ENEECO-02850; No of Pages Energy Economics xxx (2014) xxx–xxx Contents lists available at ScienceDirect Energy Economics journal homepage: www.elsevier.com/locate/eneco Applied econometrics and implications for energy economics research Russell Smyth a,⁎, Paresh Kumar Narayan b,1 a b Department of Economics, Monash University 3800, Australia School of Accounting, Economics and Finance, Deakin University, 221 Burwood Highway, Burwood, Victoria 3125, Australia a r t i c l e i n f o Article history: Received April 2014 Received in revised form 18 June 2014 Accepted 29 July 2014 Available online xxxx JEL classification: Q40 Q41 Q42 Q48 Q49 Keywords: Unit root Cointegration Granger causality a b s t r a c t Developments in applied econometrics, particularly with regard to unit root tests and cointegration tests, have motivated a rich empirical literature on energy economics over the last decade This study reviews recent developments in time series econometrics applications in the energy economics literature We first consider the literature on the integration properties of energy variables We begin with a discussion of the implications of whether energy variables contain a unit root and proceed to examine how results differ according to the specific unit root or stationarity test employed We then proceed to examine recent developments in the literature on cointegration, Granger causality and long-run estimates between (disaggregated) energy consumption and economic growth We review both single country and panel studies and pay particular attention to studies which have expanded the literature through adding variables such as financial development and trade, in addition to energy consumption to the augmented production function, as well as studies which have extended the literature through examining disaggregated energy consumption by type In each case we highlight best practice in the literature, point to limitations in the literature, including econometric modeling challenges, and suggest recommendations for future research A key message of our survey is that the profession needs to guard against ‘overload’ of research in these areas as most applied studies are no longer adding anything more to what is already known © 2014 Elsevier B.V All rights reserved Introduction Developments in applied econometric estimation methods have been the catalyst for a rich body of applied energy economics research Judging by papers accepted, and published, in leading energy economics journals, this trend is gaining momentum There is a need to take stock of this literature There is a need to review whether, at least in the most popular strands of the energy economics literature, greater volume of applied work is adding anything new If it is making new contributions—this is welcomed, but if it is not, then future directions of research need to be reconsidered This paper is a response to the growing energy economics literature motivated by new developments in applied econometrics This paper not only addresses whether additional applied research is adding new insights to what is already known in two of the most popular fields in the energy economics literature, but also offers several directions for future research, allowing the profession to develop, and expand, upon the rich body of literature that it has so successfully developed We focus on two specific strands of the energy economics literature that have their origins in applied econometric methods Specifically, our focus is on (a) integration properties of energy variables and ⁎ Corresponding author Tel.: +61 99051560; fax: +61 9905 5476 E-mail addresses: russell.smyth@monash.edu (R Smyth), paresh.narayan@deakin.edu.au (P.K Narayan) Tel.: +61 9244 6180; fax: +61 9244 6034 (b) cointegration and Granger causality analysis So much growth in energy economics research has documented that a need to undertake a stock take of this literature is not only timely but, hopefully, will also guide future research in energy economics In certain strands of the energy economics literature, it seems as if applied work is no longer making any new contributions and throwing any new light to what is already known (see also Karanfil, 2009) It is this ‘overload’ of research in certain fields against which the literature needs to guard Our review of the literature suggests two messages, which have important implications for existing and future research in energy economics based on new developments in applied econometrics First, there is largely a consensus in the unit root literature that most energy type variables are stationary if tests utilize sufficiently large time-series data This is confirmed by panel data models that examine the same unit root null hypothesis Because panel data models have the advantage of having more power to reject the null hypothesis—a power gain that results from pooling of time-series components of a panel with its crosssection—almost all panel data unit root models with structural breaks reveal clear evidence that energy variables are stationary It is imperative to assign greater weight to panel data models of unit root tests, as opposed to time-series models, because unit root models function parsimoniously when they are imposed on large sample sizes Typically most energy type variables will have 30–40 years of annual data, which, particularly when the literature uses the relatively more popular structural break unit root models, is insufficient for unit root models to function precisely Panel data models are a perfect response to this http://dx.doi.org/10.1016/j.eneco.2014.07.023 0140-9883/© 2014 Elsevier B.V All rights reserved Please cite this article as: Smyth, R., Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx (2014) xxx–xxx concern with time-series models Our main message here is that the energy unit root literature has reached a point of consensus and unless of course there are new developments in unit root tests that suit the application of energy variables, there is perhaps not much to gain from additional applications of unit root tests Second, when we ask whether cointegration between energy variables and non-energy variables exists, the answer is overwhelmingly affirmative Therefore, existence of a long-run relationship between energy variables and non-energy variables has become somewhat of a stylized fact By comparison, there is no consensus when it comes to interpreting evidence on Granger causality, for which the evidence is mixed The mixed findings for Granger causality reflect several factors, including institutional differences between countries, model specification and econometric approach With respect to model specification and econometric testing, there are at least two important considerations One is that Granger causality is almost always tested in a multivariate framework, and since the sample size to begin with is already small, a multivariate treatment leads to loss of degrees of freedom The second concerns the choice of lag length in a Granger causality model Despite being chosen based on a lag length selection criteria, if the selected lag length is high, the model will be problematic since the sample period of estimation is already small and this results in an over-parameterized model Ideally, a multivariate model should be used (see Payne, 2010) However, such an empirical specification assumes a large sample size Typically, in applied energy economics literature, this is hardly the case In the absence of large historical time-series data, an alternative framework in which to consider Granger causality is bivariate models, as, for example, in Narayan and Popp (2012) and Narayan and Popp (2010a,b,c,d) There is obviously a trade-off Within a bivariate framework, concern relates to problems associated with omitted variable bias, while, with a multivariate model the concern is with over-parameterization and loss of degrees of freedom, which contributes to estimation error One could argue that to obviate the small sample and omitted variable biases, one should use a panel Granger causality model It will be sufficient, to the extent that the objective is to examine a group of countries, for a panel Granger causality model will not reveal anything about the causality relationship for individual countries that make up the panel In sum, a panel data model will not be appropriate if the research question and resulting policy implications focus on results for individual countries The rest of the paper is set out as follows Section is about integration properties of energy variables It begins with an analysis of motivation and implications, reviews the literature, discusses what constitutes the ‘state-of-the-art’ in the field and concludes with an agenda for future research These steps are repeated in Section on the subject of cointegration, Granger causality, and long-run estimates The final section concludes by reiterating the main implications and messages in the paper Integration properties of energy variables 2.1 Motivation and implications The main motivation for testing for a unit root in energy consumption or production is to ascertain whether shocks have permanent or temporary effects If energy consumption or production contains a unit root, shocks will have permanent effects If energy consumption or production is stationary, a shock will result in only a temporary deviation from the energy variable's long run growth path (Smyth, 2013) There are several implications stemming from whether shocks to energy variables are permanent or temporary (Narayan and Smyth, 2007; Smyth, 2013) The major implication is whether the relevant shock represents a policy change designed to reduce consumption of fossil fuels or promote consumption of renewable energy If fossil fuels contain a unit root, policies designed to reduce energy consumption will be effective because the negative shock induced by the policy change will be persistent If renewable energy contains a unit root, policies designed to induce permanent changes, such as renewable portfolio standards, will be more effective than policies designed to induce temporary changes, such as tax incentives (Barros et al., 2012) There are several other implications as well First, if energy is integrated into the real economy, one can expect that following a shock to energy consumption or production, non-stationarities will be transmitted to other macroeconomic variables, such as employment and output Second, if shocks to energy variables result in persistent spreading to other macroeconomic variables, this raises serious questions about economic theories, such as real business cycle models, premised on output being stationary and has implications for the efficacy of Keynesian demand management policies Third, whether energy variables contain a unit root has implications for forecasting energy demand and the correct modeling of energy and other variables, such as economic growth (for more details see Smyth, 2013) 2.2 Overview of existing studies The early studies applied the Augmented Dickey–Fuller (ADF) unit root test to energy consumption for a large number of countries (Hasanov and Telatar, 2011; Narayan and Smyth, 2007) The main conclusion from these studies was that the unit root could be rejected for about one third of countries While these findings serve as a benchmark, traditional unit root tests, such as the ADF test, have several limitations, meaning they have low power to reject the unit root null hypothesis These limitations include low power to reject the unit root null hypothesis in the presence of one or more structural breaks, non-linearities in the data, if the alternatives are of a fractional form or if there is an insufficient number of observations Each of these limitations has served as a catalyst for subsequent studies to re-examine whether there is a unit root using more recent tests which address one or more of these shortcomings associated with traditional tests A limited number of studies have addressed the issue of the low power of traditional tests in the presence of non-linearities in energy variables (Aslan, 2011; Aslan and Kum, 2011; Hasanov and Telatar, 2011; Maslyuk and Smyth, 2009) Overall, the evidence from these studies is that energy variables contain a unit root (Aslan and Kum, 2011) or that the evidence is ambiguous (Aslan, 2011; Hasanov and Telatar, 2011) In general, the evidence from studies which have applied non-linear unit root tests is more consistent with energy consumption and production being non-stationary Related studies that have applied a non-linear version of an observed components model have found evidence of persistence in consumption of specific types of energy such as coal and natural gas (Congregado et al., 2012; Golpe et al., 2012) A number of studies have addressed the issue of the low power of traditional tests to reject the unit root null hypothesis in the presence of one or more structural breaks Most studies which have employed a univariate unit root test with one or two structural breaks have used the Lee and Strazicich (2003) Lagrange multiplier (LM) unit root test with one or two breaks (Agnolucci and Venn, 2011; Apergis et al., 2010a,b; Aslan, 2011; Aslan and Kum, 2011; Lean and Smyth, 2013, 2014a,b; Maslyuk and Dharmaratna, 2013; Mishra and Smyth, 2014a, b; Narayan et al., 2010a,b,c,d) Some studies have employed the Narayan and Popp (2010) unit root test with one and two breaks (Apergis and Payne, 2010a,b,c,d; Mishra and Smyth, 2014a,b) The main finding from these studies is that energy consumption is stationary around a broken trend, although some studies have reached inconclusive results or found that energy variables contain a unit root, even after accommodating structural breaks (see e.g Aslan, 2011; Lean and Smyth, 2013; Maslyuk and Dharmaratna, 2013; Mishra and Smyth, 2014a,b) Most studies that have tested for a unit root in energy consumption have employed annual data; however, some studies have Please cite this article as: Smyth, R., Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx (2014) xxx–xxx employed higher frequency data such as quarterly or monthly data (see e.g Gil-Alana et al., 2010; Lean and Smyth, 2009, 2013) High frequency data is subject to heteroskedasticity Failure to account for heteroskedasticity in high frequency energy consumption data lowers the power to reject the unit root null hypothesis (Narayan and Liu, 2013) Narayan and Liu (2013) develop a GARCH unit root test with structural breaks that simultaneously account for heteroskedasticity and structural breaks in high frequency data Mishra and Smyth (2014a) find that US monthly natural gas consumption contains a unit root with the Lee and Strazicich (2003) and Narayan and Popp (2010) unit root tests, but when the Narayan and Liu (2013) test is used, the series is found to be mean reverting There are also a growing number of studies that have tested for fractional integration in energy variables with and without structural breaks (Apergis and Tsoumas, 2011, 2012; Barros et al., 2011, 2012, 2013; Gil-Alana et al., 2010; Lean and Smyth, 2009) The most common finding from these studies is that the degree of integration lies between 0.5 and 1, suggesting a high level of persistence with mean reversion taking a long time The level of persistence is affected by the inclusion of structural breaks with more evidence of reversion in the presence of breaks A final set of studies have employed panel data to address the short span of data One subset of studies has employed traditional panel unit root tests without structural breaks (Agnolucci and Venn, 2011; Apergis and Payne, 2010a,b,c,d; Hsu et al., 2008; Mishra et al., 2009a,b; Narayan and Smyth, 2007; Narayan et al., 2008) Overall, these studies have found more evidence in favor of a panel unit root A second subset of studies have employed panel tests that accommodate structural breaks to energy consumption or production Among these, studies have applied the Im et al (2005) and Westerlund (2005) panel unit root tests and the Carrion-i-Silvestre et al (2005) panel stationarity test (Agnolucci and Venn, 2011; Apergis et al., 2010a,b; Chen and Lee, 2007; Lean and Smyth, 2013, 2014a,b,c; Mishra et al., 2009a,b; Narayan et al., 2008) Most of these studies conclude that energy consumption and production are stationary Many of the studies have applied unit root tests to aggregate data on energy consumption or production (Agnolucci and Venn, 2011; Aslan and Kum, 2011; Chen and Lee, 2007; Hasanov and Telatar, 2011; Hsu et al., 2008; Mishra et al., 2009a,b; Narayan et al., 2010a,b,c,d; Narayan and Smyth, 2007; Ozturk & Aslan, 2011) The problem with doing this, however, is that focusing on aggregate energy variables masks the fact that some types of energy might be stationary and others nonstationary As a consequence, in the recent literature much attention has been given to examining the unit root properties of various types of disaggregated energy consumption and production Some studies have considered various types of fossil fuels (Apergis and Payne, 2010a,b,c,d; Apergis and Tsoumas, 2012; Aslan, 2011; Barros et al., 2011; Lean and Smyth, 2009; Maslyuk and Dharmaratna, 2013; Maslyuk and Smyth, 2009; Mishra and Smyth, 2014a,b; Narayan et al., 2008) Other studies have centered on renewable and alternative energy sources (Apergis and Tsoumas, 2011; Barros et al., 2012, 2013; Lean and Smyth, 2013, 2014a,b,c) There is no clear consensus in the literature on the unit root properties of disaggregated energy variables by energy type The findings typically depend on the type of unit root test that is employed A small number of studies have examined the unit root properties of (disaggregated) energy by sector (Apergis and Tsoumas, 2011; Aslan and Kum, 2011; Narayan et al., 2010a,b,c,d) This is important because stationarity of energy variables might vary according to the sector in which they are involved There are, however, not enough studies yet for a consensus to emerge on this issue 2.3 State-of-the-art We distinguish between univariate and panel data With univariate data, the state-of-the-art is fractional integration tests, which accommodate seasonality (in high frequency data) and structural breaks, applied to disaggregated energy by type, preferably at the sector level (see e.g Apergis and Tsoumas, 2012; Barros et al., 2013) The fractional integration tests allow for differing degrees of persistence while employing disaggregated energy by type and sector allows for the fact that different types of energy might exhibit different levels of persistence and different sectors depend differently on energy With high frequency data, it is important to also account for heteroskedasticity (Mishra and Smyth, 2014a,b) The Narayan and Liu (2013) unit root test accommodates heteroskedasticity and structural breaks, but assumes a I(0)/I(1) dichotomy, which does not test for fractional integration Moreover, the Narayan and Liu (2013) test requires seasonality to be first filtered out of the data There are no unit root tests which simultaneously address fractional integration, structural break seasonality and heteroskedasticity in high frequency energy data With panel data, the state-of-the-art are panel unit root and stationarity tests with structural breaks, again applied to disaggregated energy by type and sector The literature has not generally applied panel fractional integration tests; an exception is Lean and Smyth (2009) 2.4 Recommendations for future research Most studies are large multi-country studies Of studies, which focus on a single country, most focus on various aspects of energy consumption or production in the United States This is particularly so for extant studies of the integration properties of disaggregated energy by type Thus, we know quite a lot about the unit root properties of various types of disaggregated energy consumption in the United States, but much less so for other countries One suggestion for future research is to examine the unit root properties of disaggregated energy for countries other than the United States, along the lines of Lean and Smyth (2014b) for Malaysia The main advantage of this line of research is that one is able to explore any heterogeneity regarding the importance of different energy types A second feature of the existing literature is that most studies are at the national level There are few studies which use data at the state level, restricted to Australia and the United States (Apergis et al., 2010a,b; Aslan, 2011; Narayan et al., 2010a,b,c,d) Future research is needed to examine differences in the integration properties of (disaggregated) energy at the state level There are advantages in exploiting sub-national data; using sub-national data allows one to address potential heterogeneity in the behavior of energy consumption across states (see Smyth, 2013) Another direction for future research in this area is to apply unit root tests to examine convergence in energy consumption Meng et al (2013), who examine convergence in energy consumption per capita in OECD countries, and Mishra and Smyth (2014a), who examine convergence in energy consumption per capita among ASEAN countries, represent first steps in this direction Future research could examine convergence in energy consumption at the sector level within specific countries, convergence of specific types of energy or convergence in energy consumption among countries at different levels of economic development Finally, there can potentially be more work on the efficiency of energy futures markets; for examples of this type of work, see Narayan et al (2010a, 2010b, 2010c, 2010d) and Narayan et al (2011a) Recently, Narayan and Popp (2011b) and Costantini et al (in press) propose a seasonal unit root test that can be usefully applied to energy variables, particularly those that are sampled at the quarterly frequency In this respect, seasonal unit root tests can be usefully employed to test for efficiency of energy futures The bulk of research on energy markets focuses on spot market activities There are important advantages in also considering energy futures markets Oil producers and airlines, for instance, have significant commercial exposure to changes in the price of oil and petroleumbased fuels These investors are likely to hedge their risk by buying and selling energy derivatives Take one example A particular airline Please cite this article as: Smyth, R., Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx (2014) xxx–xxx may want to buy futures or options to avoid the risk associated with increases in fuel prices This means that understanding the statistical properties of energy futures is imperative Cointegration, Granger causality and long-run estimation 3.1 Motivation and implications The literature on the relationship between energy and GDP, originating with Kraft and Kraft (1978), is motivated by the close perceived relationship between the two variables Much of the literature has tested four competing hypotheses, which have important policy implications These are (a) the conservation hypothesis — unidirectional Granger causality running from GDP to energy; (b) the growth hypothesis — unidirectional Granger causality running from energy to GDP; (c) the feedback hypothesis — bilateral Granger causality between energy and GDP; and (d) the neutrality hypothesis — energy and GDP are independent The literature has adopted progressively more advanced econometric methods Mehrara (2007) identifies four generations of studies Coers and Sanders (2013) suggest that there is now a fifth generation of studies There is, however, no consensus about the existence of a long-run relationship and direction of causality (Ozturk, 2010; Payne, 2010) This uncertainty has been a catalyst for further studies using even more recent methods As an extension of cointegration testing, studies have estimated the long-run elasticities Typically, such studies have examined the longrun effect (elasticity) of income with respect to energy indicators and the long-run energy elasticity with respect to income; see Narayan et al (2010a, 2010b, 2010c, 2010d) and the references therein 3.2 Overview of existing studies Much of the early literature examined the causal relationship between energy and GDP within a bivariate framework The problem with this approach is that omitted variables can lead to the wrong conclusions about causal inference (Lutkepohl, 1982) Consequently, the subsequent literature has examined the causal relationship between energy and GDP within a multivariate setting, adding one or more variables in addition to energy and GDP Apergis and Tang (2013) find that models with three or more variables are more likely to support the growth hypothesis than studies employing a bivariate model The problem with just adding random third or fourth variables, however, is that the choice of additional variables is ad hoc without any underpinning theoretical framework Stern (1993, 2000) was the first to highlight the potential complementarities between energy and other inputs (capital and labor) in the production process The use of an augmented production function framework in which GDP is treated as a function of capital, labor and energy consumption has become the norm in the literature An alternative to including labor in the production function is to normalize the production function by labor/population and specify it in per capita terms (see e.g Lee et al., 2008; Liddle, 2013; Narayan and Smyth, 2008) In addition to examining the relationship between energy and GDP, many studies examine the relationship between energy, GDP and a third and, sometimes, a fourth variable Most of these studies now employ an augmented production function model in which, in addition to capital, labor and energy, the third (or fourth) variable is included on the right-hand side One set of studies examines the relationship between energy, urbanization and GDP (see e.g Liddle, 2013; Liu and Xie, 2013; Mishra et al, 2009a; Sadorsky, 2013; Wang, 2014) A second set of studies examines the relationship between energy, financial development and GDP (see e.g Coban and Topcu, 2013; Jalil and Feridun, 2011; Shahbaz and Lean, 2012; Sadorsky, 2010, 2011a,b) A third set of studies examine the relationship between energy, GDP and trade (see e.g Aissa et al., 2014; Farhani et al., 2014; Lean and Smyth, 2010; Narayan and Smyth, 2009; Nasreen and Anwar, 2014; Sadorsky, 2011a, 2012) Shahbaz et al (2013) examine the relationship between energy consumption, financial development, GDP and trade in China within one empirical framework Traditionally, electricity has been classified as a non-traded good, produced and consumed within the country of origin It is only recently that electricity has been traded between countries (Srinivasan, 2013) Lean and Smyth (2014a) extend the third group of studies to examine the contribution of Bhutanese hydroelectricity exports to India on economic growth in Bhutan within a production function framework Bhutan is an interesting country to examine the effect of exports of electricity on GDP because Bhutan not only trades electricity with India, but electricity represents Bhutan's major export Other studies extend the modeling framework in other directions, such as the relationship between energy consumption, GDP and foreign direct investment (Omri and Kahouli, 2014), energy consumption, political instability and tourism (Tang and Abosedra, 2014) The existence of multiple structural breaks in energy variables not only affects the stationarity of energy variables, but also the cointegration relation between energy and non-energy variables and the performance of forecasting Ignoring the existence of structural breaks can result in unreliable estimates For this reason, studies have examined how multiple structural breaks affect the manner in which news impacts on volatility in crude oil markets (Mensi et al., 2014) and how multiple structural breaks affect the relationship between spot and futures oil prices (Chen et al., 2014) In a related study, Arouri et al (2012) examine how multiple structural breaks affect forecasts of oil spot and futures prices A feature of many of these studies has been the use of methods, such as those proposed by Bai and Perron (2003) and Kejriwal and Perron (2010), to pinpoint the location of the structural breaks and then identify the factors determining those structural breaks Typically the structural breaks in energy variables have been linked to world economic events affecting energy markets For example, considering the period 1986 to 2011, Arouri et al (2012) identifies breaks in nine returns on oil spot and futures as linked to variously, the Latin American crisis, Asian financial crisis, the First and Second Gulf War, OPEC overproduction and the Global financial crisis Noguera (2013) uses the Kejriwal and Perron (2010) procedure to locate structural breaks in oil prices over a much longer period (1861–2011) The structural breaks identified in that study coincided with the 1890s and 1930s Depressions, the World Wars, the OPEC oil crisis, the Islamic Revolution in Iran and the Asian and Global financial crises Linear Granger causality assumes that the parameters of the model are constant over time One problem with using linear causality tests is that myriad factors, including economic events, environmental change, changes in energy policies and fluctuations in oil prices can result in structural changes in energy consumption for a given timeframe being studied This creates potential for a nonlinear relationship between energy consumption and economic growth or other relevant variables such as between crude oil spot and futures prices or oil prices and economic growth (Chiou-Wei et al., 2008; Lee and Chang, 2007; Yang et al., 2010) Linear causality tests cannot uncover nonlinear predictive power (Chiou-Wei et al., 2008; Hiemstra and Jones, 1994) A small number of studies have tested for nonlinear causality between (dis)aggregated energy consumption and economic growth (Chiou-Wei et al., 2008; Dergiades et al., 2013; Fallahi, 2011; Lee and Chang, 2007; Salamaliki and Venetis, 2013; Yang et al., 2010; Yildirim et al., 2014), between spot and futures oil prices (Berikos and Diks, 2008; Chen et al., 2014; Lu et al., 2014) or oil price changes and economic growth (Hamilton, 2003) Some of these studies have applied the Hiemstra and Jones (1994) approach to nonlinear causality (see e.g Chiou-Wei et al., 2008), but a limitation of this method is that it is not applicable to non-stationary data (Hassani et al., 2010) Other studies have used more recent approaches to nonlinear causality, such as the method proposed by Di Iorio and Triacca (2013), which is applicable irrespective of the stationarity of the variables (see e.g Yildirim et al., 2014) A very small literature has applied nonlinear panel cointegration and causality tests to examine the relationship between energy Please cite this article as: Smyth, R., Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx (2014) xxx–xxx consumption and economic growth (Huang et al., 2008; Omay et al., 2012) Overall, the findings from these studies are mixed, although one conclusion that emerges is that there is evidence that the pattern of causality between energy consumption and other variables changes over time (Fallahi, 2011; Huang et al., 2008; Lee and Chang, 2007; Lu et al., 2014; Salamaliki and Venetis, 2013) In terms of single country versus panel studies, there are a multitude of single country studies (see Apergis and Payne, 2011a,b, Table 1; Ozturk, 2010; Payne, 2010) Many of these studies have been plagued by only having relatively short time-series A common approach to address the short time-series has been to employ the autoregressive distributed lag (ARDL) bounds test approach suggested by Pesaran et al (2001) with the small sample critical values tabulated in Narayan (2005) (see e.g Baranzini et al., 2013; Chandran et al., 2010; Fuinhas and Marques, 2012; Odhiambo, 2009; Sari et al., 2008; Shahbaz et al., 2012; Tang, 2008; Wolde-Rufael, 2010) The results from studies for individual countries, however, have been divergent These differences reflect, inter alia, differences in econometric approaches, institutional characteristics in specific countries, model specification, variable selection and time period (Apergis and Payne, 2011a, b; Apergis and Tang, 2013; Ozturk, 2010; Payne, 2010) Stern and Enflo (2013) note that relatively small samples produce sampling variability To address this issue, Stern and Enflo (2013) and Vaona (2012) use a long time-series (around 150 years) for Sweden and Italy, respectively, and find that the relationship between energy and GDP has changed over time For most countries, a very long time-series is not available Thus, an alternative way to address the short time-series for individual countries has been to employ panel data models Mehrara's ‘fourth generation’ models, published from around 2003 onwards, analyzed the energy GDP relationship using (bivariate) panel cointegration and panel error correction models (see e.g Lee, 2005; Soytas and Sari, 2003, 2006) Coers and Sanders' (2013) so-called ‘fifth generation’ models, published from 2007 to 2008 onwards, use panel VECM models, which also control for capital-energy complementarities and estimate elasticities via methods, such as panel fully modified OLS (FMOLS) and panel dynamic OLS (DOLS) (see e.g Apergis and Payne, 2009a,b, 2010a, 2011a,b; Coers and Sanders, 2013; Lee and Chang, 2008; Lee et al., 2008; Liddle, 2012, 2013; Narayan and Smyth, 2008; Sadorsky, 2011a, 2012) This literature attempts to address structural breaks in the cointegrating vector (see e.g Narayan and Smyth, 2008) and cross-sectional dependence in the cointegration test (see e.g Liddle, 2013) The long-run energy elasticity has generally been in the range 0.1 to 0.4 A limitation with using aggregate energy data to examine the energy-GDP nexus is that aggregate energy does not reflect the extent to which different countries rely on different energy resources (Yang, 2000) Thus, finding, or failing to find, a relationship between aggregated energy consumption and economic growth, might mask nuanced relationships between specific energy types and economic growth Beginning with Yang (2000) there are few studies that consider the relationship between energy consumption, disaggregated by type, and economic growth (Alkhathlan and Javid, 2013; Bowden and Payne, 2009; Ewing et al, 2007; Hu and Lin, 2008; Payne, 2009; Sari et al, 2008; Tsani, 2010; Wolde-Rufael, 2004; Yang, 2000; Yuan et al, 2008; Ziramba, 2009) Some studies have examined the relationship between specific fossil fuels, such as natural gas (Farhani et al., 2014) and oil (Chu and Chang, 2012) and GDP There is a large literature examining the relationship between electricity consumption and GDP (see Payne, 2010 for a review) A few studies have applied an augmented production function to analyze the relationship between nuclear or renewable energy consumption and energy growth (see e.g Aissa et al., 2014; Apergis and Payne, 2010b,c, 2011a, 2012; Chu and Chang, 2012; Jebli and Youssef, 2013; Ohler and Fetters, 2014; Pao and Fu, 2013; Wolde-Rufael, 2010; Wolde-Rufael and Menyah, 2010) Most of these studies have focused on aggregate renewable energy consumption, although Ohler and Fetters (2014) differentiate between different types of renewable energy Another group of studies has analyzed the relationship between carbon dioxide emissions, economic growth and renewable energy (see e.g Sadorsky, 2009; Apergis and Payne, 2014) Each of these studies suggests that renewable energy consumption plays a role in increasing economic growth The elasticity of renewable energy varies from Lean and Smyth's (2014a) results for hydroelectricity in Bhutan (0.03–0.05%), Wolde-Rufael's (2010) results for nuclear energy in India (0.04–0.06%), Aissa et al.'s (2014) panel results for Africa (0.03%) and Jebli and Youssef's (2013) panel results for 69 countries (0.04%) through panel results for Central America, Eurasia and the OECD in the range 0.20% to 0.76% (see e.g Apergis and Payne, 2010b, 2010c 2011a, 2012) Studies that have included both renewable and non-renewable energy in the same specification, have found that the elasticity of renewable energy is similar to that of non-renewable energy (see e.g Apergis and Payne, 2012) In studies, which have also included a trade variable, there is also evidence of an indirect causal relationship between renewable and non-renewable energy, which runs through trade, suggesting that more trade openness can reduce the share of non-renewable energy in total energy (Aissa et al., 2014; Jebli and Youssef, 2013) Overall, in terms of energy policy, these findings suggest that increasing the share of renewable energy in total energy consumption can be effective in reducing greenhouse emissions, without a detrimental effect on economic growth There are also a few studies which have compared the relationship between both aggregate and disaggregate energy consumption and economic growth in alternative specifications (Alkhathlan and Javid, 2013; Wolde-Rufael, 2004; Yang, 2000; Yuan et al., 2008; Zhang and Yang, 2013) Other papers look at the relationship between disaggregated energy consumption and alternatives to GDP, such as industrial production (Sari et al, 2008) There are, however, very few studies which have examined the relationship between economic growth and disaggregated data on various energy types, using an augmented production function model approach (see e.g Liddle, 2013; Pao and Fu, 2013; Soytas and Sari, 2007) There is no clear consensus at this point about the manner in which elasticities for specific types of energy differ, particularly allowing for energy-capital complementarities One problem is that the results might be confounded because specific energy types might not be sufficiently independent of total energy (Bruns and Gross, 2013) In terms of geographic focus, the majority of studies have focused on developed and industrialized countries, reflecting the availability of reliable data (Payne, 2010) Apergis and Tang (2013) find that support for the competing hypotheses concerning Granger causality is linked to the level of economic development with more support for the growth hypothesis at higher levels of economic development Most single country studies have employed data at the national level, although there are a limited number of studies which have employed sub-national data, particularly for China (see e.g Akkemik et al., 2012; Herrerias et al, 2013; Zhang and Xu, 2012) The results are not universal, but provide more support for the conservation hypothesis in China, particularly in the industrialized coastal seaboard provinces Overall, the findings for China at least, suggest that taking a regional perspective is useful 3.3 State-of-the-art Several conclusions emerge regarding the current state-of-the-art with respect to examining the energy-GDP relationship.3 First, models should be multivariate to avoid omitted variable bias and should preferably employ an augmented production function that accommodates complementarities between energy and other inputs However, in circumstances in which only a short span of data is available and, for the purposes of drawing policy implications, the focus has to be on In recent reviews, Payne (2010) and Liddle (2012) outline several points of consensus regarding state-of-the-art and this section borrows several points from their reviews Please cite this article as: Smyth, R., Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx (2014) xxx–xxx individual countries, there is a trade-off The trade-off is between employing a bivariate framework, which potentially results in omitted variable bias, and a multivariate model, which potentially results in over-parameterizing the model and loss of degrees of freedom Second, unless it is necessary to use data for individual countries to derive policy conclusions for those countries, models should employ panel data to improve the power of unit root and cointegration tests Third, when using data for single countries, a long time span is preferable Stern and Enflo (2013) and Vaona (2012) set the gold standard in this regard, although in most cases 150 years of data will not be available Fourth, rather than just presenting the Granger causality results, the magnitude, sign and significance of the long-run elasticities should be calculated Fifth, energy data should be disaggregated by type and preferably sector to account for differences in energy intensity Studies should compare the results for aggregated and disaggregated energy consumption data Sixth, energy consumption data should be adjusted for the quality of the energy source (Liddle, 2012; Stern, 1993, 2000) Seventh, the GDP data should be adjusted to accommodate the unrecorded economy (Ozturk, 2010) Eighth, attention should be paid to identifying how results differ according to the level of economic development For multi-country results, Apergis and Tang (2013) use a logistic modeling approach, which presents a path forward in this respect 3.4 Recommendations for future research There are, we believe, three directions for future work with respect to cointegration, Granger causality, and long-run estimates of the effect of non-energy variables (say GDP) on energy variables or vice versa First, not much is known about whether cointegration between energy and non-energy variables is regime-dependent In other words, is cointegration restricted to certain phases of the economic cycle? Similarly, if cointegration is indeed found to be regime-dependent, the question becomes whether the long-run effects and indeed Granger causality are also regime-dependent This modeling approach also leaves open the prospect that there may well be certain regimes in which the relationship (either in terms of cointegration, Granger causality or long-run effects) may be stronger than in other regimes This finding can have implications for energy policy and planning As much as this question is interesting, it will require time-series data that is much longer than what much of the literature has considered This is the challenge, but it is one that should be taken Second, there is limited knowledge of whether cointegration, Granger causality, and long-run estimates are time-varying Time-varying cointegration, Granger causality, and long-run estimation models should be considered Third, there is limited research on whether energy variables can forecast non-energy variables (see Narayan et al., 2014a,b,c) and whether energy variables themselves can be forecasted (Hayat and Narayan, 2010) On forecasting, recent developments are in applications of both time-series data (Makin et al., 2014; Narayan et al., 2014a, b; Westerlund and Narayan, 2012, 2014a) and panel data (Narayan et al., 2013; Westerlund and Narayan, 2014a,b,c) The main advantage of these forecasting and predictability models is that they allow one to model predictors that are persistent, endogenous and heteroskedastic This is advantageous because, at least, high frequency data (such as daily, weekly, and monthly data) are likely to be characterized by these statistical features (Narayan et al., 2014a,b,c) Additional challenges may be modeling structural breaks and data nonlinearities within existing empirical frameworks Future research, at least on the methodological side, will find these issues motivating The final issue relates to small sample performance of these predictive regression models They are typically developed for large sample sizes (T), such as cases in which T N 100 To be useful in other fields, such as energy economics, where T is almost always less than 100, recently Westerlund and Narayan (2014b) have developed a panel data predictive regression model This model is aided by a predictive regression model where the cross-section can be as large as the data allows, but T can be as small as two observations (see Westerlund and Narayan, 2014c) Together, these panel data predictive regression models pave the way for exciting applications using energy data Conclusions The goal of this paper was to review the literature on two popular strands of literature in energy economics; namely, unit root testing and cointegration, Granger causality, and long-run estimation—that have been motivated by recent developments in applied econometrics This is important because despite what seems to be a consensus on findings from these two strands of the literature, more applied papers continue to be archived The question is what additional things we learn from these additional applications? We review this literature and discuss a range of new directions for research We find that both literatures have more or less reached consensus The unit root literature, for example, has concluded that when the state-of-the-art econometric techniques are used energy variables are stationary Similarly, the cointegration literature almost always finds that energy variables share a long-run relationship with non-energy variables This, therefore, seems to suggest that the evidence that there is cointegration is a stylized fact of the literature.4 The same, however, cannot be claimed with respect to the evidence on Granger causality, for which findings are mixed These mixed findings reflect differences in econometric approaches and model specification, among other things One particular issue is the trade-off between omitted variable bias and over parameterization in bivariate and multivariate frameworks respectively with short spans of data While a panel data model seems to offer a solution to biases resulting from omitted variable(s) and over-parameterization, the cost is high when the focus of research is on a single country, as opposed to a group of countries We not take a particular stand on this, leaving to applied researchers the decision to choose the model, conditional on the research question References Agnolucci, P., Venn, A., 2011 Industrial energy intensities in the UK: is there a deterministic or stochastic difference among sectors? Appl Econ 43, 1447–1462 Aissa, M.S.B., Jebli, M.B., Yousef, S.B., 2014 Output, renewable energy consumption and trade in Africa Energy Policy 66, 11–18 Akkemik, K.A., Goksal, K., Li, J., 2012 Energy consumption and income in Chinese provinces Heterogeneous panel causality analysis Appl Energy 99, 445–454 Alkhathlan, K., Javid, K., 2013 Energy consumption, carbon emissions and economic growth in Saudi Arabia: an aggregate and disaggregate analysis Energy Policy 62, 1525–1532 Apergis, N., Payne, J.E., 2009a Energy consumption and economic growth in Central America: evidence from a panel cointegration and error-correction model Energy Econ 31, 641–647 Apergis, N., Payne, J.E., 2009b Energy consumption and economic growth: evidence from the Commonwealth of Independent States Energy Econ 32, 1421–1426 Apergis, N., Payne, J.E., 2010a Structural breaks and petroleum consumption in US states: are shocks transitory or permanent? Energy Policy 38, 6375–6378 Apergis, N., Payne, J.E., 2010b Energy consumption and growth in South America: evidence from a panel error correction model Energy Econ 32, 1421–1426 Apergis, N., Payne, J.E., 2010c Renewable energy consumption and growth in Eurasia Energy Econ 32, 1392–1397 The two consensus might seem contradictory given that many studies first test for a unit root before proceeding to test for cointegration The apparent contradiction can be overcome if one recognizes two points First, the literature focused on just testing for a unit root with (univariate) time series has typically only found that energy variables are stationary when allowing for multiple structural breaks Many of the studies that pretest energy variables for stationarity before proceeding to test for cointegration and conclude that energy variables are integrated of order (I(1)), not allow for structural breaks in the unit root test Second, those studies which pretest for stationarity using unit root tests with structural breaks and conclude that one or more variables are integrated of order zero (I(0)), typically test for cointegration using methods such as the ARDL bounds test, which not depend on whether the variables are I(0) or I(1) It is also important to recognize that the energy-GDP literature is not a cointegration literature per se There are other methods (such as the Toda-Yamamoto procedure) that study the same relationship, but not specifically test, or estimate, cointegrating relationships Please cite this article as: Smyth, R., Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx (2014) xxx–xxx Apergis, N., Payne, J.E., 2010d Renewable energy consumption and economic growth: evidence from a panel of OECD countries Energy Policy 38, 656–666 Apergis, N., Payne, J.E., 2011a A dynamic panel study of economic development and the electricity consumption-growth nexus Energy Econ 33, 770–781 Apergis, N., Payne, J.E., 2011b The renewable energy consumption-growth nexus in Central America Appl Energy 88, 343–347 Apergis, N., Payne, J.E., 2012 Renewable and non-renewable energy consumptiongrowth nexus: evidence from a panel error correction model Energy Econ 34, 733–738 Apergis, N., Payne, J.E., 2014 Renewable energy, output, CO2 emissions and fossil fuel prices in Central America: evidence from a nonlinear panel smooth transition vector error correction model Energy Econ 42, 226–232 Apergis, N., Tang, C.F., 2013 Is the energy-led growth hypothesis valid? New evidence from a sample of 85 countries Energy Econ 38, 24–31 Apergis, N., Tsoumas, C., 2011 Integration properties of disaggregated solar, geothermal and biomass energy consumption in the US Energy Policy 39, 5474–5479 Apergis, N., Tsoumas, C., 2012 Long memory and disaggregated energy consumption: evidence from fossil fuels, coal and electricity retail in the US Energy Econ 34, 1082–1087 Apergis, N., Loomis, D., Payne, J.E., 2010a Are fluctuations in coal consumption transitory or permanent? Evidence from a panel of US states Appl Energy 87, 2424–2426 Apergis, N., Loomis, D., Payne, J.E., 2010b Are shocks to natural gas consumption temporary or permanent? Evidence from a panel of US states Energy Policy 38, 4734–4736 Arouri, M.E.H., Lahiani, A., Levy, A., Nguyen, D.K., 2012 Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models Energy Econ 34, 283–293 Aslan, A., 2011 Does natural gas consumption follow a nonlinear path over time? Evidence from 50 states Renew Sust Energ Rev 15, 4466–4469 Aslan, A., Kum, H., 2011 The stationarity of energy consumption for Turkish disaggregate data by employing linear and nonlinear unit root tests Energy 36, 4256–4258 Bai, J., Perron, P., 2003 Computation and analysis of multiple structural change models J Appl Econ 18, 1–22 Baranzini, A., Weber, S., Bareit, M., Mathys, N., 2013 The causal relationship between energy use and economic growth in Switzerland Energy Econ 36, 464–470 Barros, C.P., Gil-Alana, L.A., Payne, J.E., 2011 An analysis of oil production by OPEC countries: persistence, breaks and outliers Energy Policy 39, 442–453 Barros, C.P., Gil-Alana, L.A., Payne, J.E., 2012 Evidence of long memory behaviour in US renewable energy consumption Energy Policy 41, 822–826 Barros, C.P., Gil-Alana, L.A., Payne, J.E., 2013 U.S disaggregated renewable energy consumption: persistence and long memory behavior Energy Econ 40, 425–432 Berikos, S.D., Diks, C.G.H., 2008 The relationship between crude oil spot and futures prices: cointegration, linear and nonlinear causality Energy Econ 30, 2673–2685 Bowden, N., Payne, N., 2009 The causal relationship between US energy consumption and real output: a disaggregated analysis J Policy Model 31, 180–188 Bruns, S.B., Gross, C., 2013 What if energy time series are not independent? Implications for energy-GDP causality testing Energy Econ 40, 753–759 Carrion-i-Silvestre, J.L., Del Barrio-Castro, T., Lopez-Bazo, E., 2005 Breaking the panels: an application to GDP per capita Econ J 8, 159–175 Chandran, V.G.R., Sharma, S., Madhaven, K., 2010 Electricity consumption-growth nexus: the case of Malaysia Energy Policy 38, 606–612 Chen, P.F., Lee, C.C., 2007 Is energy consumption per capita broken stationary? New evidence from regional-based panels Energy Policy 35, 3526–3540 Chen, P.F., Lee, C.C., Zeng, J.H., 2014 The relationship between spot and futures oil prices: structural breaks matter? Energy Econ 43, 206–217 Chiou-Wei, S.Z., Chen, C.F., Zhu, Z., 2008 Economic growth and energy consumption revisited — evidence from linear and nonlinear Granger causality Energy Econ 30, 3063–3076 Chu, H.P., Chang, T., 2012 Nuclear energy consumption, oil consumption and economic growth in G-6 countries: bootstrap panel causality tests Energy Policy 48, 762–769 Coban, S., Topcu, M., 2013 The nexus between financial development and energy consumption in the EU: a dynamic panel data analysis Energy Econ 39, 81–88 Coers, R., Sanders, M., 2013 The energy GDP nexus: addressing an old question with new methods Energy Econ 36, 708–715 Congregado, E., Golpe, A.A., Carmano, M., 2012 Looking for hysteresis in coal consumption in the US Renew Sust Energ Rev 16, 3339–3343 Costantini, M., Narayan, P.K., Popp, S., Westerlund, J., 2014 Small-sample improved seasonal unit root tests for trending and breaking series Commun Stat Simul Comput (In Press) Dergiades, T., Martinopoulos, G., Tsoulfidis, L., 2013 Energy consumption and economic growth: parametric and non-parametric causality testing for the case of Greece Energy Econ 36, 686–697 Di Iorio, F., Triacca, U., 2013 Testing for Granger non-causality using the autoregressive metric Econ Model 33, 120–125 Ewing, B.T., Sari, R., Soytas, U., 2007 Disaggregate energy consumption and industrial output in the United States Energy Policy 35, 1274–1281 Fallahi, F., 2011 Causal relationship between energy consumption and GDP: a Markovswitching causality Energy 36, 4165–4170 Farhani, S., Shahbaz, M., Arouri, M., Teulon, F., 2014 The role of natural gas consumption and trade in Tunisia's output Energy Policy 66, 677–684 Fuinhas, J.A., Marques, A.C., 2012 Energy consumption and economic growth nexus in Portugal, Italy, Greece, Spain and Turkey: an ARDL bounds test approach (1965– 2009) Energy Econ 34, 511–517 Gil-Alana, L.A., Loomis, D., Payne, J.E., 2010 Does energy consumption by the US electric power sector exhibit long memory behaviour? Energy Policy 38, 7512–7518 Golpe, A.A., Carmona, M., Congregado, E., 2012 Persistence in natural gas consumption in the US An unobserved components model Energy Policy 46, 594–600 Hamilton, J., 2003 What is an oil price shock? J Econ 113, 363–398 Hasanov, M., Telatar, E., 2011 A re-examination of stationarity of energy consumption: evidence from new unit root tests Energy Policy 39, 7726–7738 Hassani, H., Zhigljavsky, A., Patterson, K., Soofi, A., 2010 A comprehensive causality test based on the singular spectrum analysis In: Illari, P.M., Russo, F., Williamson, J (Eds.), Causality in Science Oxford University Press, Oxford Hayat, A., Narayan, P.K., 2010 The Oil Stock Fluctuations in the United States, Appl Energy 87, 178–184 Herrerias, M.J., Joyeux, R., Giradin, E., 2013 Short-and-long run causality between energy consumption and economic growth: evidence across regions in China Appl Energy 112, 1483–1492 Hiemstra, C., Jones, J.-D., 1994 Testing for linear and nonlinear Granger causality in the stock price-volume relationship J Financ 49, 1639–1654 Hsu, Y.C., Lee, C.C., Lee, C.C., 2008 Revisited: are shocks to energy consumption permanent or temporary? New evidence from a panel SURADF approach Energy Econ 30, 2314–2330 Hu, J.L., Lin, C.H., 2008 Disaggregated energy consumption and GDP in Taiwan: a threshold cointegration analysis Energy Econ 30, 2342–2358 Huang, B.N., Hwang, M.J., Yang, C.W., 2008 Causal relationship between energy consumption and GDP growth revisited: a dynamic panel data approach Ecol Econ 67, 41–54 Im, K.S., Lee, J., Tieslau, M., 2005 Panel LM unit root tests with level shifts Oxf Bull Econ Stat 67, 393–419 Jalil, A., Feridun, M., 2011 The impact of growth, energy and financial development on the environment in China: a cointegration analysis Energy Econ 33, 284–291 Jebli, M.B., Youssef, S.B., 2013 Output, renewable and non-renewable energy consumption and international trade: evidence from a panel of 69 countries MPRA Paper No 51199 Karanfil, F., 2009 How many times again will we examine the energy-income nexus using a limited range of traditional econometric tools? Energy Policy 37, 1191–1194 Kejriwal, M., Perron, P., 2010 Testing for multiple structural changes in cointegrated regression models J Bus Econ Stat 28, 503–522 Kraft, J., Kraft, A., 1978 On the relationship between energy and GNP J Energy Dev 3, 401–403 Lean, H.H., Smyth, R., 2009 Long memory in US disaggregated petroleum consumption: evidence from univariate and multivariate LM tests for fractional integration Energy Policy 37, 3205–3211 Lean, H.H., Smyth, R., 2010 On the dynamics of aggregate output, electricity consumption and exports in Malaysia: evidence from multivariate Granger causality tests Appl Energy 87, 1963–1971 Lean, H.H., Smyth, R., 2013 Are fluctuations in US production of renewable energy permanent or transitory? Appl Energy 101, 483–488 Lean, H.H., Smyth, R., 2014a Will initiatives to promote hydroelectricity consumption be effective? Evidence from univariate and panel LM unit root tests with structural breaks Energy Policy 68, 102–115 Lean, H.H., Smyth, R., 2014b Electricity consumption, output and trade in Bhutan Working Paper Asian Development Bank, Manila Lean, H.H., Smyth, R., 2014c Are shocks to disaggregated energy consumption in Malaysia permanent or temporary? Evidence from LM unit root tests with structural breaks Renew Sust Energ Rev 31, 319–328 Lee, C., 2005 Energy consumption and GDP in developing countries: a cointegrated panel analysis Energy Econ 27, 415–427 Lee, C., Chang, C., 2007 The impact of energy consumption on economic growth: evidence from linear and nonlinear models in Taiwan Energy 32, 2282–2294 Lee, C., Chang, C., 2008 Energy consumption and economic growth in Asian economies: a more comprehensive analysis using panel data Resour Energy Econ 30, 50–69 Lee, J., Strazicich, M.C., 2003 Minimum Lagrange multiplier unit root test with two structural breaks Rev Econ Stat 85, 1082–1089 Lee, C., Chang, C., Chen, P., 2008 Energy income causality in OECD countries revisited: the key role of capital stock Energy Econ 30, 2359–2373 Liddle, B., 2012 The importance of energy quality in energy intensive manufacturing: evidence from panel cointegration and panel FMOLS Energy Econ 34, 1819–1825 Liddle, B., 2013 The energy, economic growth, urbanization nexus across development: evidence from heterogeneous panel estimates robust to cross-sectional dependence Energy J 34, 223–244 Liu, Y., Xie, Y., 2013 Asymmetric adjustment of the dynamic relationship between energy intensity and urbanization in China Energy Econ 36, 43–54 Lu, F.B., Hong, Y.M., Wang, S.Y., Kin, K.K., Liu, J., 2014 Time-varying Granger causality tests for application in global crude oil markets Energy Econ 42, 289–298 Lutkepohl, H., 1982 Non-causality due to omitted variables J Econ 19, 367–378 Makin, A.J., Narayan, P.K., Narayan, S., 2014 What expenditure does Anglosphere foreign borrowing fund? J Int Money Financ 40, 63–78 Maslyuk, S., Dharmaratna, D., 2013 Impact of shocks on Australian coal mining In: Leal, W., Voudouris, V (Eds.), Global Energy Policy and Security Springer, Berlin Maslyuk, S., Smyth, R., 2009 Non-linear unit root properties of crude oil production Energy Econ 31, 109–118 Mehrara, M., 2007 Energy consumption and economic growth: the case of oil exporting countries Energy Policy 35, 2939–2945 Meng, M., Payne, J.E., Lee, J., 2013 Convergence in per capita energy use among OECD countries Energy Econ 36, 536–545 Mensi, W., Hammoudeh, S., Yoon, S.M., 2014 How OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process Energy Econ 42, 343–354 Please cite this article as: Smyth, R., Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx (2014) xxx–xxx Mishra, V., Smyth, R., 2014a Is monthly US natural gas consumption stationary? New evidence from a GARCH unit root test with structural breaks Energy Policy 69, 258–262 Mishra, V., Smyth, R., 2014b Convergence in energy consumption per capita among ASEAN countries Energy Policy 73 180-185 Mishra, V., Sharma, S., Smyth, R., 2009a Are fluctuations in energy consumption per capita transitory? Evidence from a panel of Pacific Island countries Energy Policy 37, 2318–2326 Mishra, V., Sharma, S., Smyth, R., 2009b The energy-GDP nexus: evidence from a panel of Pacific Island countries Resour Energy Econ 31, 210–220 Narayan, P.K., 2005 The saving and investment nexus for China: evidence from unit roots, cointegration and Granger causality Appl Econ 37, 1979–1990 Narayan, P.K., Liu, R., 2013 New evidence on the weak-form efficient market hypothesis Working Paper Centre for Financial Econometrics, Deakin University Narayan, P.K., Popp, S., 2010 A new unit root test with two structural breaks in level and slope at unknown time J Appl Stat 37, 1425–1438 Narayan, P.K., Popp, S., 2012 The energy consumption-real GDP nexus revisited: empirical evidence from 93 countries Econ Model 29, 303–308 Narayan, P.K., Smyth, R., 2007 Are shocks to energy consumption permanent or temporary? Evidence from 182 countries Energy Policy 35, 333–341 Narayan, P.K., Smyth, R., 2008 Energy consumption and real GDP in G7 countries: new evidence from panel cointegration with structural breaks Energy Econ 30, 2331–2341 Narayan, P.K., Smyth, R., 2009 Multivariate granger causality between electricity consumption, exports and GDP: evidence from a panel of Middle Eastern countries Energy Policy 39, 229–236 Narayan, P.K., Narayan, S., Smyth, R., 2008 Are oil shocks permanent or temporary? Panel data evidence from crude oil and NGL production in 60 countries Energy Econ 30, 919–936 Narayan, P.K., Narayan, S., Popp, S., 2010a Energy consumption at the state level: the unit root null hypothesis from Australia Appl Energy 87, 1953–1962 Narayan, P.K., Narayan, S., Popp, S., 2010b Does electricity consumption Granger cause GDP? New global evidence Appl Energy 87, 3294–3298 Narayan, P.K., Narayan, S., Popp, S., 2010c A note on the long-run elasticities from the energy consumption-GDP relationship Appl Energy 87, 1054–1057 Narayan, P.K., Narayan, S., Zheng, X., 2010d Gold and oil futures markets: are markets efficient? Appl Energy 87, 3299–3303 Narayan, P.K., Narayan, S., Popp, S., 2011a Investigating price clustering in the oil futures market Appl Energy 88, 397–402 Narayan, P.K., Popp, S., 2011b An application of a new seasonal unit root test to inflation Int Rev Econ Financ 20 (4), 707–716 Narayan, P.K., Sharma, S.S., Bannigidadmath, D., 2013 Does tourism predict macroeconomic performance in Pacific Island Countries? Econ Model 33, 780–786 Narayan, P.K., Sharma, S., Poon, W.C., Westerlund, J., 2014a Do oil prices predict economic growth? New global evidence Energy Econ 41, 137–146 Narayan, P.K., Narayan, S., Prabheesh, P.K., 2014b Stock returns, mutual fund flows and spillover shocks Pac Basin Financ J 29, 146–162 Narayan, P.K., Narayan, S., Thuraisamy, K., 2014c Can institutions and macroeconomic factors predict stock returns in emerging markets? Emerg Mark Rev 19, 77–95 Nasreen, S., Anwar, S., 2014 Causal relationship between trade openness, economic growth and energy consumption: a panel data analysis of Asian countries Energy Policy 69, 82–91 Noguera, J., 2013 Oil prices: breaks and trends Energy Econ 37, 60–67 Odhiambo, N., 2009 Energy consumption and economic growth nexus in Tanzania: an ARDL bounds testing approach Energy Policy 37, 617–622 Ohler, A., Fetters, I., 2014 The causal relationship between renewable electricity generation and GDP growth: a study of energy sources Energy Econ 43, 125–139 Omay, T., Hasanov, M., Ucar, N., 2012 Energy consumption and economic growth: evidence from nonlinear panel cointegration and causality tests MPRA Paper No 37653 Omri, A., Kahouli, B., 2014 Causal relationships between energy consumption, foreign direct investment and economic growth: fresh evidence from dynamic simultaneous equations models Energy Policy 67, 913–922 Ozturk, I., 2010 A literature survey on the energy growth nexus Energy Policy 38, 340–349 Ozturk, I., Aslan, A., 2011 Are fluctuations in energy consumption per capita transitory? Evidence from Turkey Energy Explor Exploit 29, 161–168 Pao, H.-T., Fu, H.-C., 2013 The causal relationship between energy resources and economic growth in Brazil Energy Policy 61, 793–801 Payne, J.E., 2009 On the dynamics of energy consumption and output in the US Appl Energy 86, 575–577 Payne, J., 2010 A survey of the electricity consumption-growth literature Appl Energy 87, 723–731 Pesaran, M.H., Shin, Y., Smith, R.J., 2001 Bounds testing approach to the level relationship J Appl Econ 16, 289–326 Sadorsky, P., 2009 Renewable energy consumption, CO2 emissions and oil prices in the G7 countries Energy Econ 31, 456–462 Sadorsky, P., 2010 The impact of financial development on energy consumption in emerging economies Energy Policy 38, 2528–2535 Sadorsky, P., 2011a Financial development and energy consumption in central and eastern European frontier economies Energy Policy 39, 999–1006 Sadorsky, P., 2011b Trade and energy consumption in the Middle East Energy Econ 33, 739–749 Sadorsky, P., 2012 Energy consumption, output and trade in South America Energy Econ 34, 475–488 Sadorsky, P., 2013 Do urbanization and industrialization affect energy intensity in developing countries? Energy Econ 37, 52–59 Salamaliki, P.K., Venetis, I.A., 2013 Energy consumption and real GDP in G7: multihorizon causality testing in the presence of capital stock Energy Econ 39, 108–121 Sari, R., Ewing, B., Soytas, U., 2008 The relationship between disaggregate energy consumption and industrial production in the United States: an ARDL approach Energy Econ 30, 2302–2313 Shahbaz, M., Lean, H.H., 2012 Does financial development increase energy consumption? The role of industrialization and urbanization in Tunisia Energy Policy 40, 473–479 Shahbaz, M., Zeshan, M., Afza, T., 2012 Is energy consumption effective to spur economic growth in Pakistan? New evidence from bounds test to level relationships and Granger causality tests Econ Model 29, 2310–2319 Shahbaz, M., Khan, S., Tahir, M.I., 2013 The dynamic links between energy consumption, economic growth, financial development and trade in China: fresh evidence from multivariate framework analysis Energy Econ 40, 8–21 Smyth, R., 2013 Are fluctuations in energy variables permanent or transitory? A survey of the literature on the integration properties of energy consumption and production Appl Energy 104, 371–378 Soytas, U., Sari, R., 2003 Energy consumption and GDP: causality relationship in G7 countries and emerging markets Energy Econ 25, 33–37 Soytas, U., Sari, R., 2006 Energy consumption and income in G7 countries J Policy Model 28, 739–750 Soytas, U., Sari, R., 2007 The relationship between energy and production: evidence from Turkish manufacturing industry Energy Econ 29, 1151–1165 Srinivasan, S., 2013 Electricity as a traded good Energy Policy 62, 1048–1052 Stern, D.I., 1993 Energy and economic growth in the USA: a multivariate approach Energy Econ 15, 137–150 Stern, D.I., 2000 A multivariate cointegration analysis of the role of energy in the US macroeconomy Energy Econ 22, 267–283 Stern, D.I., Enflo, K., 2013 Causality between energy and output in the long-run Energy Econ 39, 135–146 Tang, C.F., 2008 A re-examination of the relationship between electricity consumption and economic growth in Malaysia Energy Policy 36, 3077–3085 Tang, C.F., Abosedra, S., 2014 The impact of tourism, energy consumption and political instability on economic growth in the MENA countries Energy Policy 68, 458–464 Tsani, S.Z., 2010 Energy consumption and economic growth: a causality analysis for Greece Energy Econ 32, 582–590 Vaona, A., 2012 Granger non-causality tests between (non)renewable energy consumption and output in Italy since 1861: the (ir)relevance of structural breaks Energy Policy 45, 226–236 Wang, Q., 2014 Effects of urbanization on energy consumption in China Energy Policy 65, 332–339 Westerlund, J., 2005 A panel unit root test with multiple endogenous breaks Technical Report Lund University Westerlund, J., Narayan, P.K., 2012 Does the choice of estimator matter when forecasting returns? J Bank Financ 36, 2632–2640 Westerlund, J., Narayan, P., 2014a Testing for predictability in conditionally heteroskedastic stock returns J Financ Econ http://dx.doi.org/10.1093/jjfinec/nbu001 Westerlund, J., Narayan, P., 2014b A random coefficient approach to the predictability of stock returns in panels J Financ Econ http://dx.doi.org/10.1093/jjfinec/nbu003 Westerlund, J., and Narayan, P., (2014c) Testing for predictability in panels of small time series dimensions with an application to Chinese stock returns, Unpublished Manuscript Wolde-Rufael, Y., 2004 Disaggregated industrial energy consumption and GDP: the case of Shanghai, 1952–1999 Energy Econ 26, 69–75 Wolde-Rufael, Y., 2010 Bounds test approach to cointegration and causality between nuclear energy and economic growth in India Energy Policy 38, 52–58 Wolde-Rufael, Y., Menyah, K., 2010 Nuclear energy consumption and economic growth in nine developed countries Energy Econ 32, 550–556 Yang, H.Y., 2000 A note on the causal relationship between energy and GDP in Taiwan Energy Econ 22, 309–317 Yang, C.L., Lin, H.P., Chang, C.H., 2010 Linear and nonlinear causality between electricity consumption and economic growth: evidence from Taiwan Energy Policy 38, 6570–6573 Yildirim, E., Sukruoglu, D., Alslan, A., 2014 Energy consumption and economic growth in the next 11 countries: the bootstrapped autoregressive metric causality approach Energy Econ 44, 14–21 Yuan, J.H., Kang, J.G., Zhao, C.H., Hu, Z.G., 2008 Energy consumption and economic growth: evidence from China at both aggregated and disaggregated levels Energy Econ 30, 3077–3094 Zhang, C., Xu, J., 2012 Retesting the causality between energy consumption and GDP in China: evidence from sectoral and regional analyses using dynamic panel data Energy Econ 34, 1782–1789 Zhang, W., Yang, S., 2013 The influence of energy consumption of China on its real GDP from aggregated and disaggregated viewpoints Energy Policy 57, 76–81 Ziramba, E., 2009 Disaggregate energy consumption and industrial production in South Africa Energy Policy 37, 2214–2220 Please cite this article as: Smyth, R., Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 ... stationary and has implications for the efficacy of Keynesian demand management policies Third, whether energy variables contain a unit root has implications for forecasting energy demand and the... Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx... Narayan, P.K., Applied econometrics and implications for energy economics research, Energy Econ (2014), http://dx.doi.org/10.1016/j.eneco.2014.07.023 R Smyth, P.K Narayan / Energy Economics xxx