Notably, non-normality of economic variables among other effects may be associated with the presence of outliers. It is therefore important, before embarking on empirical investigations, to examine whether or not the data exhibits normality. Therefore, the study adopted three normality tests: test for skewness in the distribution, test for kurtosis distribution and finally test for Jarque-Bera distribution statistic which is a combination of the skewness test and the kurtosis tests. The skewness of a symmetric distribution, such as the normal distribution is expected to be zero. Positive skewness means that the distribution has a long right tail and negative skewness implies that the distribution has a long left tail. According to the findings (appendix, table 1), while changes in the MNFG (-0.027), ELC (0.425), are normally distributed with slight negative and positive skew, meaning that extreme changes were not recorded in the observed period. PET (1.47) is border line normally distributed with a positive skew. Thus, the study concluded that the variables are a symmetric. Therefore, we reject the null hypothesis that the variables are not normally distributed around their mean. On the other hand, Kurtosis distribution of the variables which is a measure of the levels of peak or flatness of the distribution of the series around their mean was determined. Normally the kurtosis of a normal distribution is 3. If the kurtosis exceeds 3, the distribution is leptokurtic relative to the normal; if the kurtosis is less than 3, the distribution is platykurtic relative to the normal. The distribution of MNFG (2.07) and PET (3.942) are borderline leptokurtic to the normal while the distributions of ELC (2.97) is borderline less than 3 meaning its distribution is relatively platykurtic to normal. Again, the kurtosis analysis shows that there are no extreme variations in the distributions relative to the normal. A further consideration of the distribution of the variables around the mean was done by Jarque-Bera test statistic. The test statistic measures the difference of the skewness and kurtosis of the series with those from the normal distribution. The reported results show that the probability of Jarque-Bera statistic exceeds the observed values. A small probability value leads to the rejection of the null hypothesis of normal distribution. Observably, all the variables were significantly different from zero in their absolute values. We therefore reject the null hypothesis to accept that all variables are normally distributed at the 0.05 significance level.
Green House Gas (GHG) in the atmosphere and resulting global warming and climate change. Global warming and climate change affect pattern of rainfall, worsen the agricultural productivity and reduce the productivity of labour force. Accordingly, economists and environmentalists became more aware of the environmental consequences of economic growth, which shifted the attention from simple economic growth to the ecology (environment) friendly economic growth ( Alam, Murad, Noman, & Ozturk, 2016). The relationship betweenenergyconsumption and economic growth, energyconsumption and environmental pollution as well as economic growth and environmental pollution, has been the issue of intense research in the energy-economics literature (Acaravci & Ozturk, 2010a). Nevertheless, the empirical evidence remains controversial and unclear. The existing literature reveals that empirical studies differ substantially in terms of methods of data analysis and are not conclusive to present policy recommendation that can be applied across countries (Ozturk, Aslan, & Kalyoncu, 2010). Most of the existing studies focus either on the nexus of economic growth-energyconsumption or economic growth- environmental pollutants where little effort has been made to test these two relations under the same model ( Ozturk & Acaravci, 2010b).
Although several plausible nonlinear models have been used in the empirical economics literature, we prefer smooth transition regression (STR) modelling approach. The STR modelling approach has several advantages over other nonlinear models (see, for example, Teräsvirta and Anderson, 1992; Granger and Teräsvirta, 1993). First, STR models are theoretically more appealing over simple threshold and Markov regime switching models, which impose an abrupt change in coefficients. Instantaneous changes in regimes are possible only if all economic agents act simultaneously and in the same direction. Second, the STR model allows for modelling different types of nonlinear and asymmetric dynamics depending on the type of the transition function. In particular, a STR model with a first-order logistic transition function is more convenient for modelling the interaction betweenenergyconsumption and output growth rate if the dynamic interrelationships between the variables depend on the phases of business cycles. On the other hand, a STR model with an exponential or second-order logistic transition function is more convenient if, for example, the interaction between the variables depend not on the sign but on the size of fluctuations in variables. Finally, STR modelling approach allows one to choose both the appropriate switching variable and the type of the transition function unlike other regime switching models that impose both the switching variable and function a priori.
Adding to the intensive research literature of energy-growth hypothesis, our study emphasizes on the short and long run relationship betweenenergyconsumption, manufacturing output and economic growth in Pakistan while using ARDL bound testing approach. We further provide the evidence of the direction of causality among the variables by using Granger Causality analysis.
The ARDL method by Pesaran et al. (2001) has advantages over other estimators. Because it does not require testing for unit roots, it can indicate which variable should be the dependent variable and it calculates both short and long run estimates through linear transformation technique and its suitable in case of mixed stationarity at I (0) and I (1). There are several studies that used ARDL approach: Sami and Makun (2011) found that there is a significant relation among trade, energyconsumption and economic development in Brazil; Talib and Fan (2019) applied the method in Pakistan and concluded a unidirectional causalitybetweenmanufacturing and economic growth and a bidirectional causalitybetweenenergyconsumption and manufacturing; Adebola (2011) found an unidirectional causalitybetweenenergyconsumption and economic growth and another unidirectional causalitybetween capital formation and economic growth in Botswana; Akalpler and Hove (2019) established the long term relation between capital formation and trade with economic growth in India; Chen et al. (2019) applied the model and concluded a direct causalitybetween economic growth and CO2 emissions in China; Ahmad and Du (2017) established the direct causalitybetween capital formation and energyconsumption with economic growth in Iran. Engle and Granger (1987) integrated the concept of cointegration into causality, stating that causal relations among variables can be examined within the framework of the ECM. A time series (X) is said to Granger-cause another time series (Y) if the prediction error of current Y declines by using past values of X in addition to past values of Y, the error correction term contains the information of long run causality. Hence, significance of each explanatory variable lags depict short run causality. On the other hand, a negative and statistical significant error correction term is assumed to signify long run causality.
This paper examined the relationship between oil consumption and economic growth in OPEC countries within a panel cointegration and panel based error correction model by using data from 1980 – 2011. In this paper we use unit root test and cointegration test for empirical test as gross domestic production (GDP) and oil consumption according to ADF test was integrated of one, we used Granger causality test. The results indicate the presence of a long run relationship among real GDP and oil consumption. The short run results also indicate the causality running from oil consumption to economic growth and vice-versa, supporting the feedback hypothesis which asserts that energy policies oriented toward improvements in oil consumption efficiency would not adversely affect real GDP. In other words we can say that energy efficiency have not a significant effect on economic growth in long – run. For this country there are any casualty between this variables in long-run.
The novelty of our study is that it bridge’s a lacuna in the literature by combining non- linear causality techniques along with the use of a quality-weighted scheme in constructing the total energyconsumption series for the Greek economy. To the extent that we know the literature there are no previous studies combining non-linear causality techniques along with the use of a quality-weighted scheme in the construction of the total energyconsumption series. The energy quality adjustment approach adopted in this paper is in accordance with the influential study of Cleveland et al. (2000), who argued that in ”aggregating different energy types by their heat units embodies a serious flaw: it ignores qualitative differences among energy vectors” as well as that ”adjusting energy for quality is important as is consid- ering the context within energy use is occurring”. This view, for energy quality adjustment, is further acknowledged and supported by Zachariadis (2007), who argued that such practice has to be seriously considered in similar empirical applications. Moreover, our methodolog- ical framework is in line with the conclusion of Ozturk (2010) who noted that ”it should be understood that research papers using the same methods with the same variables, just by changing the time period examined, have no more potential to make contribution to the existing energy-growth literature”. In particular, apart from the usual implementation of the standard Granger causality test (Granger, 1969); we applied the well known non-parametric Hiemstra and Jones (1994) test for non-linear causality as well as its recent modification, proposed by Diks and Panchenko (2006).
Oil is also very important for the Iran’s economic growth. This paper studies the causal relationships between oil consumption and economic growth for Iran using cointegration and error correction model from annual data covering the period of 1980-2010. As economic growth and oil consumption variables used in empirical analysis was integrated of order one, employed Granger causality test. The results show that in the short-run, the Granger causality runs from economic growth to energyconsumption In Iran. However, in the long run there is not any Granger causality relationship for this country. In other words, if unidirectional causality runs from energyconsumption to income, reducing energyconsumption could lead to a fall in economic growth.
Since the seminal work of Kraft and Kraft (1978), examining the causal relationship between economic growth and energyconsumption has been subject to numerous empirical studies which tried to find the direction of causalitybetweenenergyconsumption and economic growth using different econometric techniques. However, the results of empirical studies are mixed ranging from bi- and uni-directional causality to no causality for both developed and developing countries. These conflicting results may be due to the fact that countries have different energyconsumption patterns and various sources of energy. Therefore, different sources of energy may have varying impacts on the output of an economy (Soytas and Sari, 2007). For example, Erol and Yu(1987), Chontanawat et al.(2006), Halicioglu(2007), Yoo(2006), Lee(2006), Ghosh(2002), Cheng and Lai(1997) and Masih and Masih(1996) for the cases of Germany, Canada, Turkey, Thailand, France, India, Taiwan and Indonesia, respectively, find no causality running from energyconsumption to economic growth. While Soytas and Sari(2003), Lee(2006), Asafu-Adjaye(2000), Masih and Masih(1998), Soytas and Sari(2003), Masih and Masih(1996), Lee and Chang(2005) and Fatai et al.(2004), for the same countries, find uni-directional causality from energyconsumption to GDP growth. Uni-directional causality from energyconsumption to economic growth also were found in the cases of U.S.(Stern, 1993; 2000), Philippines(Yu and Choi, 1985), Singapore(Glasure and Lee, 1997), Sri Lanka(Masih and Masih, 1998; Morimoto and Hope, 2004), China(Shiu and Lam, 2004), Belgium, Netherland and Switzerland(Lee, 2006). On the other hand, a reverse uni-directional causality running from economic growth to energyconsumption was found for the cases of South Korea (Yu and Choi, 1985; Oh and Lee, 2004; Soytas and Sari, 2003), Italy (Erol and Yu
Soytas and Sari (2003) discovered bidirectional causality in Argentina, causality running from GDP to energyconsumption in Italy and Korea, and from energyconsumption to GDP in Turkey, France, Germany and Japan. Paul and Bhattacharya (2004) found bidirectional causalitybetweenenergyconsumption and economic growth in India. The empirical results by Oh and Lee (2004) for the case of Korea suggested the existence of a long-run bidirectional causal relationship betweenenergy and GDP, and short-run unidirectional causality running from energy to GDP using vector error correction model (VECM). Based on a production function approach, Ghali and El-Sakka (2004), develops a vector error-correction (VEC) model to test the existence and direction of causalitybetween output growth and energy use in Canada. Their empirical findings indicate that the long-run movements of output, labour, capital and energy use in Canada are related by two co-integrating vectors and the short-run dynamics of the variables indicate that Granger- causality is running in both directions between output growth and energy use.
theoretical and econometrical methodologies to arrive at certain conclusions. It is found that empirical researchers are far from agreement that the environmental Kuznets curve provides a good fit to the available data, even for conventional pollutants (Lakshmi & Sahu, 2012). Although a number of studies have examined the relationship between carbon emissions and economic growth in developing countries, the majority of these studies have mainly concentrated on the relevance of the Environmental Kuznets Curve (EKC). Very few studies have gone the full distance to examine the nexus between CO2 emissions and economic growth. Even where such studies have been done, the focus has mainly been on Asia and Latin American countries. Studies on the causal relationship between carbon emissions and economic growth in sub-Saharan countries are very scant. In addition, the majority of the previous studies suffer from four major weaknesses; namely, 1) the use of a bivariate causality test, which may lead to the omission-of-variable bias; 2) the use of cross-sectional data, which does not satisfactorily address the country-specific effects; 3) the use of the maximum likelihood test based on Johansen (1988) and Johansen and Juselius (1990), which has been proven to be inappropriate when the sample size is too small (see Nerayan and Smyth, 2005); and 4) they employ unit root tests which fail to consider structural breaks. It is against this backdrop that the current study attempts to examine the inter-temporal causal relationship between CO2 emissions and economic growth, using the newly developed ARDL-Bounds testing approach. By incorporating energyconsumption as an intermittent variable in a bivariate setting between CO2 emissions and economic growth, we develop a simple trivariate causality model between CO2 emissions, energyconsumption and economic growth (Odhiambo, 2011).
Using data on 17 African countries including Egypt during the period 1971–2001, Wolde- Rufael (2006) found mixed results concerning the causalitybetween electricity consumption and economic growth. For Egypt, Wolde-Rufael (2006) found positive bidirectional causalitybetween electricity consumption and economic growth. In another study, Wolde-Rufael (2009) re- examined the causal relationship betweenenergyconsumption and economic growth in seventeen African countries including Egypt during the period 1971-2004, within a multivariate framework by including labor and capital as additional variables. A variance decomposition analysis was used to evaluate the importance of the causal effect of energyconsumption on economic growth relative to labor and capital. The causality test rejected the neutrality hypothesis for the energy–income relationship in fifteen out of the seventeen countries. Results of the variance decomposition analyses showed that in eleven out of the seventeen countries, energy is merely a contributing factor to output growth and not an important one when compared to capital and labor. For Egypt, Wolde-Rufael (2009) found a uni-directional causality running from economic growth to energyconsumption. Similar mixed results on the direction of causalitybetween economic growth and energyconsumption was found by Akinlo (2008) using a multivariate causality test for eleven
Meanwhile, in terms of energy formation, Jacobsen (2007) did the input-output tables for Malaysia in 2000 and the energy goods domestically supplied as well as imported. His study took three energy commodities/sectors in the Malaysia’s input- output tables. According to Table 2, we extract that crude petrol, natural gas, and coal as the main contributors for output formation, accounted for RM 46 billion, meanwhile electricity and gas is a small amount, around RM 15 billion. Interestingly, those three energy commodities/sectors are mainly impetus for Malaysian growth since they are used to supply either for domestic intermediate or domestic final goods. In other words, Malaysian economic growth is highly dependence on the energyconsumption, particularly crude oil, natural gas, coal, petrol, and coal product. This phenomenon was explained by Ang (2008) who explores long run relationship and causality among output, energyconsumption and pollutant emissions for Malaysia over the period 1971-1999. He found pollution and energyconsumption positively affect output in the long run. The causality runs from economic growth to energyconsumptiongrowth, both in the short and the long- run. More specifically, Shaari et al (2012) concluded that there is a long-run relationship betweenenergyconsumption and GDP. However, once the granger causality model is used, the oil and coal consumption do not granger cause economic growth and vice versa. A unidirectional relationship exists between gas and economic growth in Malaysia.
impact of oil price shocks on some macroeconomic variables in the US. Burbidge and Harrison (1984) have also found supporting evidences from the US, Canada, UK, Japan and Germany. Using VAR models, Burbidge and Harrison (1984) show that the 1973 - 1974 oil embargo explains a substantial part of the behavior of industrial production in examined country. Jiménez-Rodríguez and Sánchez (2005) ﬁnd that oil price movement has asymmetric impact on GDP growth in major industrialised countries. Notably, oil price upsurges are found to have an impact on GDP growth of a larger magnitude than that of oil price declines, with the latter being statistically insigniﬁcant in most cases. Among oil importing countries, oil price increases are found to have a negative impact on economic activity in all cases but Japan. Moreover, the eﬀect of oil shocks on GDP growth diﬀers between the two oil exporting countries in the sample, with the UK being negatively aﬀected by an oil price increase and Norway beneﬁting from it. Álvarez et al. (2011) ﬁnd that the changes in crude oil prices have both direct and indirect impacts on Spain and Euro zone economic growth. In addition, they ﬁnd evidence that crude oil prices play a vital role in determining inﬂation. In contrast, Mehrara and Mohaghegh (2011) ﬁnd that oil price movements are not necessary inﬂationary. Yet, the results of the panel VAR model show that changes in oil price yield a signiﬁcant eﬀect on economic growth and a positive and signiﬁcant eﬀect on money supply.
develop. Hence, the impact might not be felt immediately in the short run. However, the coefficients of labour and capital are both statistically significant at 5% level which implies that the two variables contribute to economic growth in the short run. The z-plus and z-minus are the asymmetric long run speed of adjustment parameters which measure the speed at which the model adjusts to the long run equilibrium whenever there is disequilibrium in the short run. In the model, they are denoted as 𝜌 and 𝜌 . Nonetheless, from Table 2 it can be gleaned that both the coefficients of 𝜌 and 𝜌 have the correctmathematical sign (negative) but only the latter is statistically significant. The error correction terms indicate that 20% of positive de- viations are eliminated per year during the increase/positive shocks whereas negative deviations are eliminated at a rate of 83% during same time frame. Moreover, these adjustment coefficients indicate that any short-run deviations from the steady-state equilibrium during period of high natural gas consumption will take 5.1years (1/0.195899) to automatically correct/adjust to the long-run equilibrium while in the period of low natural gas consumption it takes 1.2years (1/0.830275) to adjust. This implies that the speed of adjustment towards steady-state equilibrium in the lower regime is faster than in the higher regime.
Brazil is the amongst the top patrons of the energy . For the erection of buildings, conveyance, cultivation inhabitants of different economies devours large amount of energy (F. Islam, Shahbaz, Ahmed, & Alam, 2013)as the inhabitants increases, it will govern the level of energy essential –larger the population ,the more the oomph will be consumed (Batliwala & Reddy).The economic progress of any country is meticulously associated to the ingesting of energy . Brazil is among the commercial energy consumer but still Brazil necessities much more energy to keep pace with its growth and economic purposes as well as to fulfills growing needs of its population.
Sarkar and Singh (2010) also showed that energy efficiency programs can conserve natural resources, reduce the environmental pollution and carbon footprint of the energy sector, reduce a country’s dependence on fossil fuels, thus enhancing its energy security, ease infrastructure bottlenecks and impacts of temporary power shortfalls, as well as improve industrial and commercial competitiveness through reduced operating costs. Using monthly data for Lebanon, Abosedra, et al. (2009) investigated the causal relationship between electricity consumption and economic growth for Lebanon, Empirical results of the study confirm the absence of a long-term equilibrium relationship between electricity consumption and economic growth but the existence of unidirectional causality running from electricity consumption to economic growth. Belloumi (2009) used Johansen cointegration technique to examine the causal relationship between per capita energyconsumption and per capita gross domestic product for Tunisia during 1971–2004. Estimation results indicate that the economic growth and electricity consumption are related by one cointegrating vector and that there is a long-run bi-directional causal relationship between the two series and a short-run unidirectional causality from energy to GDP.
By using the data for the period 1955 to 1996 for Pakistan, Aqeel and Butt (2001) concluded that economic growth causes total energyconsumption. The study further investigated that economic growth leads to growth in petroleum consumption. On the other hand, in the case of gas sector, neither economic growth nor gas consumption affects each other. In power sector, electricity consumption leads to economic growth. Lee (2005) tested the cross sectional and time series data of 18 developing countries to find the relationship betweenenergyconsumption and GDP. He concluded that energyconsumption Granger causes GDP. Asafu (2000) found a unidirectional Granger causality from energy to income in India and Indonesia while bi directional causalitybetweenenergy an income in the case Thailand and Philippines. By using a data for three SAARC countries, Imran and Masood (2010) found a long run relationship between economic growth and energyconsumption and a unidirectional causality from energyconsumption to economic growth but no causality found in short run. Jumble (2004) found that the causality running from electricity consumption to the income in the case of Turkey.
There is no unanimous idea regarding the direction of causality due to countries’ characteristics, different data sets, variables and econometric methodologies that used (Naser, 2014, p. 289). Meanwhile, the usage of aggregate energyconsumption rather than disaggregated one may be another reason behind the lack of uniformity in empirical results since the importance of a certain energy resource for a country may change over time. Thus, the usage of disaggregated data rather than aggregate one may be more meaningful (Naser, 2014, p. 289). To this end, nuclear energy is typically used as a disaggregate energy measure in recent empirical studies. Also, due to volatile oil prices, rapid energy demand growth, scarcity of alternative resources and high dependence on foreign energy sources, the importance of nuclear energy has been accelerating. Moreover, nuclear energy development induces industry-wide technology spill-over effects, and enhances the productivity of capital and labor (Toth and Rogner, 2006; Yoo and Ku, 2009; Yoo and Jung, 2005). Besides, nuclear energy has a main role in electricity supply which in turn is important for a nation’s industry (Yoo and Ku, 2009, p.1905). Nuclear energyconsumption also reduces air pollution and greenhouse gas emissions (Toth and Rogner, 2006; Heo et al., 2011, p.111). As a result of these advantages, the demand for nuclear energy raises. To alleviate increase in demand and formulate appropriate nuclear energy policies, policy makers need information regarding the relationship between nuclear energyconsumption and economic growth (Yoo and Ku, 2009).
According to the results of the short run causality, there is evidence to support the growth hypothesis in OECD countries with high income. There is evidence to support the conservation hypothesis for Brazil, France, Mexico Turkey and countries with high growth. The conservation hypothesis suggests that the policy of conserving hydropower energy may be implemented with little or no adverse effects on economic growth, such as in a less energy-dependent economy. Therefore this is not the theoretically expected outcome for developing countries.