1378-1383 - International Journal of Management and Humanity

International Journal of Management and Humanity Sciences. Vol., 3 (2), 1378-1383, 2014
Available online at http://www.ijmhsjournal.com
ISSN 2322-424X©2014
Relation between Petroleum taking and economic development ARDL
Testing – Iran case study
Mohsen Talebzadeh Kasgeri*, Hossein Etesami, and Masoud Alizadeh Chemazketi
Master of Theoretical Economic, Islamic Azad University, Firouzkoh, Tehran-Iran
*Corresponding author E-mail: [email protected]
Petroleum or Oil is very consequential and significant for economical growth in Iran.
The present article investigates the causal relation between economic growth and oil
consumption for Iran employing co-integration and error correction model from
annual data encompassing the years of 1980-2010. As oil consumption and economic
growth variables employed in empirical analysis was integrated of order one,
employed Granger causality test. The results indicate that the Granger causality runs
from economic growth to energy consumption in the short-run, in Iran. In the long
run, however, there is not any Granger causality relationship for this country. In other
words, if unidirectional causality runs from energy consumption to income, reducing
energy consumption could lead to a decrease or fall in economic growth.
Key Words: ARDL, Abosedra, Oil Consumption, Baghestani, Co-integration.
Oil now comprises an important factor in supporting the well-being of Iran’s as well as the economic
growth of the nation. Therefore, oil supply side measures are needed in conformity with economical growth.
Production in industries such as manufacturing, transportation, and electricity generation requires a
considerable quantity of oil. Demand side management measures are also necessary in addition to supply
side measures. The oil intensity in Iran is much larger than those in the developing countries. High oil
intensity in Iran reflects inefficient oil consumption in industries and agriculture sector which indicates that
there are high oil-saving capabilities and potentials. Therefore, improving oil consumption efficiency of
automobiles and machines and introducing different types of tariff reforms intending to control oil supply and
use models through leveling projected oil demand and saving supply costs of oil can induce a high extent of
efficiency in the present facilities without unfavorably affecting a high level of oil use for economic growth.
The direction of causality between energy consumption and economic growth has consequential policy
implications for countries, enjoying implicit generous subsidies (low domestic prices) for energy. The
literature related to the relation between economic growth and energy consumption has directed to the
revelation of two different and opposite views. One perspective proposes that energy consumption is a
limiting factor for economic growth. The other perspective mentions that energy is impartial and neutral to
growth. This is known in the literature as the neutrality assumption which suggests that the energy cost is a
small proportion of ‘GROSS DOMESTIC PRODUCT’ or GDP, and so it should not have a significant effect
on output growth. It has also been argued that the possible impact of energy consumption on growth would
depend on the structure of the economy and the phase of economic growth of the country related. As the
economy grows its production structure is likely to shift towards services, which are not energy-intensive
activities (Asafu - Adjaye, J., 2000, Solow, 1978; Cheng, 1995). There are a large number of articles
investigating the empirical relations between economic growth and energy use. One on the categories these
studies in to four main approaches: One approach is based on a traditional VAR (Sims, 1972) and Granger's
causality testing, which assumed that the data are stationary (Erol and Yu, 1987; Abosedra and Baghestani,
1989). The other two approaches are presuming that the variables are non-stationary and as a result, the cointegration technique is the proper tool for examining these relationships (Asafu-Adjaye, J., 2000). Another
approach is, based on the Granger (1988) two stage procedure; in this approach the variables are tested in
pairs by co-integrating relationships and error correction models to test for Granger causality (Glasure and
Lee, 1997). The third approach is based on multivariate estimators (Johansen, 1990), which facilitated
estimations of systems of equation where restrictions on co-integrating relations can be tested and
Intl. J. Manag. Human. Sci. Vol., 3 (2), 1378-1383, 2014
information on short-run adjustment are investigated. The multivariate approach also allows for more than
two variables in the cointegration relationship (see, e.g. Masih and Masih, 1998; Asafu-Adjaye, 2000). The
last and fourth approach utilizing the Panel-based error correction models, which providing more powerful
tests compared to the time series approach. In some of the literature the focus is on the relationship between
energy consumption and economic growth. However, when it comes to whether energy consumption in the
result or a prerequisite for, economic growth, one cannot find clear trends in the literature. Depending on the
methodology employed, and the country as well as time period studied, the direction of causality is indistinct
and controversial (Asafu-Adjaye, J., 2000). In this paper, we intend to examine the relationship between oil
consumption and economic growth for Iran, according to Odhiambo. M. N., (2010) article.
The purpose of this paper is, thus, to study the causality between economic growth and oil consumption,
and to obtain policy implications from the results. The paper is arranged in the following fashion. Section 2,
defines the econometric methodology. Section 3 presents data and empirical study. Final section contains
the conclusions.
Econometric Methodology Co-integration – ARDL-Bounds Testing Procedure
In this regard, by applying the model suggested by Odhiambo, 2010 the recently developed
Autoregressive Distributed Lag (ARDL)-Bounds testing approach is used to examine the long-run
relationship between oil consumption and economic growth. The ARDL modelling approach was originally
introduced by Pesaran and Shin (1999) and later extended by Pesaran et al. (2001).
= log of oil consumption;
/ = the log of real per capita income; ? = white noise
error term; Δ = first difference operator. The bounds testing procedure is based on the joint F-statistic (or
Wald statistic) for co-integration analysis. The asymptotic distribution of the F- statistic is non-standard under
the null hypothesis of no co-integration between examined variables. Pesaran and Pesaran (1997) and
Pesaran et al. (2001) report two sets of critical values for a given significance level. One set of critical values
assumes that all variables included in the ARDL model are I(0), while the other is calculated on the
assumption that the variables are I(1). If the computed test statistic exceeds the upper critical bounds value,
then the Ho hypothesis is rejected. If the F-statistic falls into the bounds then the co-integration test becomes
inconclusive. If the F-statistic is lower than the lower bounds value, then the null hypothesis of no cointegration cannot be rejected (Odhiambo, 2010). Granger Non-Causality Test The existence of cointegration relationships indicates that there are long-run relationships among the variables, and thereby
Granger causality among them in at least one direction. The ECM was introduced by Sargan (1964), and
later popularized by Engle and Granger (1981). It is used for correcting disequilibrium and testing for long
and short run causality among cointegrated variables. The ECM used in this paper is specified as follows:
Although the existence of a long-run relationship between OILCON and y/N suggests that there must be
Granger-causality in at least one direction, it does not indicate the direction of temporal causality between
the variables. The direction of the causality in this case can only be determined by the F-statistic and the
lagged error-correction term. It should, however, be noted that even though the error-correction term has
been incorporated in all the equations (3) – (4), only equations where the null hypothesis of no co-integration
is rejected will be estimated with an error-correction term (Odhiambo, 2010). In each equation, change in the
endogenous variable is caused not only by their lags, but also by the previous period‟s disequilibrium in
level. Given such a specification, the presence of short and long-run causality could be tested (Aktaş, Cengiz
and Y?lmaz, Veysel., 2008). ADF Unit Root Test Nelson and Plosser (1982) argue that almost all
macroeconomic time series typically have a unit root. Thus, by taking first differences the null hypothesis of
non-stationary is rejected for most of the variables. Unit root tests are important in examining the stationary
of a time series because non-stationary regressors invalidates many standard empirical results and thus
requires special treatment. Granger and Newbold (1974) have found by simulation that the F-statistic
Intl. J. Manag. Human. Sci. Vol., 3 (2), 1378-1383, 2014
calculated from the regression involving the non-stationary time-series data does not follow the Standard
distribution. This nonstandard distribution has a substantial rightward shift under the null hypothesis of no
causality. Thus the significance of the test is overstated and a spurious result is obtained. The presence of a
stochastic trend is determined by testing the presence of unit roots in time series data. Non-stationary or the
presence of a unit root can be tested using the Dickey and Fuller (1981) tests. The test is the t statistic on φ
in the following regression:
Where is the first-difference operator, is a stationary random error (Chang, at all, 2001). Tests of Co
integration The co integration test is based in the methodology developed by Johansen (1991), and
Johansen and Juselius (1993). Johansen's method is to test the restrictions imposed by co-integration on the
unrestricted variance autoregressive, VAR, involving the series. The mathematical form of a VAR is
= 1 −1+⋯+
− +
is an n-vector of non-stationary I(1) variables,
is a d-vector of deterministic variables, 1,..,
are matrices of coefficients to be estimated, and
is a vector of innovations that may be
contemporaneously correlated with each other but are uncorrelated with their own lagged values and other
right-hand side variables. We can rewrite the VAR as (Eq. (7)):
Granger's representation theorem asserts that if the coefficient matrix n has reduced rank r<n, then there
exist n x r matrices and each with rank r such that =
′ and ′ is stationary. Here, r is the number
of cointegrating relations and each column of is a co-integrating vector. For n endogenous non-stationary
variables, there can be from (0) to (n-1) linearly independent, co-integrating relations (Yin and Xu, 2003;
Aktaş, Cengiz and Yılmaz, Veysel, 2008).
Data and Empirical Results Data
The data used in this study consist of annual time series of GDP and oil consumption for Iran 1980 to
2010. Annual time series data were utilized in this study. The series for Iran cover the period 1980-2010; the
data are obtained from BP Statistical Review2011and the Titi Tudorancea Bulletin. GDP: Gross Domestic
Product (1.000.000$), OIL: Oil Consumption (Thousand Barrels Per Day). Figure 1 and 2, respectively,
describes oil consumption and GDP over the period of 1980-2010.
Result of Unit Roots and Cointegration Test
The results of the unit root tests for the series of Oil consumption and GDP variables are shown in Table 1
the ADF test provides the formal test for unit roots in this study. The p-values corresponding to the ADF
values calculated for the two series are larger than 0.05. This indicates that the series of all the variables are
non-stationary at 5% level of significance and thus any causal inferences from the two series in levels are
Table 1. Result of ADF Test for Unit Roots
Trend and Intercept First difference Critical values (5%)
Note: The optimal lags for the ADF tests were selected based on optimising Akaike‟s information Criteria
AIC, using a range of lags. We use the Eviews soft ware to estimate this value. Source: BP Statistical
Review2011and the Titi Tudorancea Bulletin.
The analysis of the first differenced variables shows that the ADF test statistics for all the variables are
less than the critical values at 5% levels (Table 1). The results show that all the variables are stationary after
differencing once, suggesting that all the variables are integrated of order I(1). As indicated, the basic idea
behind co-integration is to test whether a linear combination of two individually non-stationary time series is
itself stationary. Given that integration of two series is of the same order, it is necessary to test whether the
two series are co-integrated over the sample period. The results of the Johansen co-integration test for the
series OILCON and GDP are reported in Table 2.
Intl. J. Manag. Human. Sci. Vol., 3 (2), 1378-1383, 2014
Table 2. Results of Johansen's Cointegration Test
Null Hypotheses
Alternative Hypotheses
Critical Value (5%)
r 1
Source: BP statistical Review 2011 and the Titi Tudorancea Bulletin, we use Eviews soft ware to estimate
this value.
Figure 1. Oil Consumption in Iran
The likelihood ratio tests show that the null hypothesis of absence of co-integrating relation (r = 0) cannot
be rejected at 5% level of significance. Thus, we can conclude that oil consumption and GDP are not cointegrated in the long run.
Results of Error - Correction Model
If the series of two variables are non-stationary and the linear combination of these two variables is
stationary, then the error correction modeling rather than the standard Granger causality test should be
employed. Therefore, an ECM was set up to investigate both short-run and long-run causality. In the ECM,
first difference of each endogenous variable (GDP and OILCON) was regressed on a period lag of the cointegrating equation and lagged first differences of all the endogenous variables in the system, as shown in
Eq. (3). The results of error correction model are presented in Table 4.
Intl. J. Manag. Human. Sci. Vol., 3 (2), 1378-1383, 2014
Figure 2. GDP in Iran
Table 3. The Results of Error Correction Model
Lag Lengths
F Statistics
T Statistics foe ECMt-1
∆ GDP - ∆ OIL
∆ OIL - ∆ GDP
Notes: The lag Lengths are chosen by using AIC information criterion. *Denotes the rejection of the null
hypothesis at 5% lenel of significance.
According to results of the Table 3, short-run causality is found to run from economic growth to oil
consumption. That is, there is directional short-run Granger-causality economic growth to oil consumption.
The coefficient of the ECM is not be significant in Eq. (3) and (4), which indicates that no exists bidirectional
Granger causality between oil consumption and economic growth in long run. In other words, if unidirectional
causality runs from energy consumption to income, reducing energy consumption could lead to a fall in
economic growth.
The present article has investigated the ECM model to investigate the causal relationship between oil
consumption and GDP in Iran by employing the annual data covering the time duration of 1980- 2010.
Before conducting the casualty test, the ADF test and Johansen maximum likelihood test were employed to
examine the unit roots and co-integration. Our assessment and estimation results indicate in short run that
there are bidirectional short-run causality between oil consumption and economic growth. Results confirm
that both direct and indirect Granger causality do not show a long run impact of oil consumption on economic
growth. This means, our research indicates that energy consumption does not lead to economic growth and
therefore sizeable energy consumption is not likely to bring about important economic growth except an
increase in pollution. It is very important for this country to adopt appropriate energy policy to further the
economic growth. Since Iran has a high oil exports, efficient use of oil and substituting of gas and technology
for oil can cover good policy measures.
Abbasian E, Nazari M, Nasrindoost M, 2010. Energy consumption and Economic growth in the Iranian
economy: Testing the causility relationship. Middle-east journal of scientifice reserch 5(5) 374,381
Cheng BS, 1997. Energy consumption and economic growth in Brazil, Mexico and Venezuela: a time series
analysis. Applied Economics Letters 4, 671–674
Chen ST, Kuo HI, Chen C, 2007. The relationship between GDP and electricity consumption in 10 Asian
Countries. Energy Policy, 35, 2611-2621
Dunkerley J, 1982. Estimating energy demand: the developing countries. Energy Journal 23, 79–99, Energy
balance sheet of Iranian economy, department of energy. 2007
Engle RF, Granger CJ, 1987. Cointegration and error-correction-representation, estimation and
testing.Econometrica 55, 251–278.
Glasure YU, 2002. Energy and national income in Korea: further evidence on the role of omitted variables.
Energy Economics 24, 355–365
Hatemi JA, Irandoust M, 2005. Energy consumption and economic growth in Sweden: a leveraged bootstrap
approach (1965–2000). International Journal of Applied Econometrics and Quantitative Studies 2,
Johansen S, Juselius K, 1990. Maximum likelihood estimation and inference on cointegration with
applications to the demand for money. Oxford Bulletin of Economics and Statistics 52, 169–210.
Johansen S, 1996, Likelihood-Based Inference in Cointegrated Vector Auto-Regressive Models 2nd edn.
Oxford university press.
Lee CC, 2005. Energy consumption and GDP in developing countries: A cointegrated panel analysis. Energy
Economics, 27, 415-427
Masih AMM, Masih R, 1996. Electricity consumption, real income and temporal causality: results from a
multi-country study based on cointegration and error correction modeling techniques. Energy
Economics 18, 165–183
Intl. J. Manag. Human. Sci. Vol., 3 (2), 1378-1383, 2014
Mehrara M, 2007. Energy–GDP relationship for oil-exporting countries: Iran, Kuwait and Saudi Arabia.
Organization of the petroleum exporting countries
Mozumder P, Marathe A, 2007. Causality relationship between electricity consumption and GDP in
Bangladesh. Energy Policy 35, 395–402
Narayan PK, 2005. The saving and investment nexus for China: evidence from cointegration tests. Applied
Economics 37, 1979–1990.
Narayan PK, Singh B, 2007. The electricity consumption and GDP nexus for Fiji Islands. Energy Economics
29, 1141–1150.
Narayan PK, Smyth R, 2005. Electricity consumption, employment and real income in Australia: evidence
from multivariate Granger causality tests. Energy Policy 33, 1109–1116.
Ng S, Perron P, 2001. Lag length selection and the construction of unit root tests with good size and
power.Econometrica 69, 1519–1554
Pesaran M, Shin Y, 1999. An autoregressive distributed lag modeling approach to cointegration analysis. In:
Strom, S. (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch
centennial Symposium. Cambridge University Press, Cambridge.
Pesaran M, Shin, Y, Smith R, 2001. Bounds testing approaches to the analysis of level relationships. Journal
of Applied Econometrics 16, 289–326.
Phillips P, Hansen B, 1990. Statistical inference in instrumental variables regression with I(1) process,
Review of Economic Studies, 57, 99-125
Odhiambo N, 2010. Energy consumption, prices and economic growth in three SSA countries: A
comparative study. Energy Policy 38, 2463–2469
Odhiambo N, 2009. Energy consumption and economic growth nexus in Tanzania: An ARDL bounds testing
approach. Energy Policy 37, 617– 622
Odhiambo NM, 2009b. Electricity consumption and economic growth in South Africa: a trivariate causality
test. Energy Economics 31, 635–640
Shiu A, Lam PL, 2004. Electricity consumption and economic growth in China. Energy Policy 32, 47–54