Energy Economics 44 (2014) 259–269 Contents lists available at ScienceDirect Energy Economics journal homepage: www.elsevier.com/locate/eneco Asymmetries in the dynamic interrelationship between energy consumption and economic growth: Evidence from Turkey Ayşen Araỗ a,, Mỹbariz Hasanov b,1 a b Department of Economics, Hacettepe University, Beytepe, Ankara, Turkey Department of Banking and Finance, Okan University, İstanbul, Turkey a r t i c l e i n f o Article history: Received 23 September 2013 Received in revised form 14 April 2014 Accepted 17 April 2014 Available online 27 April 2014 JEL classification: Q43 C32 C51 Keywords: Energy Output growth Nonlinearity a b s t r a c t In this study we examine possible nonlinearities in dynamic interrelationship between energy consumption and economic growth in Turkey for the 1960–2010 period by using a smooth transition vector autoregressive model In order to trace the effects of one variable on another, we calculate Generalized Impulse Response Functions (GIRFs) The computed impulse response functions demonstrate asymmetric effects of positive versus negative and small versus large energy consumption shocks on output growth and vice versa Specifically, we find that negative energy shocks have a greater effect on output growth than positive energy shocks, and that big negative energy shocks affect output much more than small negative energy shocks Similarly, we find that positive output shock has a greater effect on energy consumption whereas negative shocks have almost no effect on energy consumption The results of this study have clear and important implications for energy economists and policymakers in Turkey © 2014 Elsevier B.V All rights reserved Introduction The knowledge of the dynamic interaction between energy consumption and economic growth plays a crucial role in design and implementation of energy policies If, for instance, a decrease in energy consumption hampers economic growth, then adopting of energy conserving policies designed to reduce energy consumption will not be desirable On the other hand, if reducing energy consumption does not affect economic growth, energy conserving policies may be implemented without deteriorating economic growth In this study we aim to analyze the dynamic interaction between energy consumption and economic growth in Turkey There are different views on interrelationship between energy consumption and economic growth in the energy economics literature (see, for example, Ozturk, 2010) Proponents of the so-called “neutrality hypothesis” argue that there is no relationship between energy use and output growth (Yu and Jin, 1992) This hypothesis is supported by the absence of causality between energy consumption and output growth rate, and implies that energy conversing policies will not affect output and hence employment adversely Supporters of the “growth hypothesis” view energy as a compliment to labor and capital in the ⁎ Corresponding author Tel.: +90 312 2978651/164; fax: +90 312 299 2003 E-mail addresses: aysens@hacettepe.edu.tr (A Araỗ), muhariz.hasanov@okan.edu.tr (M Hasanov) Tel.: +90 216 677 1630; fax: +90 216 677 1667 http://dx.doi.org/10.1016/j.eneco.2014.04.013 0140-9883/© 2014 Elsevier B.V All rights reserved production function Hence, reducing energy use will hamper output (Beaundreau, 2005; Ghali and El-Sakka, 2004; Lee and Chang, 2008; Oh and Lee, 2004; Stern, 2000) Supporters of the “conservation hypothesis”, on the other hand, argue that positive relationship between energy use and output growth stems from positive effect of output on energy, but not vice-versa Therefore, energy conversing policies may be implemented without hampering employment and output (Apergis and Payne, 2009; Lee and Chang, 2008) Finally, “feedback hypothesis” implies that there is bidirectional causality between energy use and output growth Hence, reducing energy use may hamper output growth.2 Due to the importance of the issue both for policymakers and economists, the dynamic interaction between energy consumption and economic growth has been intensively investigated in energy economics literature since the seminal work of Kraft and Kraft (1978) However, the empirical evidence is mixed (see also literature surveys by Ozturk, 2010; Payne, 2010) For example, Kraft and Kraft (1978), Akarca and Long (1979, 1980), Yu and Hwank (1984), Abosedra and Baghestani (1989), Yu and Choi (1985), Erol and Yu (1987), Masih and Masih (1996), Cheng and Lai (1997), Ang (2008), Zhang and Cheng (2009), Zamani (2007) and Mehrara (2007) found unidirectional causality running from economic growth to energy consumption On the other hand, Yu and Choi (1985), Masih and Masih For a thorough discussion of the issue, see, for example, literature surveys by Ozturk (2010) and Payne (2010) 260 A Araỗ, M Hasanov / Energy Economics 44 (2014) 259–269 (1996), Asafu-Adjaye (2000), Bowden and Payne (2009), Belloumi (2009), Stern (2000), Oh and Lee (2004), Wolde-Rufael (2004) and Ho and Siu (2007) found unidirectional causality running from energy consumption to economic growth Glasure (2002), Erdal et al (2008) and Belloumi (2009) found bidirectional causal relationship between energy consumption and economic growth, whereas Halicioglu (2009) and Payne (2009) found no causality between them A common feature of the aforementioned studies is that all of them used linear models Conflicting findings regarding the dynamic interactions between energy consumption and economic growth may be attributed to the assumption that the relationship between energy consumption and economic growth is linear In linear models the parameters assumed to be constant over the sample period which implies that the relationship between energy consumption and economic growth is stable However, some events such as changes in the policies, energy crises and economic crises could affect the parameters Hence, in time series framework these changes must be taken into consideration in order to avoid spurious results Recently, a growing number of theoretical and empirical studies have taken into consideration nonlinearity to analyze the dynamic interactions between the macroeconomic series in question Moon and Sonn (1996), Lee and Chang (2007), Chiou-Wei et al (2008), Huang et al (2008), Cheng-Lang et al (2010), Rahman and Serletis (2010), among others, have investigated possible nonlinear relationships between energy use and macroeconomic variables By introducing an endogenous growth model that emphasizes energy requirements to support potential growth, Moon and Sonn (1996) claim that at the beginning, economic growth rate increases with productive energy expenditures but it subsequently decreases They estimated their theoretical model with Korean data to confirm the validity of their hypotheses Taking account of the fact that level of development may affect the interrelationship between energy use and economic growth, Lee and Chang (2007) examined energy consumption output growth causality by categorizing countries into different groups by level of development Their results suggest that the causality between energy consumption and output level is not linear, and varies with output level Chiou-Wei et al (2008) used nonlinear causality tests besides linear causality tests to examine causality between energy consumption and economic growth in the case of eight Asian countries and the USA They argue that changes in the economic events and regime changes such as changes in energy policy or fluctuations in energy price can cause structural changes in the pattern of energy consumption, which in turn, creates a room for a nonlinear relationship between energy use and economic growth When they take into account nonlinearity in the interrelationship between energy consumption and output, the direction of causality between the variables is reversed in the cases of Taiwan, Singapore, Malaysia and Indonesia On the other hand, in the cases of Korea, Hong Kong, Philippines, Thailand and the USA both linear and nonlinear causality tests imply the same direction of causality or non-causality Based on the arguments of Chiou-Wei et al (2008) that changes in economic environment, policy alterations and world energy prices may lead to a nonlinear interrelationship among electricity consumption and economic activity, Cheng-Lang et al (2010) analyzed causality between total and sectoral electricity consumption levels and output in Taiwan They concluded that there is bidirectional nonlinear causality between total electricity consumption and real output In addition, they find that there is unidirectional nonlinear causality from output level to residential electricity consumption In order to investigate nonlinear relationships between energy consumption and economic growth for 82 countries, Huang et al (2008) employed threshold regression models Their results suggest a significant positive relationship between energy consumption and output growth for regimes associated with lower threshold values However, when the threshold variables are higher than certain threshold levels, they found either no significant relationship or a significant but negative relationship between energy consumption and economic growth Rahman and Serletis (2010) examined asymmetric effects of oil price and monetary shocks using data for the United States In particular, they employ a nonlinear VAR model by using realized oil price volatility as a regime switching variable They find that both oil prices and oil price volatility have impacts on macroeconomic activity In addition, they find that monetary policy not only reinforces the effects of oil price shocks on output, but it also contributes to the asymmetries in the effects of oil price shocks on output Hasanov and Telatar (2011) analyzed stationarity properties of per capita total primary energy consumption across 178 countries around the world allowing for both structural breaks and nonlinearities in the data generating process, and found that allowing for breaks and nonlinearities in the data generating process leads to more frequent rejection of the null hypothesis of unit root They also tested linearity of the series under investigation and found that all series under consideration are characterized by some type of nonlinearity They suggested taking account of possible nonlinear dynamics in analyzing relationship between energy use and macroeconomic variables The results obtained in these empirical studies imply that nonlinearity may stem from level of development or changes in energy policies or fluctuations in world energy markets In fact, Hasanov and Telatar (2011) argue that fluctuations in energy prices may lead nonlinear dynamics in presence of adjustment costs As they point out, a change in input prices affects firms' input demands Firms react to increases in energy prices by reducing energy use in the short run, and adopting energy saving production technologies in the long run However, adoption of new technologies is costly Hence, if the costs of adoption of new technology are greater than the costs of operation with energy-intensive technology, then firms shall not adjust their production processes On the other hand, if the gains from adopting new technology cover the costs of adjustment, then firms will incur adjustment costs and adopt energy-saving technology This implies that the adjustment of energy consumption to the desired level might be inherently nonlinear The purpose of this study is to examine possible nonlinearities in the dynamic interaction between energy consumption and economic growth in Turkey Several authors have examined energy consumption and economic growth nexus and reported conflicting results in the case of Turkey Soytaş and Sarı (2003) employed a vector error correction model (VECM) and concluded that unidirectional causality runs from energy consumption to economic growth for the 1960–1995 period Altinay and Karagol (2004), using Hsiao' version of Granger method over the period 1950–2000, found no causal relationship between energy consumption and economic growth Altinay and Karagol (2005) focused on the 1960–2003 period and used a VAR model and standard Granger test and found unidirectional causality running from electricity consumption to economic growth Jobert and Karanfil (2007) employed a cointegration and Granger causality analysis and found no evidence of causality between energy consumption and economic growth in the long run Lise and Van Montfort (2007), using an error correction model (ECM) for the 1970–2003 period, concluded that unidirectional causality runs from economic growth to energy consumption Erdal et al (2008), employing a pairwise Granger causality for the 1970–2006 period, concluded that bidirectional causality exists between energy consumption and economic growth Halicioglu (2009) undertook an autoregressive distributed lag (ARDL) approach for the 1960–2005 period and found that energy consumption and economic growth are neutral to each other Yalta (2011), using a maximum entropy bootstrap over the period 1950–2006, found an evidence supporting the neutrality between energy consumption and economic growth Our approach in this paper differs from previous researches on energy-output relationship for the case of Turkey As briefly discussed in Section 2, Turkey has undergone serious structural changes during the analyzed period In addition, Turkey has limited energy sources A Araỗ, M Hasanov / Energy Economics 44 (2014) 259–269 and heavily depends on imported energy, which increases vulnerability of economy of the country to energy shocks These specific features of the Turkish economy coupled with massive reforms in the energy sector imply that simple linear models might be inadequate to analyze energyoutput causality in Turkey Therefore, we prefer to use a nonlinear model to examine possible nonlinearities in the interrelationship between energy use and output growth Specifically, we employ smooth transition (STR) vector autoregressive model, as detailed in Teräsvirta and Anderson (1992) and Teräsvirta (1994) The STR modeling has several advantages over the competing nonlinear models 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 modeling 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 modeling the interaction between energy consumption 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, the STR modeling allows one to choose both the most appropriate switching variable and type of transition function unlike other regime-switching models In passing we note that Rahman and Serletis (2010) also used a multivariate STR model and found that these models capture possible asymmetries in the effects of energy shocks on macroeconomic variables quite well The main novelty of this paper is that we examine possible asymmetries in the effects of big versus small and negative versus positive shocks to one variable on another Traditional linear models assume that negative energy shocks have the same effect on output level as the positive energy shocks of the same magnitude in absolute terms and that a twofold energy shock affects output twice larger than a onefold energy shock However, we find asymmetries in the effects of positive versus negative and small versus large shocks In particular, our computations show that negative energy shocks have greater effect on output growth rate in absolute terms than the positive energy shocks In addition, we find that big negative energy shocks have relatively larger effects on output than small negative energy shocks On the other hand, we find no asymmetry in the effects of big and small positive energy shocks As regards the effects of output shocks on energy use, we find that small and big negative output shocks have negligible effects on energy use whereas positive output shocks affect energy use Interestingly, our results imply that small output shocks have relatively greater effects on energy use than larger output shocks These results shed light on design of appropriate energy policies in Turkey The remaining of this paper is organized as follows Section provides the general overview of output growth and energy policies in Turkey over the period 1960–2010 Section specifies a brief description of smooth transition vector autoregressive (STR-VAR) model and computations of the generalized impulse response function Section presents the data and empirical evidence The final section discusses policy implications of the results and concludes the paper Economic performance and energy in Turkey in the period 1960–2010 Turkey has implemented quite different macroeconomic policies and has undergone several economic and political crises since 1960.3 Turkey pursued an import-substitution industrialization and growth See Müslümov et al (2002), Dibooglu and Kibritcioglu (2004), Hasanov and Omay (2008), Hasanov et al (2010), among others, for a brief discussion on economic developments in Turkey 261 strategy during the period 1960–1980 This period is also known as “planned development period” With the establishment of the State Planning Organization in 1960 and adoption of a new constitution in 1961, state enterprises started to play a key role in industrialization of the economy Many state enterprises were established during this period, which have made huge investments in heavy manufacturing and capital goods sectors in order to boost economic growth and contribute to industrialization of the economy The main target of the import-substitution industrialization policies implemented until 1980 was to achieve foreign trade balance and reduce dependence on imported goods However, these policies were not successful in achieving these targets as industrial production was heavily dependent on intermediate goods imports As a result, Turkey abandoned inward oriented growth strategies and adopted exportoriented growth strategies in 1980 in a response to a severe balance of payment crisis Turkey has implemented massive economic reforms throughout the 1980s aimed at liberalization of import regime and encouraging exports and foreign direct investments, adopting a flexible exchange rate regime, liberalization of current account transactions, and removing price controls While private enterprises were considered as main actors of economic activity, the government continued to undertake huge investments in order to bolster economic growth Unlike the 1960s and 1970s, public investments were directed mainly to infrastructure rather than industry The engine of economic growth during the 1980s was huge public investments in infrastructure and growth in exports Although economy grew well during the 1980s, government's concern has shifted from external competitiveness to domestic stability in the face of accelerated inflation Therefore, Turkey abandoned real exchange rate depreciation policy aimed at export promotion in order to prevent inflationary effects, as a consequence output growth slowed with the loss of external competitiveness The exchange rate policy aimed at stabilization of exchange rates was unsustainable as foreign trade deficits grew drastically As a result, Turkey was obliged to abandon exchange rate policy in 1994 and Turkey underwent a severe crisis in that year Although stabilization program announced in that year was successful in stabilizing financial markets and reducing budget deficits, the government once more switched to expansionary policies due to political considerations Since comprehensive reforms were not undertaken, the stabilization policies had limited effects, and the economic condition began to deteriorate starting from 1995 The economic growth slowed down in 1999 when two earthquakes hit the most industrialized region of Turkey Turkey adopted an IMF-backed exchange rate stabilization program in December 1999 However, the failure of the government to implement massive structural reforms and privatization has diluted the credibility of the stabilization program As a result, the stabilization program failed, and an economic and financial crisis outburst in early 2001 Turkey abandoned exchange rate targeting and implemented massive reforms starting from 2001 The Central Bank was given independence and adopted inflation targeting policies Fiscal policy was tightened, goods and financial markets were further liberalized, and massive privatization has been implemented As a result, Turkey enjoyed relatively high growth rates and low inflation post-2001 period Even though Turkey underwent several crises and output fluctuated widely during the analysed period, economy grew considerably during the analysed period and Turkey succeeded to transform from agricultural economy to industrial economy In fact, the annual compounded GDP growth rate from 1960 to 2010 was 4.3% Total energy consumption rose by 4.6% per annum during the same period Table below presents average annual growth rates of per capita GDP and energy consumption as well as share of energy imports in total energy use and output volatility First note that output volatility was relatively high and increased throughout the period as a result of the economic crises in 1994 and in 2001 The table suggests that per capita energy use grew with per capita income 262 A Araỗ, M Hasanov / Energy Economics 44 (2014) 259–269 Table GDP and energy use in Turkey, 1960–2010 Periods Annual growth rate of per capita GDP, % Output volatility Annual growth rate of per capita energy use, % Net energy imports (as % of total energy use) 1960–1969 1970–1979 1980–1989 1990–1999 2000–2010 2.87 0.030 2.83 19.4 2.30 0.031 3.16 42.4 2.37 0.034 2.61 47.8 1.45 0.049 1.42 60.9 1.72 0.052 1.26 69.3 Source: World Development Indicators Notes: The annual growth rates of per capita GDP and energy use are calculated as compounded annual growth rates for the relevant period Net energy imports are as of the end-of-period Output volatility is measured as the standard deviation of annual growth rates of per capita GDP for the relevant decade However, the pattern of energy consumption growth was not the same over the decades In particular, energy growth was lower than output growth during the 1960–1969 and post 2000 periods, but higher than output growth 1970 to 1990, and almost the same as the output growth in the 1990s This implies that the relationship between output and energy was not the same over the analysed period Energy mix also changed drastically during the analysed period In particular, the share of fuels with higher calorific values increased over the past five decades Table below presents share of fuels in total energy consumption of Turkey As the table suggests, the share of low calorific fuels (wood, bio-fuels and wastes) dropped drastically from 32% in 1970 to 3% by 2010, whereas share of hydrocarbons (oil and gas) rose to almost 60% by 2010 The share of coal and hydroelectric power also rose during this period This is an expected result as use of hydrocarbon resources grows with industrial output In the face of poor endowment with hydrocarbon resources, energy imports of Turkey also rose drastically as industrial production increased While energy imports constituted around 12.3% of total energy use in 1960, the share of energy imports in total energy use rose to almost 70% by 2010 Growing dependence on energy imports has increased vulnerability of the Turkish economy to world energy shocks Econometric methodology We follow Weise (1999) and Rothman et al (2001), who generalize the smooth transition autoregressive (STAR) model of Teräsvirta (1994) to vector autoregressive (VAR) models Let lent and lyt denote the energy consumption and the output growth in logarithm, respectively Then the smooth transition vector autoregressive (STR-VAR) model for energy consumption and output growth can be written as follows: p X 1;i xti ỵ xt ẳ 1;0 ỵ p X 2;0 ỵ 2;i xti iẳ1 ! F st ; ; cị ỵ t 2:1ị F st ; ; cị ẳ ỵ expf ðst −cÞgÞ −1 ; γ N0 ð2:2Þ This choice of the transition functions gives rise to the logistic smooth transition vector autoregressive (LSTVAR) model Here, st is a transition variable, and γ and c are slope and threshold parameters, respectively The restriction γ N is an identifying restriction As st increases, the logistic function F(st;γ,c) changes monotonically from to around threshold parameter c with F(c;γ,c) = 0.5 The slope parameter γ determines the smoothness of transition from one regime to another This function can be convenient for modeling, for example, business cycle asymmetries to distinguish expansions and recessions (Teräsvirta and Anderson, 1992) In the LSTVAR model, the regimes are associated with small and large values of the transition variable st with respect to the threshold c In order to allow for regime-changing behavior that depends on the absolute value but not on the sign of the transition variable, one may use the following exponential transition function: n o F st ; ; cị ẳ 1exp st cị ; N0 2:3ị iẳ1 where xt is a (2x1) column vector given by xt = (lent, lyt) ', Ψj,0, j = 1, are (2x1) vector of constants, Ψj,i, j = 1, 2, i = 1, 2, … p are (2x2) Table Share of fuels in total energy consumption Coal Oil Natural gas Hydroelectric Wood, agricultural residues and animal wastes matrices of parameters, and εt = (εent, εyt) ' is a (2x1) vector of residuals with mean zero and (2x2) covariance matrix Σ The transition function F(st;γ,c) is a continuous function bounded between zero and one The STR-VAR model can be interpreted as a regime switching model that allows for two regimes, associated with the extreme values of the transition function, F(st;γ,c) = and F(st;γ,c) = 1, whereas the transition from one regime to the other is gradual The regime that occurs at time t can be determined by the observable variable st and associated value of F(st;γ,c) One of the popular choices of the transition function F(st;γ,c) is the logistic function as given below: 1970 1980 1990 2000 2010 24.6% 42.2% 0.0% 1.4% 31.7% 22.0% 50.3% 0.1% 3.1% 24.0% 30.5% 45.4% 5.9% 3.9% 13.7% 28.5% 41.0% 17.4% 3.5% 8.2% 31.0% 27.0% 32.0% 4.0% 3.4% Source: Our own calculations using data from Total Energy Balance 2010, and Primary Energy Consumption by Source, 1970–2006 Ministry of Energy and Natural Resources of Turkey Raw data on primary energy consumption by source is available online at ministry's website at: http://www.enerji.gov.tr/index.php?dil=tr&sf=webpages&b=y_istatistik&bn= 244&hn=244&id=398 Last viewed on April 8, 2010 The exponential transition function equals zero when st = c and approaches for larger deviations of the transition variable st from the threshold value c Thus, in the exponential smooth transition vector autoregressive (ESTVAR) models, regime changes are associated with small and big values of the transition variable st with respect to the threshold value c One of the shortcomings of the exponential function is that if γ approaches either or infinity, the function F(st;γ,c) becomes a constant In this case, one may use quadratic or second-order logistic transition function (see, for example, van Dijk, 1999) One may adopt a “specific-to-general” approach to specify an appropriate STR-VAR model In the first step of this approach, one first estimates an appropriate linear model and then tests linearity against a smooth-transition type nonlinearity The linearity tests, however, are complicated by the presence of unidentified nuisance parameters In particular, under the null hypothesis of linearity, Ψ2,i , i = 0, 1, 2, … p are unidentified nuisance parameters, which renders standard asymptotic inference invalid Following Luukkonen et al (1988), one may circumvent this problem by approximating the transition functions in Eq (2.1) by a third-order Taylor expansion A Araỗ, M Hasanov / Energy Economics 44 (2014) 259–269 around the null hypothesis After replacing the transition function in Eq (2.1) by a third-order Taylor expansion, one obtains the following auxiliary regression model: xt ¼ 1;0 ỵ p p p X X X 1;i xti ỵ 2;i xti st ỵ 3;i xti st iẳ1 p X ỵ 4;i xti st ỵ et iẳ1 2:4ị iẳ1 iẳ1 The vector e t comprises the original shocks εt as well as the error arising from the Taylor approximation In Eq (2.4) it is assumed that the transition variable st is one of the elements of xt If this is not the case, then additional regressors Φ5sit, i = 1, 2, enter the auxiliary regression model (2.4) The parameters in Φj,i, j = 1, , 4, i = 0, 1, 2, … p are functions of the parameters Ψj,i , j = 1, 2, i = 0, 1, 2, … p, γ and c in the bivariate STR-VAR model in Eq (2.1) In Eq (2.4), it is clear that Φ1 = Ψ1 and Φ2,i = Φ3,i = Φ4,i = if and only if γ = in Eq (2.1) Therefore, the null hypothesis of linearity in the auxiliary regression model (2.4) can be written as H0 : Φ2,i = Φ3,i = Φ4,i = 0, which can be tested directly by a likelihood 0 ^t =T and Ω1 ¼ ∑e ^t =T be the estimated ^t e ^t e ratio (LR) test Let Ω0 ¼ ∑e variance–covariance matrices of residuals from the restricted and unrestricted regressions, respectively Then the LR test statistic for linearity of a k variable VAR model with p lags is given by LR = T {log |Ω0| − log |Ω1|}, which is asymptotically distributed χ2(3pk2) In particular, the empirical specification procedure for STR-VAR models consists of the following steps: Specify an appropriate linear VAR model for time series xt under investigation Test the null hypothesis of linearity against the alternative of STR-type nonlinearity To identify the appropriate transition variable st, the LR test can be computed for several candidates, and the one for which the p-value of the test statistics is smallest can be selected If the null of linearity is not rejected against either alternative, then retain the linear VAR model If the linearity tests described above suggest that the appropriate model is a STR-VAR model, then one may follow the procedure of Teräsvirta (1994) to decide whether logistic or exponential functions are more convenient transition function Teräsvirta (1994) suggests using a decision rule based upon a sequence of tests In particular, he proposes to test the hypotheses H03 : Φ4 ¼ H02 : Φ3 ¼ 0jΦ4 ¼ H01 : Φ2 ¼ 0jΦ3 ¼ ẳ 2:5ị in Eq (2.4) by means of LM type test The decision rule is as follows: (i) Φ4 is always nonzero only if the model is an LSTVAR model, (ii) Φ3 is always nonzero if the transition function is “exponential” function, (iii) Φ2 is always nonzero if the transition function is a logistic function If the p-value corresponding H02 is the smallest, a model with “exponential” function should be chosen, otherwise the logistic transition function should be preferred as the transition function Once the appropriate model is chosen, the model can be estimated by nonlinear least squares, and used for descriptive or forecasting purposes After estimating the model, we use generalized impulse response functions to examine the dynamic relationships between energy consumption and economic growth The method of computation and features of the generalized impulse response functions are discussed by Koop et al (1996) in great detail.4 For a thorough discussion of generalized impulse response functions and comparison to traditional impulse response functions, see also Chapter 2.6 in van Dijk (1999) 263 The impulse response measure which is commonly used in the analysis of linear models is defined as the difference between two realizations of xt + n which starts from identical histories of the time series up to time t − 1, denoted as wt − In one realization, the process is ‘hit’ by a shock of size vt = δ at time t, while in the other realization no shock occurs at time t All shocks in intermediate periods between t and t + n are set equal to zero in both realizations That is, the traditional impulse response function [TIRF] is given by TIRFx ðn; vt ; wt−1 ị ẳ E xtỵn jvt ẳ ; vtỵ1 ẳ 0; ; vtỵn ẳ 0; wt1 E xtỵn jvt ẳ 0; vtỵ1 ẳ 0; ; vtỵn ẳ 0; wt1 ð2:6Þ for n = 0, 1, … The traditional impulse response function as defined above has some characteristic properties in case the model is linear First, the TIRF is symmetric, in the sense that a shock of −δ has exactly the opposite effect as a shock of size + δ Furthermore, it might be called linear, as the impulse response is proportional to the size of the shock Finally, the impulse response is history independent as it does not depend on the particular history wt − Because of these properties, TIRF is not appropriate for nonlinear models In nonlinear models, the impact of a shock depends on the sign and the size of the shock, as well as on the history of the process Furthermore, if the effect of a shock on the time series n N periods ahead is to be analyzed, the assumption that no shocks occur in intermediate periods might give rise to quite misleading inference concerning the propagation mechanism of the model (van Dijk, 1999) The Generalized Impulse Response Function [GIRF], introduced by Koop et al (1996) provides a natural solution to the problems involved in defining impulse responses in nonlinear models The GIRF for an arbitrary shock vt = δ and history wt − is defined as GIRFx n; vt ; wt1 ị ẳ E xtỵn jvt ; wt1 E xtỵn jwt1 for n ẳ 0; 1; ð2:7Þ The GIRF is a function of vt and wt − It is natural to treat vt, and wt − as realizations from the same stochastic process that generates the realizations of {x t } (Koop et al., 1996) The generalized impulse response function (GIRF) is designed to solve the problems categorized as follows: What types of shocks (e.g., variable-specific or system-wide shocks) hit the system at time t? What is the “history” of the system at time t − (e.g., expansionary or recessionary) before the shock hits? What future shocks are assumed to hit the system from t + to t + n? The problem of treatment of the future is circumvented by using the expectation operator conditioned on only the history and/or shock Thus, the response constructed is an average of what might happen given the present and past The natural baseline for the impulse response function is then defined as the conditional expectations, given only the history Koop et al (1996) suggest that the impulse response functions are to be computed by simulating the model In order to compute the impulse response functions, the following algorithm might be used (see also Weise, 1999): Pick a history wrt − The history is the actual value of the lagged endogenous variables at a particular date Pick a sequence of (k-dimensional) shocks vbt + n, n = 0, …, q The shocks are drawn with replacement from the estimated residuals of the model The shocks are assumed to be jointly distributed, so if date t's shock is drawn, all k residuals for date t are collected Using wrt − and vbt + n, simulate the evolution of xt + n over q + periods Denote the resulting baseline path Xt + n(wrt − 1, vbt + n), n = 0, , q Substitute v i0 for the i,0 element of v bt + n and simulate the evolution of Xt + n over q + periods Denote the resulting path Xt + n(vi0, wrt − 1, vbt + n), n = 0, , q 264 A Araỗ, M Hasanov / Energy Economics 44 (2014) 259–269 Table Descriptive statistics Output growth rate Energy use growth rate Mean S.E Min Max Sk Ku J-B Q(1) ARCH(1) 0.025 0.028 0.039 0.041 −0.070 −0.091 0.088 0.105 −0.705 (0.048) −0.664 (0.063) 0.010 (0.989) 0.518 (0.486) 4.138 (0.126) 4.231 (0.121) 0.000 (0.993) 0.214 (0.644) 0.002 (0.961) 0.525 (0.469) Notes: S.E denotes standard error, Sk denotes excess skewness, Ku denotes excess kurtosis, and J–B denotes Jarque–Berra's test for normality of series Q(1) is Ljung–Box's Q test for autocorrelation of order one ARCH(1) is Engel's (1982) LM test for first order autoregressive conditional heteroscedasticity p-values of diagnostic tests are provided in parenthesis 0.125 OUTPUT ENERGY 0.100 0.075 0.050 0.025 -0.000 -0.025 -0.050 -0.075 -0.100 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 Fig Output and energy consumption growth rates in Turkey, 1961–2010 Repeat steps to B times Repeat steps to R times and compute X at + n (v i0 ) = [X t + n (v i0 , w rt − , vbt + n ) − X t + n (w rt − , v bt + n )]/BR for the average impulse response function, or X rn t + n (v i0 ) = median[Xt + n (v i0, wrt − , v bt + n) − Xt + n (wrt − , v bt + n)] for the median response In this paper, we compute impulse responses for ten periods (n = 10) for all available data points (R = 36)5 with B = 1000 replications Data and estimation results In this paper, we use the annual data spanning the period 1960–2010 for Turkey This time period is dictated by data availability Energy consumption is proxied by energy use (kg of oil equivalent per capita) The output level here is GDP per capita All data are obtained from World Development Indicators database of The World Bank We first present the data and then give estimation results 4.1 Preliminary data analysis Table presents basic descriptive statistics of the growth rates of output and energy consumption As the table reveals, average growth rate of energy consumption was higher than the output growth rate, Note that we use the annual data covering the period 1960–2010 After preliminary data transformations and lags used to estimate the model, we have 46 data points Since we estimate impulse responses for ten periods, there remain only 36 data points for computing impulse responses mainly due to industrialization of the economy Although both output and energy consumption exhibited large fluctuations during the analysed period, fluctuations in per capita energy use were larger than per capita income Large falls in both variables were observed during the economic crisis in 2001 Large increases in both variables, on the other hand, were observed during the early industrialization periods The statistics presented in the Table also suggest that although both variables are negatively skewed, the null hypothesis that the series are normally distributed cannot be rejected In addition, Ljung and Box's (1978) Q test for autocorrelation does not reject the null hypothesis of no correlation of order one Similarly, Engle's (1982) ARCH-LM test indicates no conditional heteroscedasticity in both of the series In order to better understand the fluctuations in energy use and output growth we present a graph of the growth rates of output and energy consumption in Fig below As can be seen from Fig 1, energy consumption and output moved in the same direction for most of the period On average, both per capita output and energy use grew rapidly during the 1960s and at the beginning of the 1970s as a consequence of rapid industrialization during that period Furthermore, as the figure suggests, energy use and output moved closely with each other during the 1990s On the other hand, this close co-movement weakened during other sub-periods In fact, as further elaborated in Section 2, per capita output and energy use grew on average by 1.5% during the 1990 whereas growth rate of energy use exceeded that of output growth during the period 1970–1990 This pattern of co-movement of the variables suggests that the relationship between energy use and output might be nonlinear Now, we turn to formal statistical tests to determine the appropriate model for examining the dynamic interrelationship between the variables A Araỗ, M Hasanov / Energy Economics 44 (2014) 259–269 265 Table Unit root test results Variables ADF lent Δlent lyt Δlyt PP Intercept only Intercept and time trend Intercept only Intercept and time trend −1.226 −6.906*** −0.696 −7.263*** −2.179 −7.016*** −2.951 −7.236*** −1.257 −6.907*** −0.692 −7.263*** −2.212 −7.021*** −3.003 −7.235*** KPSS lent Δlent lyt Δlyt NG–Perron 0.940*** 0.123 0.950*** 0.047 0.175** 0.041 0.088 0.038 1.506 −24.876*** 1.723 −21.839*** −6.848 −22.759** −12.119 −22.746** Note: The lag lengths of each variable in each equation are selected by applying conventional AIC Figures in parenthesis are p-values of the test statistics *, **, *** denote rejection of the null hypothesis of unit root for NG–Perron test and rejection of the null hypothesis of stationarity for KPSS test at 10%, 5% and 1% significance levels, respectively 4.2 Stochastic properties of the series The specification procedures described in the previous section rely on the assumption that both the output growth (lyt) and energy consumption (lent) are I(0) processes Therefore, prior to estimation of the linear model, we first tested stationarity of the variables concerned Taking account of the low power of conventional Augmented Dickey– Fuller (ADF) test against alternative data generating processes, we also carried out the Phillips–Perron (PP), Kwiatkowski, et al (KPSS), and Ng and Perron (NG–Perron) unit root tests The KPSS test differs from other tests in that it assumes that the series under investigation are stationary under the null hypothesis whereas the other tests assume that series have a unit root under the null hypothesis We apply all tests both with and without a time trend Table presents the unit root test results As can be seen from the table, the ADF, PP and NG–Perron tests suggest that both energy consumption and output level contain a unit root On the other hand, the KPSS test suggests that per capita output may be trend stationary In order to check if there is a cointegration relationship between the variables, we first employed Johansen and Juselius (1990) (JJ) cointegration tests, results of which appear below in Table The results of the JJ cointegration test suggest no cointegration relationship between energy use and output level In addition to the conventional JJ cointegration test, we also applied Pesaran et al (2001)'s bounds testing procedure and nonlinear cointegration tests of Kapetanios et al (2006) The bounds testing approach proposed by Pesaran et al (2001) is applicable irrespective of whether the underlying regressors are purely I(0), purely I(1) or mutually cointegrated This test became popular recently in the face of increasing concerns about power and size properties of the unit root tests In fact, the results presented in Table above lead to conflicting conclusions about order of integration of the per capita income Therefore, the bounds testing procedure of Pesaran et al (2001) provide a better tool for testing relationship between levels of per capita income and energy use Table Cointegration test results The statistic underlying the bound testing procedure is the familiar F-test with new critical values that they tabulate The statistic is used to test the significance of lagged levels of the variables under consideration in a conditional unrestricted equilibrium correction model (ECM) The critical values consist of a lower bound on the assumption that all variables are integrated of order zero and an upper bound on the assumption that all variables are integrated of order one If the computed F-statistics falls behind the lower bound, it indicates no cointegration If the computed F-statistics exceeds the upper bound, the conclusive decision can be made in favor of the cointegration If, however F-statistics falls within these bounds, inference would be inconclusive The critical values of the F-statistic for upper bound and the lower bound with one regressor are 4.04 and 4.78 respectively at the 10% level of significance for the model with unrestricted intercept and no trend and, 5.59 and 6.26 for the model with unrestricted intercept and unrestricted trend, respectively The computed F-statistics are given in Table The computed F-statistics fall behind the lower bounds, suggesting that energy consumption and economic growth are not cointegrated As briefly described above, the dynamics of energy consumption and output suggest that the interrelationship between these variables might not be linear Hence, we also applied the cointegration test procedure proposed by Kapetanois et al (2006) Unlike other cointegration tests, this test allow for possible nonlinearities in the adjustment to the equilibrium level Kapetanois et al (2006) propose four different test statistics, namely FNEC, F⁎ NEC, tNEC and tNEG Computed test statistics are given in Table As none of these test statistics are significant even at 10% significance level, we conclude that energy consumption and output are not cointegrated All considered cointegration tests suggest that the variables under consideration are not cointegrated This finding implies that there exists no long-run relationship between energy use and output level Hence, time paths of the variables are independent of each other in the longrun and the relationship between them are restricted to the short run 4.3 Linearity tests and smooth-transition VAR model Since we found no cointegration relationship between the variables, we estimated a VAR model in differences The nonlinearity tests are sensitive to autocorrelation So, the autoregressive structure of the Unrestricted cointegration rank test (trace) Hypothesized no of CE(s) Eigenvalue Trace statistic Critical value(0.05) Probability None At most 0.127 0.011 7.161 0.535 15.495 3.842 0.559 0.465 Unrestricted cointegration rank test (maximum eigenvalue) Hypothesized no of CE(s) Eigenvalue Max-eigen statistic Critical value(0.05) Probability None At most 0.127 0.011 6.626 0.535 14.265 3.842 0.534 0.465 Table Pesaran et al (2001)'s Bounds testing results Dependent variable Lag order F-statistics (unrestricted intercept, no trend) F-statistics (unrestricted intercept, unrestricted trend) Δlyt Δlent 1 1.187 2.961 5.161 2.658 Note: The lag lengths of each variable in each equation are selected by applying conventional AIC 266 A Araỗ, M Hasanov / Energy Economics 44 (2014) 259269 Table Nonlinear cointegration tests results Dependent variable Lag order Δlyt Δlent 1 Δlyt Δlent 1 ⁎ FNEC FNEC Intercept only Intercept and time trend Intercept only Intercept and time trend 1.650 1.910 3.135 2.147 1.393 2.800 4.743 3.039 4.4 Asymmetries in the dynamic interrelationship between variables tNEC −1.518 −2.389 tNEG −2.557 −2.488 −1.778 −1.917 −1.923 −2.072 Critical values of the FNEC , F⁎NEC, tNEC and t NEG tests at 10% significance level for the intercept (intercept and trend) case are 11.79(13.95), 10.13(12.83), − 2.92(− 3.30), and − 2.98(− 3.41), respectively The lag lengths of each variable in each equation are selected by applying conventional AIC model should be specified so as to capture significant autocorrelation in the linear model The lag lengths of each variable in each equation were selected by applying conventional Akaike Information Criterion (AIC), and then the resultant model was tested against autocorrelation of residuals Accordingly, one lag of each variable was used in each equation Moreover, we added dummy variables into each equation of the VAR model for outliers evident in the residuals in order to ensure that rejection of the null hypothesis of linearity is not due to the presence of big outliers We first estimated the equations without dummy variables, and computed standard deviations of the residuals Then, we defined positive outliers as those observations which are two times larger than the standard deviation whereas negative outliers as those observations which are two times larger than the negative value of the standard deviation for each equation After estimating a linear VAR model, we tested linearity of the model against STR type nonlinearity The results of the linearity tests are provided in Table In panel A of Table 8, we report system-wide linearity tests for several candidate transition variables As can readily be seen from the table, the null hypothesis of linearity is rejected for many candidate transition variables This result implies that the dynamic interaction between energy use and output growth rate might be inherently nonlinear The null hypothesis is more convincingly rejected when the third lag of the change in energy consumption is used as a candidate transition variable Therefore, we chose this variable as the appropriate switching variable and proceed to select the form of the transition function The tests of the null hypotheses H03, H02 and H01 are reported in panel B of Table Since H02 is not rejected whereas both H03 and H01 Table Linearity test results Panel A System-wide linearity tests against STR-type nonlinearity Candidate transition variable LR test statistic Probability Δlyt − Δlyt − Δlyt − Δlyt − Δlyt − Δlent − Δlent − Δlent − Δlent − Δlent − Time trend 14.054 37.775 55.699 35.503 26.753 28.668 56.120 58.070 23.986 −3.193 33.627 (0.7256) (0.0366) (0.0003) (0.0613) (0.3161) (0.0526) (0.0002) (0.0001) (0.4624) (NA) (0.0916) Panel B Transition function specification test H03 H02 H01 are rejected at conventional significance levels, we choose logistic transition function After selecting both appropriate switching variable and the form of the transition function, we estimated a LSTVAR for energy use and output growth rates The parameter estimates of the LSTVAR model are provided in Table 15.576 −88.992 28.190 Note: Boldface indicates selected transition variable (0.0489) (NA) (0.0004) The parameters of the estimated LSTVAR model are difficult to interpret (e.g., Rahman and Serletis, 2010) However, one may use impulse-response functions to figure out the dynamic relationship between the variables in the estimated LSTVAR model Accordingly, the generalized impulse response functions (GIRF) introduced by Koop et al (1996) are used to compute impulse responses The computation of GIRFs in the case of multivariate nonlinear models is made difficult by the inability to construct analytical expressions for the conditional expectations, E[x t + n |vt , w t − ] and E[xt + n |wt − 1] in Eq (2.7) To deal with this problem, following suggestions of Koop et al (1996), we carry out stochastic simulation to construct the generalized impulse responses We use all available initial data points as histories, which leave 36 data points, and compute impulse responses for 10 consecutive periods Shocks for a particular horizon are randomly drawn from the residuals of the estimated nonlinear model In order to assess possible asymmetries in the effects of positive versus negative and small versus big shocks, we compute impulse response of one variable to positive and negative small and large shocks to the other variable In particular, small positive energy shock is set to one standard error of the residuals from the energy equation, whereas large energy shock is defined as two standard errors Negative small and large energy shocks are defined as negative of one and two standard errors, respectively In a similar way, positive and negative small and large output shocks are defined similarly as positive and negative one and two standard error of the residuals of the output equation, Table The estimates of the LSTVAR model Output equation Constant Δly Δlyt − Δlen Δlent − F(Δlent − 3) F(Δlent − 3) Δly F(Δlent − 3) Δlyt − F(Δlent − 3) Δlen F(Δlent − 3) Δlent − D+ y D− y D+ e D− e Estimated transition function: Residual diagnostic tests Skewness Kurtosis (excess) J–B normality test Ljung–Box Q(1) ARCH(1) Energy equation 0.003 (0.022) −0.010 (0.023) – 0.364 (0.313) −1.734 (0.940)* 1.152 (0.870) 2.033 (0.608)*** – 1.623 (0.793)** −1.074 (0.678) −0.004 (0.023) 0.016 (0.024) – 0.653 (0.328)** 1.696 (0.959)* −1.089 (0.893) −1.137 (0.623)* – −1.556 (0.807)* 0.931 (0.699) 0.034 (0.020)* – −0.018 (0.009)* – – 0.024 (0.010)** – −0.020 (0.012) F(Δlent − 3) = (1 + exp {−5.204(Δlent − + 0.028)})−1 (5.246) (0.009)*** −0.441 [0.237] −0.483 [0.536] 1.941 [0.379] 0.042 [0.838] 3.053 [0.081] 0.360 [0.335] −0.130 [0.867] 1.025 [0.599] 0.104 [0.748] 0.000 [0.994] − + − Note: D+ y , Dy , De , De denote positive and negative dummy variables for the output and energy equations, respectively J–B is Jarque–Berra's test for normality of residuals Ljung–Box Q(j) denotes Ljung and Box (1978) Q-test for residual autocorrelation of order j ARCH(1) is Engel's (1982) LM test for first order autoregressive conditional heteroscedasticity Figures in parenthesis are standard errors of parameter estimates Significance levels of the diagnostic tests are provided in square brackets ***, **, and * denote significance at 1%, 5%, and 10% signicance levels, respectively A Araỗ, M Hasanov / Energy Economics 44 (2014) 259–269 267 Response of Output Growth to Positive and Negative sd Energy Consumption Shock 0.096 +1 sd shock -1 sd shock 0.080 0.064 0.048 0.032 0.016 0.000 -0.016 10 11 Response of Output Growth to Positive and 2sd Energy Consumption Shock 0.096 +1 sd shock +2 sd shock 0.080 0.064 0.048 0.032 0.016 0.000 -0.016 10 11 Response of Output Growth to Negative and 2sd Energy Consumption Shock 0.096 -1 sd shock -2 sd shock 0.080 0.064 0.048 0.032 0.016 0.000 -0.016 10 11 Fig Response of output growth to energy consumption shocks respectively The difference between these forecast values and baseline model is the impulse response for a given shock and particular history In this way, 1000 realizations of impulse responses are calculated for each available history, and average impulse responses are obtained Computed impulse responses are plotted below in Figs and In these figures, responses to negative shocks are plotted with reversed sign so as to compare them with the responses to positive shocks Similarly, responses to two standard error shocks are divided by two so as to compare them to responses to one standard error shocks Fig presents the responses of output growth rate to positive and negative small and large energy shocks As can be seen in Fig 2, the magnitude of the effects of negative energy shocks on output is greater than the effects of positive energy shocks This finding implies that a reduction in energy consumption decreases output much more than an increase in energy consumption boosts output In addition, the computed impulse responses suggest that there is no asymmetry in the effects of big versus small energy consumption shocks on output On the other hand, large negative energy shocks affect output growth rate much more than small negative energy shocks This finding implies that the effects of energy shocks depend not only on the sign, but on the magnitude as well The computed responses of energy consumption to output shocks are plotted in Fig Fig suggests that while positive shocks to output growth rate increases energy consumption, a negative shock to output has almost no effect on energy use This finding implies that although energy consumption increases with increasing output growth rate, a decrease in output growth rate does not affect energy use In addition, we find that small shocks to output growth rate have greater effects on energy use when compared to large output shocks This finding implies that although energy consumption is increasing with output growth rate, the rate of growth of energy consumption is decreasing with higher output growth rates Finally, we find no asymmetry in the effects of big versus small negative output shocks on the energy consumption Policy implications and conclusion In this paper we have examined possible asymmetries in the dynamic interrelationship between energy consumption and economic growth in Turkey for the 1960–2010 period For this purpose, we first estimated a linear VAR model for energy consumption and output growth rate, and tested for linearity of the model The results of the linearity tests suggest that the dynamic interrelationship between energy consumption and output growth rate is inherently nonlinear Then we estimated a nonlinear smooth transition vector autoregressive model, and used generalized impulse response functions to assess dynamic effects of one variable on the other In order to examine nonlinearities in the effects of big versus small and negative versus positive shocks, we have computed generalized responses of variables to one and two standard deviation positive and negative shocks This approach allows us to examine possible effects of moderate and aggressive energy conversing or energy-promoting policies as well as shed light on the dynamics of energy consumption in the future as the economy grows further The computed impulse responses suggest that negative and positive energy shocks affect output growth rate asymmetrically We also find asymmetry in the big versus small negative energy shocks On the 268 A Araỗ, M Hasanov / Energy Economics 44 (2014) 259–269 a) Response of Energy Consumption to Positive and Negative sd Output Growth Shock 0.096 +1 sd shock -1 sd shock 0.080 0.064 0.048 0.032 0.016 0.000 -0.016 10 11 b) Response of Energy Consumption to Positive and 2sd Output Growth Shock 0.096 +1 sd shock +2 sd shock 0.080 0.064 0.048 0.032 0.016 0.000 -0.016 10 11 c) Response of Energy Consumption to Negative and 2sd Output Growth Shock 0.096 -1 sd shock -2 sd shock 0.080 0.064 0.048 0.032 0.016 0.000 -0.016 10 11 Fig Response of energy consumption to output growth shocks other hand, we find no asymmetry in the effects of big versus small positive energy shocks Similarly, we find that negative and positive output shocks affect energy consumption asymmetrically Although we find asymmetry in the effects of small versus big positive output shocks, we find no asymmetry in the effects of negative output shocks The results of this paper shed light on the nature of dynamic interrelationship between energy use and economic growth in Turkey, and hence provide important information for the design of energy policies in Turkey First, we find that energy consumption increases with output, but at declining rates This result implies that higher output growth rates are achieved mainly by increasing employment rather than energy or energy using capital inputs Such an outcome may be observed if firms believe that higher growth rates will not be sustained in the long run and hence hesitate to invest in capital goods In fact, as briefly discussed in Section 2, output fluctuated widely in Turkey as a result of economic crises, which reduced investors' beliefs in Turkey's future economic performance The finding that energy use increases with output at declining rates is also important for policymakers and energy specialists, suggesting that they must take account of such a nonlinear behavior in forecasting energy demand in the future Second, we find that a decrease in output growth rate has a negligible effect on the energy consumption This finding suggests that energy consumption exhibits a downward inertia in the case of Turkey This result implies that producers tend to substitute other production factors, especially labor force, with energy and/or energy intensive capital goods in bad economic conditions In fact, it is a well-established fact that unemployment rises sharply in recession periods but declines very slowly and far after economic recovery starts This was also the case during and after the economic crises in Turkey Therefore, it must not be surprising that a decline in output does not reduce energy use Third, our results imply that a decline in energy use decreases output much more than an increase in energy consumption increases output In addition, we find that a larger decrease in energy consumption affects output much more than a small decrease in energy consumption One of the possible reasons of such an asymmetric behavior may be the use of energy intensive production processes In fact, although energy intensity in Turkey was far below when compared to developed countries, it stayed relatively stable throughout the analysed period In particular, energy intensity measured as kg of oil equivalent energy use per thousand constant dollars GDP fall only to 115 in 2010 from 120 in 1980 Despite successes in industrialization and structural transformation of the Turkish economy over the last five decades, technological progress in Turkey has not been energy saving and hence energy intensity stayed stable over longer periods of time As regards energy policies, this finding implies that aggressive energy conserving policies might be detrimental for output growth and hence for employment as well Therefore, policy authorities must assess the effects of energy conserving policies very carefully, and adopt a gradual, rather than an aggressive energy conserving policies Possibly, most viable energy conserving policies in Turkey are those that promote energy-saving production technologies In particular, incentives for adopting more environment-friendly production processes might be more appropriate and effective than penalizing greenhouse gas emissions in the case of Turkey, and probably, other countries with similar economic structure A Araỗ, M Hasanov / Energy Economics 44 (2014) 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