicating
PET not
does
not Granger
and so on and so forth for the rest of variables.
cause GDP.
and so on and so forth for the rest of variables.
and so on and so forth for the rest of variables.
and so on and so forth for the rest of variables.
Results Discussion
Results Discussion
Results
Discussion
Results Discussion
Conducting the unit root test is essential in analyzing time series-based macroeconomic
Conducting
the
unitspurious
root test
is essential
in analyzing
time
series-based
macroeconomic
variables
to the
avoid
regression.
Thus,
augmented
Dickey-Fuller
(ADF)
and PhilipConducting
the
unit test
root
is Thus,
essential
in analyzing
time (ADF)
seriesConducting
unit
root
is test
essential
in augmented
analyzing
time
series-based
macroeconomic
variables
to
avoid
spurious
regression.
Dickey-Fuller
and PhilipPerron
(PP)
unit
root
tests
were
conducted
to
determine
the
order
of
integration.
Specifically,
variables
to
avoid
spurious
regression.
Thus,
augmented
Dickey-Fuller
(ADF)
and
Philipbased
macroeconomic
variables
to to
avoid
spurious
regression.
Thus,Specifically,
Perron
(PP)
unit
root
tests were
conducted
determine
thewith
order
ofinclusion
integration.
all variables
were
examined
at conducted
level and first
difference
theof
of intercept
and
Perron
(PP)
unit
root
tests
were
to
determine
the
order
integration.
Specifically,
augmented
Dickey-Fuller
(ADF)
and
Philip-Perron
(PP)
root tests
all
variables
were
examined
at
level
and
first
difference
with
the unit
inclusion
of intercept and
trend.
The
results
of
the
unit
root
tests
are
reported
in
Table
1.
all
variables
were of
examined
at level
difference
with
of intercept and
trend.
The
results
the
root
testsand
are first
reported
in Table
1. the inclusion
were
conducted
tounit
determine
the
order
of
integration.
Specifically,
trend.
The
results
of
the
unit
root
tests
are
reported
in
Table
1.
all variables were examined at level and first difference with the
inclusion
of intercept and trend. The results of the unit root tests are
Table
1
Table 1
reported
in Table 1.
Table
1
The Results of Unit Root Tests
The Results of Unit Root Tests
1 of Unit Root Tests
TheTable
Results
7
7
7
The Results of Unit Root Tests
Variables
Level
First Difference
ADF
PP
ADF
PP
PET
-0.728
-1.207
-3.631**
-3.627**
FD
-1.472
-1.618
4.567**
-4.524**
GDP
-1.165
-1.379
-4.406**
-4.414**
EP
-3.119
-3.318*
-4.799**
-4.864**
Note: ** and * denote 5% and 10% significance level, respectively. For ADF
test, SIC was used to select the optimal lag length.
The maximum number of lags is 7. For PP test, Barlett Kernel was used as the
spectral estimation method.
Based on Table 1, all variables are statistically significant at first
difference except EP is significant at level in the PP test. Thus, the order
of integration for PET, FD, and GDP are I(1) while EP is I(0). Hence,
36
http://ijms.uum.edu.my
GDP
-1.165
-1.379
-4.406**
-4.414**
EP
-3.119
-3.318*
-4.799**
-4.864**
EP
-3.119
-3.318*
-4.799**
-4.864**
Note:
**
and
*
denote
5%
and
10%
significance
level,
respectively.
For
ADF
Note: ** and * denote 5% and 10% significance level, respectively. For ADF test,
test, SIC
SIC was
was used
used to
to
select
select the
the optimal
optimal lag
lag length.
length.
IJMS
23
(2),
27–44
(2016)
The maximum
maximum number
number of
of lags
lags is
Barlett Kernel
The
is 7.
7. For
For PP
PP test,
test, Barlett
Kernel was
was used
used as
as the
the spectral
spectral estimation
estimation
method.
method.
performing the ARDL bound test for cointegration was relevant as
Based
on
1,
variables
statistically
significant
at
except
our
showed
of I(0)
and I(1)
variables.
Thedifference
results of
the EP
Baseddata
on Table
Table
1, all
alla mixture
variables are
are
statistically
significant
at first
first
difference
except
EP is
is
significant
at
level
in
the
PP
test.
Thus,
the
order
of
integration
for
PET,
FD,
and
GDP
are
significant at level
in the PP
test.are
Thus,
the order of
for PET,
and GDP are I(1)
I(1)
cointegration
bound
tests
presented
in integration
Table 2. The
null FD,
hypothesis
while
EP
is
the
bound
test
was
as
while
EP
is I(0).
I(0). Hence,
Hence,isperforming
performing
the3ARDL
ARDL
bound implying
test for
for cointegration
cointegration
was relevant
relevant
as
of
no
cointegration
rejected
in
equations
the
presence
of bound
our
data
showed
a
mixture
of
I(0)
and
I(1)
variables.
The
results
of
the
cointegration
our data showed a mixture of I(0) and I(1) variables. The results of the cointegration bound
atests
long-run
equilibrium
relationship
in the model.
This indicatesrejected
that
tests are
are presented
presented in
in Table
Table 2.
2. The
The null
null hypothesis
hypothesis of
of no
no cointegration
cointegration is
is rejected in
in 3
3
these
variables
are
tied
together
and
any
deviations
among
them
will
equations
implying
the
presence
of
a
long-run
equilibrium
relationship
in
the
model.
equations implying the presence of a long-run equilibrium relationship in the model. This
This
indicates
that
are
and
deviations
them
be
adjusted
back variables
to the long-run
equilibrium.
PET among
and FD
arewill
indicates
that these
these
variables
are tied
tied together
together
and any
anyWhen
deviations
among
them
will be
be
adjusted
back
to
the
long-run
equilibrium.
When
PET
and
FD
are
served
as
dependent
adjusted as
back
to the long-run
equilibrium.
PET and
FD are served
dependent
served
dependent
variables,
their When
calculated
F-statistics
4.208asand
variables,
their
F-statistics
4.208
and
respectively are
the
variables,
their calculated
calculated
F-statistics
4.208
and 4.320,
4.320,
are beyond
beyond
the upper
upper
4.320,
respectively
are
beyond
the
upper
boundrespectively
critical values
at 10%
bound
critical
values
at
10%
significance
level
(4.150).
Similarly,
cointegration
was
also
bound
critical
values
at
10%
significance
level
(4.150).
Similarly,
cointegration
was
also
significance
level (4.150).
Similarly,
cointegration
was also
found in
found
found in
in the
the EP
EP equation
equation where
where the
the generated
generated F-statistics
F-statistics (5.456)
(5.456) was
was higher
higher than
than the
the upper
upper
the
EP
equation
where
the generated
bound
critical
values
5%
level.
bound
critical
values at
at
5% significance
significance
level. F-statistics (5.456) was higher
than the upper bound critical values at 5% significance level.
Table 2
Table 22
Table
Bound
Bound Test
Test for
for Cointegration
Cointegration
Bound Test for Cointegration
Critical
Critical value
value bounds
bounds of
of the
the F-statistic:
F-statistic: unrestricted
unrestricted intercept
intercept and
and no
no trend
trend
Critical value bounds of the F-statistic: unrestricted intercept and no trend
90%
90% level
level
90% level
95%
95% level
level
95% level
I(0)
I(0)
I(0)
I(1)
I(1)
I(1)
I(0)
I(0)
I(0)
3.008
3.008
3.008
4.150
4.150
4.150
3.710
3.710
3.710
I(1)
I(1)
I(1)
99%
99% level
level
99% level
I(0)
I(0)
I(0)
I(1)
I(1)
I(1)
5.018
5.018
5.333
5.018 5.333
5.3337.063 7.063
7.063
Calculated
Calculated
F-statistic
CalculatedF-statistic
F-statistic
Cointegration
Cointegration
hypothesis
Cointegration
hypothesis
hypothesis
F
F (( PET
PET FD
FD,, GDP
GDP,, EP
EP))
4.208*
4.208*
4.208*
F
F (( FD
FD PET
PET ,, GDP
GDP,, EP
EP))
4.320*
4.320*
4.320*
F
F ((GDP
GDP PET
PET ,, FD
FD,, EP
EP))
2.673
F
F ((EP
EP PET
PET ,, FD
FD,, GDP
GDP))
88
5.456**
5.456**
5.456**
2.673
2.673
Note:
**and * denote
5% and
10% significance
level, respectively.
The are extracted
Note:
Note: **and
**and ** denote
denote 5%
5% and
and 10%
10% significance
significance level,
level, respectively.
respectively. The
The critical
critical values
values are extracted
critical
values
are
extracted
from
Narayan
(2005),
using
Case
III,
number
of
from
Narayan
(2005),
using
Case
III,
number
of
regressors
(k)
is
3,
number
of
observation
from Narayan (2005), using Case III, number of regressors (k) is 3, number of observation (n)
(n) is
is 30.
30.
regressors (k) is 3, number of observation (n) is 30.
Having found that the variables are cointegrated, we investigated
Having
found
the
cointegrated,
we
the
coefficients of
Having
found that
that
the variables
variablesofare
areequation
cointegrated,
we investigated
investigated
the long-run
long-run
the
long-run
coefficients
1 using
the ARDL
(p,q,s,r) coefficients of
equation
1
using
the
ARDL
(p,q,s,r)
specification:
equation 1 using the ARDL (p,q,s,r) specification:
specification:
p
p
q
q
s
s
r
r
PET




PETtt 

 00 

11 PET
PETtt ii 

 22 FD
FDtt ii 

 33GDP
GDPtt ii 

 33 EP
EPtt ii 

tt




i 1
i 0
i 0
i 0
37
i 1
i 0
i 0
i 0
(7)
(7)
The
The lag
lag lengths
lengths p,
p, q,
q, s,
s, rr were
were selected
selected by
by the
the Schwarz
Schwarz Bayesian
Bayesian Criteria
Criteria (SBC).
(SBC). Table
Table 33
from Narayan (2005), using Case III, number of regressors (k) is 3, number of observation (n) is 30.
IJMS that
23 (2),
(2016)
Having found
the 27–44
variables
are cointegrated, we investigated the long-run coefficients of
equation 1 using the ARDL (p,q,s,r) specification:
p
q
s
r
i 1
i 0
i 0
i 0
PETt  
0    1 PETt i    2 FDt i    3 GDPt i    3 EPt i   t
(7)(7)
http://ijms.uum.edu.my
The lag lengths p, q, s, r were selected by the Schwarz Bayesian Criteria (SBC). Table 3
The the
lag lengths
q, s,long-run
r were selected
by the
Bayesian
Criteria
summarizes
results ofp, the
elasticities.
The Schwarz
results reveal
that the
effect of
Table on
3 summarizes
the long-run
elasticities.
financial(SBC).
development
energy demandthe
wasresults
positiveofwhich
is consistent
with previous
studies such
Sadorsky
(2010,
2011),
Shahbaz
Lean (2012)
and Islam on
et al.
(2013). A
The as
results
reveal
that
the effect
ofand
financial
development
energy
10 per cent
increase
in domestic
to is
theconsistent
private sector
is anticipated
to raise petroleum
demand
was
positive credit
which
with
previous studies
such
consumption
by 0.02 per
cent, ceteris
Thisand
implies
development
in theetfinancial
as Sadorsky
(2010,
2011), paribus.
Shahbaz
Leanthat
(2012)
and Islam
al.
market (2013).
enables A
businesses
and increase
consumersintodomestic
have better
access
to private
credit facilities
10 per cent
credit
to the
sector for
investment and buying goods and services such as automobiles and home appliances that add
is anticipated to raise petroleum consumption by 0.02 per cent, ceteris
to energy use. The coefficient of economic growth is 1.053 and it is significant at 5% level. A
paribus. This implies that development in the financial market enables
10 per cent rise in economic growth stimulates petroleum use by 10.53 per cent, ceteris
to have
better access
to output
credit and
facilities
for the
paribus. businesses
This mirrorsand
thatconsumers
economic growth
enhances
industrial
improves
investment
and
buying
goods
and
services
such
as
automobiles
and
standard of living that lead to a greater demand for energy. This confirms earlier findings
appliances
that Shazbaz
add to energy
use.
The coefficient
of economic
reportedhome
in Islam
et al. (2013),
and Lean
(2012),
Halicioglu (2007),
Odhiambo
growth and
is 1.053
it isAng
significant
at 5% and
level.
A 10(2005),
per cent
rise(2002),
in
(2009), Bowden
Payneand
(2009),
(2008), Altinay
Karagol
Ghosh
and Aqeel
and Buttgrowth
(2001) which
claimedpetroleum
that economic
hasper
a positive
impact on
economic
stimulates
use growth
by 10.53
cent, ceteris
energy use.
The diagnostic
tests show
that economic
the model is growth
well specified
as the Ramsey
RESET
paribus.
This mirrors
that
enhances
industrial
test failed
to
reject
the
null
hypothesis
of
no
specification
error.
Besides
that,
the
residuals
output and improves the standard of living that lead to a greater
were notdemand
serially correlated
and homoscedastic.
for energy.
This confirms earlier findings reported in Islam
et al. (2013), Shazbaz and Lean (2012), Halicioglu (2007), Odhiambo
Table 3 (2009), Bowden and Payne (2009), Ang (2008), Altinay and Karagol
(2005), Ghosh (2002), and Aqeel and Butt (2001) which claimed that
economic
growthEstimates
has a positive impact on energy use. The diagnostic
ARDL (2,
1, 0, 2) Long-run
tests show that the model is well specified as the Ramsey RESET test
Dependent
variable:
failed
to PET
reject
the null hypothesis of no specification error. Besides
t
Regressorthat, the residuals
Coefficients
t-statistics and homoscedastic.
p-values
were not serially correlated
FDt
0.002***
GDPt Table 3
1.053**
EPt
-0.697
Constant
-19.079
ARDL (2, 1, 0, 2) Long-run Estimates
R-Bar-Square = 0.995
DW-Statistic = 2.354
Dependent
variable: PETt
S.E. of Regression
= 0.026
Regressor
FDt
GDPt
EPt
Constant
R-Bar-Square = 0.995
DW-Statistic = 2.354
S.E. of Regression = 0.026
5.204
3.752
1.128
-4.107
Coefficients
0.002***
9
1.053**
-0.697
-19.079
0.000
0.001
0.273
0.001
t-statistics
5.204
3.752
1.128
-4.107
p-values
0.000
0.001
0.273
0.001
(continued)
38
http://ijms.uum.edu.my
IJMS 23 (2), 27–44 (2016)
Dependent variable: PETt
Regressor
Diagnostic Test
t-statistics
Coefficients
p-values
Serial Correlation: F (1,18) =1.3430 [.262]
Functional Form: F (1,18) = 2.8155 [.111]
Heteroscedasticity: F (1,26) = 0.15414 [.698]
Note: *** and ** denote 1% and 5% significance level, respectively.
To ensure that our results are robust, we performed the CUSUM and
the CUSUM square stability tests as shown in Figure 1a and Figure
1b, respectively. The plots of CUSUM and CUSUMSQ for the model
lie within the critical bounds of 5% significance level. This indicates
that the coefficients in the model are stable and free from structural
break over the sample period.
15
10
15
5
10
0
5
-5
0
-10
-5
-10
-15
1983
1988
1993
1998
2003
2010
2008
The straight lines represent critical bounds at 5% significance level
-15
1983
1988
1993
1998
2003
2010
2008
The straight lines represent critical bounds at 5% significance level
Figure 1a. Plot of CUSUM test.
Figure 1a. Plot of CUSUM test.
Figure 1a. Plot of CUSUM test.
1.5
1.5
1.0
0.5
0.0
1.0
0.5
0.0
-0.5
1983
-0.5
1983
1988
1993
1998
2003
The straight lines represent critical bounds at 5% significance level
1988
1993
1998
2003
Figure 1b. Plot of CUSUM Square Test.
2010
2008
2008
The straight lines represent
critical
bounds
significance
level
Figure
1b.
Plot atof5%
CUSUM
Square
Figure 1b. Plot of CUSUM Square Test.
2010
Test.
39
http://ijms.uum.edu.my
claimed that the direction of causation between economic growth and energy consumption
were bidirectional which was not found in our study. Our results differed from Islam et al
(2013) probably due to several reasons. First, we used the non-renewable energy consumption,
i.e. 23
petroleum
consumption.
Second, we employed the Toda-Yamamoto (1995) approach,
IJMS
(2), 27–44
(2016)
which is considered as superior in small sample size, to run the Granger-causality test instead
of the conventional VECM (Zapata & Rambaldi, 1997).
Table 5
Table 5
Toda-Yamamoto
non
Granger-causality Test
Test (  2 statistics)
Toda-Yamamoto
non
Granger-causality
statistics)
Effect
Effect
PETt PETt
PETt
FDt
-
PETt
-
Cause
FDt
3.778* (0.052)
0.155 (0.694)
1.474 (0.225)
FDt GDPt0.155 (0.694)
EPt
FDt
GDPt
3.778* (0.052)
2.228 (0.136)
-
Cause
GDPt
7.629**EP
(0.006)
t
7.629** (0.006)
0.298 (0.585)
0.022 (0.881)
1.960 (0.162)
2.227 (0.136)
0.022 (0.881)
EPt
2.227 (0.136)
2.333 (0.127)
2.333 (0.127) 0.073 (0.788)
0.559 (0.455)
-
(0.225)
0.298
(0.585)
0.073 in
(0.788)
GDP
**t and1.474
* denote
significance
level
of 5% and 10%,
respectively. Figures
parenthesis are p-values.
2.228 (0.136)
1.960 (0.162)
0.559 (0.455)
EPt
Conclusion
** and * denote significance level of 5%
and 10%, respectively. Figures in
parenthesis are p-values.
The nexus between energy consumption and economic growth has been discussed
substantially over the past decades. This paper attempts to add knowledge to the existing
literature by examining the dynamic
association among petroleum consumption, financial
Conclusion
development, economic growth and energy price. The sample of this study was based on the
Malaysian annual data from 1980 to 2010. In view of the finite sample size, this study applied
Thethenexus
between
consumption
economic
growthcoefficients of
ARDL bound
testingenergy
to examine
the presence ofand
cointegration
and long-run
hasvariables
been discussed
substantially
over
the
past
decades.
This
paper
in the multivariate framework. The results of the ARDL bound
testing confirmed
attempts
add knowledge
to the
existingindicating
literature
examining
that the to
underlying
variables were
cointegrated,
thatbythese
variables were moving
in the
long run. From
the aspect
of long-run
relations, we financial
found out that financial
thetogether
dynamic
association
among
petroleum
consumption,
development and
economicgrowth
growth and
positively
affect
petroleum
development,
economic
energy
price.
The consumption
sample of in Malaysia.
the financial
provides annual
a platform
for from
consumers
thisDevelopment
study wasinbased
on thesector
Malaysian
data
1980and
to businesses to
access low-cost credit facilities to purchase motor vehicles which would lead to a greater
2010. In view of the finite sample size, this study applied the ARDL
demand for petroleum consumption. At the same time, increases in economic growth improve
bound
testingoftoliving
examine
the presence
long-run
the standard
and purchasing
poweroftocointegration
purchase motor and
vehicles
that cause petroleum
coefficients
of
variables
in
the
multivariate
framework.
The
results
of
consumption to escalate.
the ARDL bound testing confirmed that the underlying variables were
cointegrated, indicating that these variables were moving together
Besides
Toda-Yamamoto
Granger-causality
testweindicated
that there was
in the
longthat,
run. the
From
the aspect of long-run
relations,
found out
unidirectional
Granger-causality
running
from
financial
development
and
economic
growth to
that financial development and economic growth positively affect
petroleum consumption in the long run. This suggests that Malaysia is not a petroleum
petroleum consumption in Malaysia. Development in the financial
dependent country. Hence, the government could implement petrol conservation policies to
sector
provides
platformand
forreplace
consumers
and non-petroleum
businesses to
access
reduce
the use ofapetroleum
it with other
based
fuel. On the other
low-cost
credit
facilities
to
purchase
motor
vehicles
which
would
lead consumption
hand, given that financial development and economic growth increase petroleum
to a greater demand for petroleum consumption.
At the same time,
11
increases in economic growth improve the standard of living and
purchasing power to purchase motor vehicles that cause petroleum
consumption to escalate.
Besides that, the Toda-Yamamoto Granger-causality test indicated that
there was unidirectional Granger-causality running from financial
40
http://ijms.uum.edu.my
IJMS 23 (2), 27–44 (2016)
development and economic growth to petroleum consumption in the
long run. This suggests that Malaysia is not a petroleum dependent
country. Hence, the government could implement petrol conservation
policies to reduce the use of petroleum and replace it with other
non-petroleum based fuel. On the other hand, given that financial
development and economic growth increase petroleum consumption
in Malaysia, policies pertaining to petroleum consumption should
incorporate the development of the financial sector and the economic
growth of the country. The policy implication of this study is that
it initiates policy-makers’ interest to formulate strategies to increase
petroleum production and inventory. This can be probably achieved
through discovering new oil fields or negotiating new long-term
supply contracts to support the rising demand for petroleum. At
the same time, the government could introduce petrol conservation
policies through financial development that promotes efficient
petroleum use by investing in new petrol-saving technology.
End Notes
For instance, the electricity consumption-growth nexus in Malaysia
(Chandran et al., 2010; Lean & Smyth, 2010b; Tang, 2008); in Korea
(Yoo, 2005); in China (Yuan et al., 2007); in Africa (Wolde-Rufael,
2006); in ASEAN (Yoo, 2006); and in OPEC (Squalli, 2006).
1
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