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 1 2 3 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] Abstract 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. Introduction 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). Where: = 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 1379 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+⋯+ − + + (6) Where is an n-vector of non-stationary I(1) variables, is a d-vector of deterministic variables, 1,.., and 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 invalid. Variables LOILCON LGDP Table 1. Result of ADF Test for Unit Roots Trend and Intercept First difference Critical values (5%) -2.51 -5.72 -3.63 -3.64 -1.97 -4.27 -3.57 -3.58 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. 1380 Intl. J. Manag. Human. Sci. Vol., 3 (2), 1378-1383, 2014 Table 2. Results of Johansen's Cointegration Test Null Hypotheses Alternative Hypotheses Trace Critical Value (5%) H0 H=1 r=0 r=1 9.40 15.49 r=1 3.58 3.84 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. 1381 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 m=2 n=2 4.74 -2.90 ∆ OIL - ∆ GDP m=1 n=1 1.43 1.36 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. Conclusion 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. 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