Dynamic Relationship between Energy Consumption and Economic Growth: Evidence from Pakistan

Transcription

1 Journal of Empirical Economics Vol. 3, No. 1, 2014, Dynamic Relationship between Energy Consumption and Economic Growth: Evidence from Pakistan Muhammad Imran Khan 1, Muhammad Waqas 2 Abstract This study investigated the dynamic link between energy consumption and economic growth by using time series data of Pakistan from Augmented Granger Causality test depicted unidirectional causality running from economic growth to energy consumption. Petroleum consumption granger cause economic growth. There was unidirectional causality between gas consumption and economic growth. The study found that GDP granger cause electricity consumption. Engle-Granger test was applied for aggregate analysis of energy consumption and economic growth which showed long run relationship between the variables. ARDL co-integration approach was used for disaggregate analysis which revealed only short run relationship between gas consumption and economic growth. There was only long run relationship between electricity consumption and economic growth. Keywords: Energy Consumption, Economic Growth, Pakistan 1. Introduction No one can imagine growth without energy. Whether it is growth of an organism or growth of an economy, availability of energy is considered necessary for it. In the present era of resource politics, policy makers of grown as well as growing economies are well aware of the importance of energy. The debate of energy consumption and economic growth got immense importance after rapid increase in world oil prices, due to Middle East oil embargo of Those oil prices shock caused negative real supply side shock in most of the oil importing countries, which in turn raised inflation and decreased economic growth in these countries (Riaz, 1984). Being an under developing country, Pakistan energy crisis is serious concern in the way of economic development. Energy crisis is not a single issue; rather it is negatively targeting all the sectors of economy. Energy shortage reduces efficiency of all sectors which in turn decrease output in the economy. The objective of current study is to explore the relationship among energy consumption and economic growth in Pakistan by using the annual data from The study utilized the aggregate and disaggregate analysis and used sophisticated econometric techniques for analysis. The rest study is followed as; part two presents the review of literature, part three explains the data and methodology, part four investigates the findings and last part recommends policy options to control this problem. 2. Literature Review Asafu-Adjaye (2000) explored that there exist relationship between energy consumption and income in the economies of Thailand, Philippines, Indonesia and India. The study utilized the time series data of India and Indonesia for the period of , and the time series data of Thailand and Philippines for the period 1 The University of Lahore, Sargodha Campus, Pakistan 2 The University of Lahore, Sargodha Campus, Pakistan 2014 Research Academy of Social Sciences 31

2 M. I. Khan & M. Waqas of To find multiple co-integrating relationships between variables, the study applied Johansen s maximum likelihood test and Granger causality test was applied to find causal relationship between variables. The research analysis concluded that there was unidirectional Granger causality running from energy to income in Indonesia and India in the short run while there was bidirectional Granger causality running from energy to income in the economies of Philippines and Thailand in the short run. Mallick (2007) investigated annual data of various components of energy consumption and GDP from Indian economy, for the period of 1970 to By applying various econometrics procedures like; Granger causality test and Vector Autoregressive model the research found bidirectional causal link between economic growth and electricity consumption. There was unidirectional causality running from economic growth to gas consumption. Similarly, there was unidirectional causal link between coal consumption and GDP in which former caused the latter. While there was unidirectional causality running from economic growth to aggregate energy consumption. Salim et al. (2008) explored relationship between energy consumption and national output in the 6 Non- OECD under developing Asian economies, (i.e., China, Malaysia, Thailand, Pakistan, Bangladesh and India). These countries consume more than 80 percent of the total energy consumed in this region. The study used time series data of these economies for energy consumption, GDP and consumer price index from various institutions like, Energy Information Administration, International Financial Statistics and IMF, for the time period of 1980 to As the data was non-stationary, and was comprised on more than two variables therefore Johansen (1988) and Johansen and Juselius (1990) maximum likelihood tests were applied to find co-integration in variables. In order to find the effect of a unit change in residual on dependent variable, methods of generalized impulse response and generalized variance decompositions developed by Koop et al. and Pesaran and Shin were utilized. To check long run and short run causality, the study applied Error Correction Mechanism. It was concluded that there was long run and short run unidirectional causality running from income to energy consumption in case of Thailand. There was only short run causality running from income to energy consumption in case of China. There was bidirectional causality between output and energy consumption both in the long run and short run in case of Malaysia. In case of Pakistan and India they found that, there was unidirectional link between energy and income in which former cause the latter, in both long run and short run. While in case of Bangladesh, neither long run nor short run causality was found between energy consumption and output. Yuan et al. (2008) utilized neo-classical aggregate production model in order to find causal relationship between energy consumption and economic growth. The study focused both energy consumption as a whole, (i.e., at aggregate level) and component parts of energy, (i.e., coal, oil and electricity consumption) at disaggregate level. The analysis used data of these variables from the economy of China for the period of On the basis of Johansen co-integration test, the study found long run co-integration among all variables in China at aggregate as well as at disaggregates level. While on the basis of Granger causality test it was concluded that there was bidirectional relationship between GDP and total energy in the short run. Loganathan et al. (2010) were investigating dynamic relationship between energy consumption and economic growth in Malaysian economy. Data for the period of 1971 to 2008 was used in this analysis. Augmented Dickey Fuller and Phillips- Perron tests showed that both the series were stationary at difference one.akaike Information Criteria and Schwartz Bayesian Criteria were utilized to find lag length between variables in the study. By applying Dynamic Ordinary Least Square estimator and Engle-Granger causality method the study concluded that, there was long run relationship between energy consumption and economic performance. In order to find short run causality between energy consumption and GDP, Error Correction Method was used. The study utilized CUSUM and CUSUMQ tests to check stability of parameters. On the basis of Autoregressive Distributed Lag bound test procedure it was concluded that, there was bidirectional causality between economic performance and energy consumption in Malaysia. Razzaqi et al. (2011) contributed a research to find causal relationship between energy consumption and economic growth in D8 countries, (i.e., Turkey, Pakistan, Nigeria, Malaysia, Iran, Indonesia, Egypt and Bangladesh). Annual data of these economies for the period of was used in this study. By 32

3 International Journal of Management Sciences applying various econometrics techniques like, Vector Error Correction Model, Vector Autoregressive Granger causality test and Johansen Co-integration test, the study found that there was long run and short run causal relationship between energy consumption and GDP in all these countries. There was bidirectional causal relationship between energy consumption and economic growth in the long run, in case of Pakistan, Bangladesh, Iran, Malaysia and Turkey. In case of Egypt, there was unidirectional Granger causality running from economic growth to energy in both long run and short run. There was unidirectional causality running from energy consumption to GDP in the short run, in case of Iran. Nigerian economy was reflecting unidirectional causal relationship running from energy consumption to economic growth in the long run. In case of Indonesia, there was no causal relationship between GDP and economic growth in the short run while there was bidirectional causality between the two variables in the long run. Aziz (2011) find out that there was a long run relationship between energy consumption and economic growth in Malaysia. Annual data of these variables for the period of was used in this research. Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) tests were applied to check stationarity of the data. In order to find out long run relationship between energy consumption and economic growth the study used Johansen test. Farhani and Rejeb (2012) utilized panel data of 95 countries for the time period of 1971 to 2008 in order to find relationship between economic growth and energy consumption. Out of these 95 countries; 16 were low income, 27 were lower middle income, 18 were upper middle income and 34 were high income countries as mentioned by World Bank in October The methodology which was applied step by step in this research was summary statistics, panel unit root test, panel co-integration test, OLS, FMOLS and DOL. The study concluded that there was bidirectional Granger causality running from GDP to energy consumption in upper middle and lower middle income group of countries, while there was long run Granger causality between GDP and energy consumption in high and low income group of countries. Hossain (2012) carried out a multivariate Granger causality analysis in order to find relationship among economic growth, electricity consumption, exports and remittance; in the three economies of SAARC region (i.e.; Pakistan, Bangladesh and India). Annual panel data of these countries on the above mentioned variables for the period of was used in this analysis. After applying four different unit root tests,the study concluded that all the variables were integrated of order 1. According to ADF test economic growth and electricity consumption variables were integrated of order 2 in Pakistan while there was integration of order 1 in all variables for Bangladesh and India. Kao and Johansen tests of integration also provided the evidence that variables were integrated. Shaari et al. (2012) find out that there was a causal relationship between consumption of energy and economic growth. The study observed time series data of Malaysian economy for the period of Johansen co-integration and Granger causality tests on the data of GDP and different sources of energy like oil, gas, coal, and electricity consumption were applied in this research. Through Augmented dickey-fuller test it was concluded that all the variables were stationary at difference one. There was no causality between oil and GDP. There was no causality between coal and GDP. The study found that there was unidirectional causality running from GDP to electricity consumption. There was unidirectional causality running from gas to GDP. It was also concluded that there was long run relationship between energy and economic growth in Malaysian economy. Muhammad et al. (2012) carried out multivariate analysis of the economy of China in order to find causality among consumption of energy, economic growth, trade and financial development. The study utilized data of above mentioned variables for the period of 1971 to By applying Augmented Dickey Fuller and Phillips-Perron unit root tests, the analysis found that all the variables were stationary at difference one. It is a drawback of Augmented Dickey Fuller and Phillips-Perron unit root tests that it did not depict information about structural break existing in the variables. Therefore, Zivot Andrews (1992) unit root test was applied to find single structural break in the variables, and Clemente et al. (1998) unit root test was applied to find two structural breaks in the variables. To find long run causality among variables, the study applied Autoregressive Distributed Lag (ARDL) bound testing procedure as well as Johansen and Juselius 33

4 M. I. Khan & M. Waqas (1990) test. The research found long run relationship among variables. Granger causality test revealed that, energy consumption Granger cause economic growth. Financial development Granger cause energy consumption and vice versa. Energy consumption and trade had bidirectional causality. There was bidirectional causality between energy consumption and capital. There was also bidirectional causality between economic growth and financial development, and between economic growth and international trade. 3. Data and Methodology In this research annual data of the various variables for the period of 1980 to 2011 has been utilized. Data of real GDP constant at 2005 US $ has been taken from World Bank organization. Data on total primary energy consumption, measured in quadrillion Btu, electricity consumption (Billion kwh), petroleum consumption (Thousand barrels per day), gas consumption (Billion cubic feet) and coal consumption (Thousand short tons) have been taken from U.S. Energy Information Administration (EIA). Model Specification The study has borrowed the following econometric model from Loganathan et al. (2010) and Muhammad et al. (2013), in order to estimate relationship between total primary energy consumption and economic growth. Where, EC t = f (GDP t ) (1) GDP t = f (EC t ) (2) lngdp t = α 0 +α 1 lnec t + ε t (3) lnec t = α 0 + α 1 lngdp t + ε t (4) EC, represents total primary energy consumption GDP, is a proxy variable to economic growth Ln, represents log of the variable t, represents time period; and ε t is disturbance term assume to be identical, independent and normally distributed To investigate relationship between economic growth and energy consumption at disaggregate level, (i.e., component parts of energy like; electricity, petroleum, gas and coal); the following model developed by Chaudhry et al. (2012) has been utilized, with some amendments: Where; GDP = f (ELEC, PETR, GAS, COAL) lngdp = β 0 + β 1 lnelec + β 2 lnpetr + β 3 lngas + β 4 lncoal + U t (5) GDP, is proxy variable to economic growth ELEC, represents electricity consumption PETR, represents petroleum consumption GAS, represents consumption of dry gas COAL, represents consumption of coal Ln, represents log of the particular variable; and Ut, represents disturbance term 34

5 Econometric Methodology ADF and P-P Unit Root Tests International Journal of Management Sciences As econometrics theory 3 tells us that the time series data may give spurious regression analysis in case of unit root existing in the series. Therefore, Augmented Dickey-Fuller (ADF) and Phillips-Perron (P-P) unit root tests will be utilized to check the stationarity of the data. In order to reduce the chance of autocorrelation, ADF test uses extra lagged terms of the dependent variable as independent variable(s). Therefore, ADF test has priority over ordinary DF test. Though most of the time series data have trend and intercept, but this test enables us to get three possible results with the help of following form ofequations: ΔZ t = γz t-1 + β i Z t-1 + u t (6) ΔZ t = α 0 + γz t-1 + β i Z t-1 + u t (7) ΔZ t = α 0 + γz t-1 + a 2 t + β i Z t-1 + u t (8) The above three equations are differentiated from each other due to deterministic components α 0 and a 2 t, present in them. Where equation1 representsthe case of no trend and no intercept, equation2 represents the case of intercept and equation3 represents the case of intercept and trend. Basically, P-P test is a modified form of ADF test. While applying ADF test we have to confirm that the error terms have constant variance and they are uncorrelated. But P-P test take into account less restrictive nature of the error process. It estimates the following form of AR (1) process: ΔX t-1 = α 0 + γx t-1 +u t (9) Where; value of coefficient γ provides evidence about presence or absence of serial correlation in error term (u t ). This test also provides three possible results. Both the test use Mckinnon (1991) critical values for the rejection or acceptance of null hypothesis. Engle-Granger (EG) Test Engle-Granger (EG) test will be used to estimate long run relationship between economic growth (GDP) and energy consumption (EC). This test is comprised of four steps. In the first step, simple OLS like the one in equation3 or equation4 is estimated. Secondly, series of residual is obtained from OLS estimated in step one. Rename it as ecm. Thirdly, ADF test is applied on residual series (ecm). Fourthly, if calculated value of ADF in absolute term is greater than tabulated value then there is long run relationship between variables; vice versa. Error Correction Mechanism (ECM) will be included in the Engle-Granger model, in order to find short run relationship between economic growth and energy consumption. The EG-OLS is re-estimated by incorporating residual series (ecm) as independent variable. If the value of coefficient of ecm is positive then there is no short run relationship between variables. If it is negative then there is short run relationship between variables. Augmented Granger Causality Test As most of the macroeconomics variables are not stationary at level. Therefore, Augmented Granger Causality test developed by Toda and Yamamoto (1995) will be utilized to check causal relationship among variables. In case of two variables Y t and Z t, this test can be performed by estimating following equations: Y t = α β m Y t-m + γ n Z t-n + Yt (10) Z t = α + m Z t-m n Y t-n + Zt (11) The main advantage of this test over ordinary Granger Causality test is that, it can be applied in the case when variables have unit roots; no matter they are co-integrated or not. Autoregressive Distributed Lag (ARDL) Co-integration Test 3 Large part of this section is taken from Waqas and Awan (2011). 35

6 M. I. Khan & M. Waqas Autoregressive Distributed Lag (ARDL) co-integration test is considered suitable for the research in which order of integration of variables is not confirmed. It can be applied in the situation, when variables in the research model have different order of integration (i.e., some variables are I(0) and some variables are I(1)). Moreover, it is more efficient in case of small sample size of data. Keeping in view the above said properties, ARDL test will be employed to examine co-integration among variables in equation (5). ARDL test is comprised of three steps. In the first step, co-integration among variables is checked by using bounds testing procedure where different regression models of given variables are estimated by keeping each variable as dependent variable once in the model. Then the decisions are made by comparing F-statistic of each model with its critical bounds values. In the second step, long run relationship is estimated; where coefficients of ARDL regression equation will tell the magnitude and direction of impact of regressors on regressand. While in the third step short run effects of shocks on the long run equilibrium are checked. The lagged ECM-coefficient of ARDL test will show the magnitude and convergence or divergence of disequilibrium. 4. Empirical Evidences Unit Root Results ADF and P-P tests suggest that all the variables are stationary at first difference; except two, (i.e., LnELEC and LnCOAL). ADF test depicts LnELEC as stationary at level in case of intercept, at 5% level of significance. While it is stationary at level in case of trend and intercept, at 10% level of significance. P-P test expresses LnELEC stationary at level in both cases, at 5% level of significance. Regarding LnCOAL, ADF test suggests that it is stationary at level in case of trend and intercept at 5% level of significance [Table1 and Table2]. Granger Causality Test Results Augmented Granger Causality test prescribes that, there is unidirectional causality running from economic growth (GDP) to energy consumption (EC). Petroleum and Gas granger cause GDP. There is unidirectional granger causality between Gas and Coal, where former cause the latter. While GDP, Petroleum and Gas granger cause Electricity. All the decisions, regarding causality are based on comparing p-value of each variable with 5% level of significance [Table3 and Table4]. Engle-Granger Co-integration Results As variables GDP and EC are integrated of same order therefore, Engle-Granger test is applied which represents long run relationship between the variables. Because residual series of both models are stationary at first difference [Table5 and Table6]. There is no evidence of short run relationship between variables because coefficient of Error Correction Mechanism (ECM) has positive sign, which shows divergence of disequilibrium [Table7 and Table8]. ARDL Results First step of ARDL procedure is exhibiting co-integration among variables in the model. The conclusions of bounds test are based on standard F-statistics. As F-statistics of first three hypotheses is greater than the critical value of 95% upper bound therefore there exists co-integration in the variables at 5% level of significance. While F-statistics of last two hypotheses are less than 90% lower bound which represent no co-integration [Table9]. The long run estimates of ARDL test show that the coefficients of LnELEC and LnPETR are significant at 5% level. The sign of coefficient of LnELEC is negative which shows that, electricity consumption is negatively affecting GDP. Moreover, magnitude of the coefficient reveals that one unit change in electricity consumption will bring units change in economic growth. While positive sign of coefficient of LnPETR is showing positive effect of petroleum on GDP. Moreover, the value of coefficient depicts that one unit increase in petroleum consumption will increase economic growth by units. Similarly, magnitude 36

7 International Journal of Management Sciences of coefficients of LnGAS and LnCOAL are and , respectively. Both the coefficients are insignificant at 5% level [Table10]. Now view ARDL short run results. Coefficient of petroleum consumption is significant and negative. It means 1% change in petroleum consumption will negatively affect economic growth by 6.46% in the short run. The coefficient of gas consumption is significant at 10% level with positive sign which shows that 1% increase in gas consumption will increase economic growth by 1.47%. Electricity consumption and coal consumption have positive impact on economic growth in the short run but there coefficients are insignificance. While, coefficient of ecm (-1) is positive and insignificant which represents no short run adjustment to equilibrium after shock. As suggested by aggregate analysis [Table11]. 5. Conclusion and Policy Recommendations The purpose of this study was to find causal link between energy consumption and economic growth. According to Augmented Granger Causality test, there was unidirectional causality running from economic growth to energy consumption. It means that as economy will grow the demand for energy consumption will also grow. Therefore, government should seek more indigenous as well as foreign sources of energy for sustainable growth. There was only long run relationship between energy consumption and economic growth. There was unidirectional causality running from petroleum consumption to economic growth. ARDL test also depicted short run as well as long run relationship between the variables. This may be because of mechanization of economy and less availability of biofuel. Still Pakistan is meeting 85% of oil demand from abroad which requires precious foreign reserves. Therefore, production of biofuel should be promoted in the country. Moreover, there was unidirectional causality between gas consumption and economic growth. ARDL test also revealed short run relationship between the variables. There was unidirectional causality running from GDP to electricity consumption. The long run estimates of ARDL test showed negative impact of electricity consumption on economic growth. Conservation of energy should be promoted at national level. Just through conservation of energy, 20% more energy demand can be fulfilled with the present energy supply. References Abd.Aziz, A., On the causal links between energy consumption and economic growth in Malaysia. International Review of Business Research Papers, 7(6): Ahmad, D.G., Least cost power generation. Conference paper: Solution for energy crisis in Pakistan. Islamabad Policy Research Institute (IRRI). Asafu-Adjaye, J., The relationship between energy consumption, energy prices and economic growth: Time series evidence from Asian developing countries. Elsevier Energy Economics, 22(6): Asteriou, D. and Hall, S.G., Applied Econometrics: A Modern Approach using EViews and Microfit, Revised Edition. Palgrave Macmillan division of St. Martin s press, pp Farhani, S. and Rejeb, J.B., Link between economic growth and energy consumption in over 90 countries. Interdisciplinary Journal of Contemporary Research in Business, 3(11): Hossain, Md.S., Multivariate Granger causality between economic growth, electricity consumption, exports and remittance for the panel of three SAARC countries. Global Journal of Management and Business Research, 12(4):

8 M. I. Khan & M. Waqas Loganathan, Nanthakumar and Subramaniam, T., Dynamic co-integration link between energy consumption and economic performance: Empirical evidence from Malaysia. International Journal of Trade, Economics and Finance, 1(3): Mallick, H., Does energy consumption fuel economic growth in India? Centre for Development Studies, Working Paper No Muhammad, S.D., Usman, M., Mujahid, N. and Lakhan, G.R., Nexus between energy consumption and economic growth: A case study of Pakistan. World Applied Sciences Journal, 24(6): Muhammad, S., Saleheen, K. and Muhammad, I.T., The dynamic link between energy consumption, economic growth, financial development and trade in China: Fresh evidence from multivariate framework analysis. MPRA Paper No Pesaran, M.H., Shin, Y. and Smith, R.J., Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3): Razzaqi, S. and Sherbaz, S., Dynamic relationship between energy and economic growth: Evidence from D8 countries. The Pakistan Development Review, 50(4): Riaz,T.,1984. Pakistan: Energy consumption and economic growth. The Pakistan Development Review, 23(2): Salim, R.A., Rafiq, S. and Hassan A.F.M.Kamrul, Causality and dynamics of energy consumption and output: Evidence from non-oecd Asian countries. Journal of Economic Development, 33(2): Sari, R., Ewing, B.T. and Soytas, U., The relationship between disaggregate energy consumption and industrial production in the United States: An ARDL approach. Elsevier energy economics, 30(5): Shaari, M.S., Hussain, N.E. and Ismail, M.S., Relationship between energy consumption and economic growth: Empirical evidence for Malaysia. Business System Review, 2(1): Toda, H.Y. and Yamamoto,T., Statistical inference in vector auto regressions with possibly integrated processes. Journal of Econometrics, 66(1): Waqas, M. and Awan, M. S. (2011). Are Pakistani Consumers Ricardian? Economics and Business Review; Vol. 13, No. 3. Yuan, Jai-Hai, Kang, Jian-Gang, Zhao, Chang-Hong and Hu, Zhao-Guang, Energy consumption and economic growth: Evidence from China at both aggregated and disaggregated levels. Elsevier Energy Economics, 30(6):

9 International Journal of Management Sciences Annexure Table1: ADF test results for unit root test Variables Level First Difference Intercept(k) Trend and Intercept(k) Trend and Intercept(k) Intercept(k) lngdp (1) (1) (0)** (0)** lnec (0) (0) (0)** (0)** lnelec (0)** (0)*** (0)** (0)** lnpetr (1) (1) (0)** (0)** lngas (0) (0) (0)** (0)** lncoal (0) (3)** (0)** (0)** Note: k in parenthesis shows lag length; which was automatically selected by software Eviews7 in each case, by applying Schwarz Info Criterion. **, shows significance at 5% level and ***, shows significance at 10% level Table2: P-P test results for unit root test Variables Level First Difference Intercept(k) Trend and Intercept(k) Trend and Intercept(k) Intercept(k) lngdp (2)*** (1) (2)** (2)** lnec (2)*** (1) (1)** (3)** lnelec (3)** (2)** lnpetr (2)** (2) (3)** (2)** lngas (3) (1) (3)** (3)** lncoal (4) (3) (3)** (3)** Note:k in parenthesis shows lag length; which was automatically selected by software Eviews7 in each case, by applying Newey-West Bandwidth Criterion. **, shows significance at 5% level and ***, shows significance at 10% level Table3: Augmented Granger Causality test results for GDP and EC Dependent variable: GDP ENERGY All Dependent variable: energy GDP All

10 M. I. Khan & M. Waqas Table4: Augmented Granger Causality test results for GDP and component parts of EC Dependent variable: GDP ELECTRICY PETROLEM GAS COAL All Dependent variable: Electricity GDP PETROLEM GAS COAL All Dependent variable: Petroleum GDP ELECTRICY GAS COAL Dependent variable: Gas All GDP ELECTRICY PETROLEM COAL All Dependent variable: Coal GDP ELECTRICY PETROLEUM GAS All

11 International Journal of Management Sciences Table 5: Bivariate Engle-Granger test results using EC and GDP as dependent variable GDP dependent variable p-value for unit root tests on residuals ^ Intercept(k) Trend and Intercept(k) Level (0) (0) 1 st Difference 0.000*(0) 0.001*(0) Note: ^, Mackinnon (1996) one-sided p-values are used k in parenthesis shows lag length; which was automatically selected by software Eviews7 in each case, by applying Schwarz Info Criterion. * Shows 1% level of significance Table 6: Bivariate Engle-Granger test results using GDP and EC as dependent variable EC dependent variable p-value for unit root tests on residuals ^ Intercept(k) Trend and Intercept(k) level 0.410(0) (0) 1 st Difference 0.000*(0) 0.001*(0) Note: ^, Mackinnon (1996) one-sided p-values are used k in parenthesis shows lag length; which was automatically selected by software Eviews7 in each case, by applying Schwarz Info Criterion. * Shows 1% level of significance Table 7: ECM results using EC as independent and GDP as dependent variable Variable Coefficient Std. Error t-statistic Prob. C -2.17E E E ENERGY 5.04E E E ECM E E R-squared Mean dependent var 7.70E+10 Adjusted R-squared S.D. dependent var 3.09E+10 S.E. of regression 7.24E-05 Akaike info criterion Sum squared resid 1.52E-07 Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic 2.82E+30 Durbin-Watson stat Prob(F-statistic) Table 8: ECM results using GDP as independent and EC as dependent variable Variable Coefficient Std. Error t-statistic Prob. C E E GDP 1.96E E E ECM E E R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression 3.26E-16 Sum squared resid 3.08E-30 F-statistic 5.41E+31 Durbin-Watson stat Prob(F-statistic)

12 M. I. Khan & M. Waqas Table 9: ARDL Bounds- Testing results Co-integration hypotheses F-statistics Outcome F(LNGDP/LNELEC,LNPETR,LNGAS,LNCOAL) ** Co-integration F(LNELEC/LNGDP,LNPETR,LNGAS,LNCOAL) 9.534** Co-integration F(LNPETR/LNGDP,LNELEC,LNGAS,LNCOAL) 8.951** Co-integration F(LNGAS/LNGDP,LNELEC,LNPETR,LNCOAL) No Co-integration F(LNCOAL/LNGDP,LNELEC,LNPETR,LNGAS) No Co-integration Critical values:95% Lower Bound 95% Upper Bound 90% Lower Bound 90% Upper Bound Note: **, shows 5% level of significance Table10: Estimated Long Run Coefficients using the ARDL Approach, ARDL (1, 0, 0, 1, 0) selected based on Schwarz Bayesian Criterion Dependent variable is LNGDP Regressor Coefficient Standard Error T- Ratio T- Probability LNELEC **.028 LNPETR **.024 LNGAS LNCOAL Note: **, shows 5% level of significance Table11: Error Correction Representation for the selected ARDL Model, ARDL (1, 0, 0, 1, 0) selected based on Schwarz Bayesian Criterion Dependent variable is dlngdp Regressor Coefficient Standard Error T- Ratio T- Probability dlnelec dlnpetr **.026 dlngas ***.084 dlncoal ecm(-1) ecm = LNGDP *LNELEC *LNPETRO *LNGAS *LNC R-Squared = R-Bar-Squared = S.E. of Regression = F-Stat. F(4,26)= [.000] RSS = DW-statistic = Schwarz Bayesian Criterion = Akaike Info. Criterion =