Energy consumption and economic growth nexus for 17 highly developed OECD countries: Further evidence based on bootstrap-corrected causality tests

Energy Policy 51 (2012) 985–993

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Communication

Energy consumption and economic growth nexus for 17 highly developed OECD countries: Further evidence based on bootstrap-corrected causality tests Ertugrul Yildirim a, Alper Aslan b,n a b

Bulent Ecevit University, Department of Economics, Zonguldak, Turkey Nevsehir University, Department of Economics, Nevsehir, Turkey

H I G H L I G H T S c c c c

This study examines energy consumption, economic growth linkage for 17 developed OECD countries. Lag length selection is important for Denmark, Ireland, Norway and Spain. There exists uni-directional causality running from energy consumption to real GDP for Japan. Bi-directional causality is found for Italy, New Zealand, Norway and Spain.

a r t i c l e i n f o

abstract

Article history: Received 12 December 2011 Accepted 6 September 2012 Available online 30 September 2012

Unlike previous energy consumption-economic growth studies, this study examines the relationship among energy consumption, economic growth, employment and gross fixed capital formation for 17 highly developed OECD countries by employing both the Toda–Yamamoto procedure which based on asymptotic critical values and the bootstrap-corrected causality test, since non-normality of the error term harms the validity of the Toda–Yamamoto procedure. This study finds that there is very small bias due to the assumption of normality. Furthermore using different information criterions, importance of lag length is tested. Findings indicate that selection of lag length is important for Denmark, Ireland, Norway and Spain. It is concluded that while there exists uni-directional causality running from energy consumption to real GDP for Japan, bi-directional causality is found for Italy, New Zealand, Norway and Spain. On the other hand, uni-directional causality from GDP to energy is found for Australia, Canada and Ireland whereas no causal nexus is found for all of other nine countries. Our analyses covering the sample periods imply that Japan, Italy, New Zealand, Norway and Spain should not follow energy conservation policy at the aggregated level, since the reduction of energy damages the economic growth. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Economic growth Energy consumption Causality

1. Introduction While concerns about climate change and global warming is rising in recent years, against the high level of greenhouse gas emissions, the pressure on policy makers increase to take measures such as reduction of fossil energy consumption and increasing use of renewable energy. But the reduction in energy consumption may affect macroeconomic variables such as economic growth. The understanding of the direction of the causality between energy consumption and economic growth is so important, therefore the existence of any causal relationships running from energy

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Corresponding author. Tel.: þ90384 2281110; fax: þ 90384 2152010. E-mail address: [email protected] (A. Aslan).

0301-4215/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2012.09.018

consumption to economic growth would indicate the dependence of the economy on energy with the latter being a stimulus to economic growth. However, in the presence of such causal relationships any structural policies aiming at the reduction of energy consumption might slow economic growth (Tsani, 2010). The causal relationship between economic growth and energy consumption has been studied in a large number of empirical studies with inconsistent results concerning the direction of the relationship that might be due to methodological differences and the time period (Payne, 2009). The causal relationship between energy consumption and economic growth has been rather mixed and synthesized into four hypotheses (Yildirim et al., 2012). The growth hypothesis states a situation in which energy consumption plays an important role in the economic growth process directly and/or as a complement to capital and labor.

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The growth hypothesis is supported, if uni-directional causality is found from energy consumption to economic growth. In this scenario, energy conservation policies aimed at decreasing energy consumption will have negative effects on economic growth. The presence of unidirectional causality from energy consumption to real GDP provides support for the growth hypothesis. The conservation hypothesis signifies that economic growth is the dynamic which causes the consumption of energy sources. The validity of the conservation hypothesis is verified if there is uni-directional causality from economic growth to energy consumption. In this scenario, energy conservation policies which may prevent energy consumption will not have negative impact on economic growth. The feedback hypothesis implies a mutual relationship between energy consumption and economic growth. The feedback hypothesis is supported if bi-directional causality exists between energy consumption and economic growth. In this scenario, although bi-directional causality implies that energy conservation policy harms economic growth in aggregated level, energy policy should be carefully regulated, since one-sided policy selection is harmful for economic growth or ecological balance and budget for energy consumption. In this situation, diversified policy as sectors or energy kinds should be followed, if the direction of causal relationships between energy consumption and economic growth change in disaggregated level. Therefore, while optimal energy policy may need conservation of energy consumption for some sectors or energy kinds, there may be no need for energy conservation policy for the others. The neutrality hypothesis indicates that energy consumption does not affect economic growth. The absence of causality between energy consumption and economic growth provides evidence for the validity of the neutrality hypothesis. In this scenario, energy conservation policies devoted to reducing energy consumption will not have an influence on economic growth. As it can be seen in Table 1, even as the relationship between energy consumption and income has been a well-studied topic, the empirical results, however, have illustrated a lack of consensus among economists. One serious problem is that many studies suffer from omission variable bias such as the labor inputs and investment since those inputs are substantial determinants of economic growth in the Solow growth model. This is because a multivariate model is employed including labor and gross fixed capital formation in aggregate production function. The second problem is the use of the Granger causality tests to find the direction of causation between energy consumption and income in the previous studies1. The third problem is about the lag selection. Two common different criteria (Schwarz, 1978) Bayesian information criterion and the Hannan and Quinn 1979) information criterion) can perform better than the other ones depending on the properties of the true VAR model. The selection of the lag orders of variables is very important because the appropriate lag selection enables researchers to identify the true dynamics of the models (Hatemi-J, 2003). The contribution of our empirical study is threefold. First, in order to remedy the econometric issue in the estimation due to the omission of the relevant variables, this study uses a multivariate causality test by including employment and gross fixed capital formation variables. Second, this study employs the Toda and Yamamoto (1995) procedure and the leveraged bootstrapped causality technique which is suggested by Hacker and Hatemi-J (2006) to avoid unclear results due to the assumption of normality. The third contribution is to pick the true lag order

1 If the assumption of normality is not fulfilled, asymptotic critical values are not valid for causality tests.

by combining the Schwarz (1978) Bayesian information criterion and the Hannan and Quinn (1979) information criterion as suggested by Hatemi-J (2003). These three important aspects will develop clearer results for policy makers. The rest of the paper is organized as follows: The next section describes the data, methodology and the results from empirical analysis. Section 3 presents conclusion and policy implications of the paper.

2. Data, methodology and results The source of real growth rate variables for 17 highly developed OECD countries due to availability of data is IMF International Financial Statistics (IFS). Employment and gross fixed capital formation data are from OECD National Accounts data that is attained from the source OECD data base, and final energy consumption data is obtained from International Energy Agency (IEA) database. Included countries and time periods in the analyses are reported in Table 2. The analysis consists of four steps. The first step is to determine maximum integration order of the variables, since the Toda– Yamamoto procedure needs the additional lag(s) as maximum integration order of the variables. In the analysis, to ensure robustness for the common components of energy consumption per capita, real GDP per capita, employment and gross fixed capital formation, several unit root tests are used including the Phillips and Perron (1988) (PP) test and the Kwiatkowski et al. (1992) (KPPS) test. Table 3 reports the results of these unit root tests. Maximum integration order is used among different unit root tests and models, since Hacker and Hatemi-J (2006) conclude that having more extra lags than the integration order results in less size distortion compared to having less extra lags than the integration order. According to the results, for most of the countries, maximum integration order of the variables are one, I(1). But in the cases of Ireland, Japan, New Zealand, Spain, Sweden, UK and US, maximum integration order are two, I(2). The next step is to pick optimal lag order2. Two of the most successful criteria according to the simulation results presented in the literature are the Schwarz (1978) Bayesian information criterion (SBC) and the Hannan and Quinn (1979) information criterion (HQC). The Schwarz criterion can be represented as follows: 2 ^ Þ þ j n lnT , j ¼ 0,. . .,K SBC ¼ lnðdet O j T ^ where Oj is the maximum likelihood estimate of the variance– covariance matrix O when the lag order used in estimation is j. T is the sample size. The goal is to estimate the largest order for the time series by the j that minimizes the above criterion. Hannan and Quinn (1979) introduces an alternative information criterion.

^ Þ þj HQC ¼ lnðdetO j

2n2 lnðln TÞ , T

j ¼ 0,. . .,K

The earlier studies illustrate that each of these two different criteria can perform better than the others depending on the properties of the true VAR model. But the true VAR model is not known in empirical analysis. When SBC and HQC are used to determine true lag order, it is difficult to know which criterion should be relied on. In this situation, Hatemi-J (2003) suggests combining these two criteria to obtain the following information criterion (HJC):  2  2 ^ Þ þ j n lnT þ2n lnðln TÞ HJC ¼ lnðdetO j ¼ 0,. . .,K j 2T

2

The multivariate ARCH effects and normality are also analyzed.

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Table 1 Summary of recent literature review for energy consumption and economic growth. Study

Methodology

Period

Country

Confirmed hypothesis

Yu and Choi (1985)

Granger causality

1950–1976

Erol and Yu (1987)

Granger causality

1952–1982

Masih, Masih (1998)

Johansen–Juselius

1955–1990 1955–1990 1960–1990 1955–1990 1960–1990 1955–1991

UK, USA, Poland Philippines Korea Japan Italy, Germany Canada France, UK India Pakistan Indonesia Malaysia Singapore Philippines

Neutrality Growth Conservation Feedback Conservation Growth Neutrality Growth Growth Conservation Neutrality Neutrality Neutrality

Cheng (1999) Stern (1993) Ghosh (2002) Soytas and Sari (2003)

Granger causality Co-integration, Granger causality Granger causality Error-correction model

1952–1995 1948–1994 1950–1997 1950–1992

Conservation Growth Conservation Feedback Conservation Conservation Growth

1960–1999

India US India Argentina Italy South Korea Turkey, France, Germany and Japan United States, United Kingdom and Canada New Zealand

1970–1999

Malawi

1965–2000 1960–2001 1968–2005 1970–2003 1980–2003

Sweden Japan Turkey Turkey Gambia, Ghana, Sudan, Zimbabwe, Congo, Senegal Cameroon, Coted’Ivoire, Nigeria, Kenya, Togo 82 Countries

Feedback and Conservation Conservation Conservation Conservation Conservation Conservation

Fatai et al. (2004) Jumbe (2004) Hatemi-J and Irandoust (2005) Lee (2006) Halicioglu (2007) Lise and Van Montfort (2007) Akinlo (2008)

Huang et al. (2008)

Lee and Chang (2008)

Payne (2009) Odhiambo (2010)

Granger causality, Toda and Yamamoto test Granger causality and Errorcorrection model Leveraged bootstrap simulation Toda and Yamamoto Granger causality Co-integration test Autoregressive distributed lag (ARDL) bounds test

Generalized method of moment system

Panel error-correction model

Toda–Yamamoto causality Autoregressive distributed lag (ARDL) bounds test, Granger causality

1972–2002

1971–2002

1959–2006 1972–2006

Low Income Countries Middle and high- income countries 16 Asian countries In the long run In the short run US South Africa

Neutrality

Neutrality

Neutrality

Conservation Neutrality

Growth Neutrality Neutrality Growth

Error-correction model Granger causality

1960–2004 1965–2006

Kenya Congo 15 Transition countries 51 Countries Low income countries Lower middle income countries Upper middle income countries New Zealand South Africa

1971–2009 1970–2006

Portugal 21 African countries

Feedback Feedback

1981–2007 1997–2007

25 OECD countries 27 European countries

Feedback Neutrality

Fang (2011) Tiwari (2011) Fuinhas and Marques (2012)

Granger causality Panel cointegration and panel causality tests Panel Granger causality One-way random effect model, panel causality tests OLS Structural VAR ARDL bounds test

1978–2008 1960–2009 1965–2009

Growth Growth Feedback

Kahsaia et al. (2012)

Granger causality

1980–2007

Yalta and Cakar (2012)

Maximum entropy bootstrap

1971–2007

China India Portugal, Italy, Greece, Spain and Turkey 40 Sub-Saharan African countries China

Acaravci and Ozturk (2010) Ozturk et al. (2010)

Bartleet and Gounder (2010) Menyah and Wolde-Rufael (2010) Shahbaz et al. (2011) Eggoh et al. (2011) Belke et al. (2011) Menegaki (2011)

Panel cointegration tests Panel Granger causality

1990–2006 1971–2005

Growth Conservation Neutrality Conservation Feedback Feedback Feedback Growth

Feedback Neutrality

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Table 2 Countries and time periods. Australia (1964–2009) Austria (1971–2009) Canada (1971–2009) Denmark (1969–2009) Finland (1971–2009)

France (1960–2009) Germany (1971–2009) Ireland (1971–2009) Italy (1971–2009) Japan (1971–2009)

New Zealand (1971–2009) Norway (1972–2009) Spain (1971–2009) Sweden (1962–2009) Turkey (1970–2009)

Using Monte Carlo simulation, Hatemi-J (2003) illustrates that HJC choose the optimal lag order both in the stable and unstable VAR models. Therefore, Hatemi-J Criteria is employed to pick true lag order, as it is displayed in Table 4. Different information criterions mostly choose the same lag length except for the models for Denmark, France, Ireland, Japan, Norway, Spain, Sweden and US. The third step of the analysis is to run the causality test. The causality relationships between growth and energy consumption are frequently analyzed using the Granger causality test. Granger (1969) runs a regression model which relies on asymptotic distribution theory. But, using Monte Carlo simulations, Granger and Newbold (1974) find that if the variables are non-stationary, the regression analysis based on the asymptotic distribution theory does not work well. So the found results can be spurious. Sims et al. (1990) depicted that when the variables are non-stationary, the vector autoregressive (VAR) model cannot be used in level form even if the variables are co-integrated. In this case, based on lag augmented VAR model, Toda and Yamamoto (1995) propose a Wald test statistic that asymptotically has a chi-square distribution irrespective of the order of integration or co-integration properties of the variables in the model. Toda and Yamamoto (1995) suggest the following augmented VAR (pþd) model: xt ¼ v þ A1 xt1 þ    þ Ap xtp þ    þ Ap þ d xtpd þ et

ð1Þ

It is supposed that the order p of the process is known and d is equal to the maximum order of integration of the variables. The Toda–Yamamoto augmented VAR(p þd) model can be explained in a compact way in the following Hacker and Hatemi-J (2006): K ¼ FZ þ c:

ð2Þ

where: K ¼ ðx1 ,. . .,xT Þðn  TÞ matrix, F ¼ ðv,A1 ,. . .,Ap ,. . .,Ap þ d Þðn  ð1þ nðp þdÞÞÞ matrix, 2 3 1 6 7 xt 6 7 6 7 6 xt1 7 6 7 6 7 : Zt ¼ 6 7ðð1 þnðp þdÞÞ  1Þ matrix, 6 7 6 7 : 6 7 6 7 : 4 5 xtpd þ 1 for t ¼ 1,. . .,T, matrix, Z ¼ ðZ 0 ,. . .,Z T1 Þðð1þ nðp þdÞÞ  TÞ matrix,

c ¼ ðe1 ,. . ., eT Þðn  TÞ matrix, Toda and Yamamoto (1995) set up the following modified Wald (MWALD) test statistic for testing the null hypothesis of non-Granger causality: MWALD ¼ ðY fÞ0 ½YððZ 0 ZÞ1  V U ÞY 0 1 ðY fÞ  w2P

ð3Þ

UK (1971–2009) US (1971–2009)

VU ¼the estimated variance–covariance matrix of residuals in Eq. (2), when the null hypothesis of non-Granger causality is not imposed. f ¼vec(F), where vec represents the column stacking operator. The MWALD test statistic is asymptotically w2 distributed, conditional on the assumption that the error terms are normally distributed, with the number of degrees of freedom equal to the number of restrictions to be tested. According to Toda and Yamamoto (1995), their function (Eq. (2)) guarantees the use of asymptotical distribution theory. However, using Monte Carlo simulations, Hacker and Hatemi-J (2006) showed that the MWALD test statistic over rejects the null hypothesis, especially if the error term is characterized by autoregressive conditional heteroscedasticity (ARCH) and non-normality. Furthermore, Hacker and Hatemi-J urged that the asymptotic distribution can be a poor approximation, especially for the small samples that are common in empirical studies. Hacker and Hatemi-J (2006) found that the bootstrapped empirical size for the modified Wald test is close to the correct size in different cases, when the extra lags are greater than or equal to the integration order of both variables, and it is generally closer to the correct size than the asymptotic distribution empirical size. To perform the bootstrap simulations by following Hacker and Hatemi-J (2006), first regression (Eq. (2)) is estimated with the null hypothesis of no Granger causality. For each bootstrap simulation, it is generated the simulated data, Kn. ^ þ cn K n ¼ FZ

ð4Þ

where F^ is the estimated value of the parameters in Eq. (2). That is F^ ¼ KZ 0 ðZZ 0 Þ1 . The bootstrap residuals (cn) are based on T random draws with replacement from the regression’s modified residuals, each with equal probability of 1/T. The mean of the resulting set of drawn modified residuals is subtracted from each of the modified residuals in that set. The modified residuals are the regression’s raw residuals modified to have constant variance, through the use of leverages3. In order to calculate the bootstrap critical values, the bootstrap simulation is run 100,000 times and calculated the MWALD test statistic each time. In this way, it is able to produce the empirical distribution for the MWALD test statistic. For the aim of comparison, to estimate the model consisting of economic growth, energy consumption, employment and gross fixed capital both the Toda–Yamamoto (TY) procedure and the bootstrap-corrected causality test were employed. To estimate the bootstrap-corrected causality test, the code written by Hacker and Hatemi-J (2006) was used. Table 5 indicates the results of the TY procedure and the bootstrap-corrected causality test. MWALD statistical values were compared to 1%, 5% and 10% bootstrap critical values and TY probability values indicate the significance levels of MWALD statistics. According to results in Table 5, the TY procedure and the bootstrap-corrected causality test reach the same results. But the statistical significance levels of the MWALD test are

where:  ¼ the Kronocker product: Y ¼ ap  nðp þ dÞ

3 The estimated raw residuals are rescaled with the leverages approach as noted in Davison and Hinkley (1999) to have constant variance. For more details on leverage adjustment is referred to Davison and Hinkley (1999) and Hacker and Hatemi-J (2006).

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Table 3 Results of unit root tests. Country

Australia

Austria

Canada

Denmark

Finland

France

Germany

Ireland

Italy

Japan

New Zealand

Norway

Spain

Sweden

Turkey

UK

US

Variable

Growth Energy consumption Employment Gross fixed capital Growth Energy consumption Employment Gross fixed capital Growth Energy Consumption Employment Gross fixed capital Growth Energy consumption Employment gross fixed capital Growth Energy consumption Employment gross fixed capital Growth Energy consumption Employment Gross fixed Capital Growth Energy consumption Employment Gross fixed Capital Growth Energy consumption Employment gross fixed capital Growth Energy consumption Employment Gross fixed capital Growth Energy consumption Employment gross fixed capital Growth Energy consumption Employment Gross fixed capital Growth Energy consumption Employment Gross fixed capital Growth Energy consumption Employment Gross fixed capital Growth Energy consumption Employment Gross fixed capital Growth Energy consumption Employment Gross fixed capital Growth Energy consumption Employment Gross fixed capital Growth Energy consumption Employment Gross fixed capital

Phillips–Perron

KPSS

None

Intercept

Intercept and trend

Intercept

Intercept and trend

I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(1) I(1) I(1) I(2) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(2) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(2)

I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(1) I(1) I(2) I(2) I(0) I(1) I(1) I(1) I(1) I(1) I(2) I(2) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(2) I(2) I(0) I(0) I(1) I(2) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(2) I(0) I(1) I(1) I(2)

I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(1) I(1) I(2) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(2) I(1) I(1) I(1) I(2) I(1) I(1) I(1) I(1) I(1) I(2) I(2) I(2) I(0) I(1) I(1) I(2) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(2) I(0) I(1) I(1) I(2)

I(0) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(0) I(1) I(1) I(2) I(1) I(1) I(1) I(1) I(1) I(2) I(2) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(2) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(0) I(1) I(2) I(1)

I(0) I(0) I(1) I(1) I(0) I(1) I(0) I(0) I(0) I(0) I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(0) I(1) I(1) I(1) I(1) I(1) I(0) I(0) I(0) I(1) I(0) I(0) I(1) I(1) I(2) I(0) I(0) I(1) I(0) I(0) I(2) I(2) I(1) I(1) I(2) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(2) I(1) I(0) I(2) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(0) I(1) I(1) I(2)

changed. For example, while according to the result of TY procedure, null hypothesis of no Granger causality from energy consumption to economic growth for Spain is rejected at 1%

significance level, the bootstrap-corrected causality test rejects the null hypothesis at 10% significance level. If the significance level at 10% is enough to validity of the MWALD test, one can

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Table 4 Lag length selection. Countries

AIC

SBC

HQC

HJC

Countries

AIC

SBC

HQC

HJC

Australia

[1] (7.8941) [1] (10.4715) [1] (9.3336) [1] (11.8716) [1] (10.4843) [1] (7.4699) [1] (9.6667) [1] (12.1280) [1] (8.3997)

[1] (15.1505) [1] (17.4547) [1] (16.3167) [1] (18.9527) [1] (17.4674) [1] (14.8194) [1] (16.6499) [1] (19.1111) [1] (15.3828)

[1] (14.6263) [1] (16.8727) [1] (15.7347) [4] (18.1718) [1] (16.8854) [2] (14.3035) [1] (16.0679) [2] (18.1805) [1] (14.8009)

[1] (14.8884) [1] (17.1637) [1] (16.0257) [1] (18.6709) [1] (17.1764) [1] (14.5708) [1] (16.3589) [2] (18.7043) [1] (15.0919)

Japan

[1] (4.0965) [1] (12.4486) [1] (10.0374) [1] (7.6675) [1] (10.9163) [1] (15.0988) [1] (8.7831) [1] (9.6609)

[1] (9.4098) [1] (19.4317) [1] (16.9632) [1] (14.6507) [1] (18.2232) [1] (22.1335) [1] (15.7663) [1] (16.6441)

[2] (9.0423) [1] (18.8497) [2] (16.0722) [4] (12.8829) [2] (17.5183) [1] (21.5608) [1] (15.1843) [4] (15.3417)

[1] (9.2261) [1] (19.1407) [2] (16.6047) [4] (13.8723) [1] (17.9681) [1] (21.8471) [1] (15.4753) [2] (16.2853)

Austria Canada Denmark Finland France Germany Ireland Italy

New Zealand Norway Spain Sweden Turkey UK US

a

Akaike Information Criteria (AIC), Hannan–Quinn (HQ), Schwarz Bayesian Information Criteria (SBC), Hatemi-J Criteria (HJC). The numbers in brackets are the optimal lag lengths and min test statistics are in the parenthesis.

conclude that the results of the Toda–Yamamoto procedure are valid. Table 5 shows that there are four bidirectional and four unidirectional causal nexus. For Italy, New Zealand, Norway and Spain; there is bidirectional causal nexus between energy consumption and economic growth. So the findings support the feedback hypothesis for these countries. For Australia, Canada and Ireland; there are unidirectional causal nexus from economic growth to energy consumption. Therefore, in the case of these countries, the conservation hypothesis is supported. Unidirectional causal nexus is found from energy consumption to economic growth for Japan. So the growth hypothesis is supported in the case of Japan. For all of the other countries, no causal nexus is found between energy consumption and economic growth. In the case of the other countries, the neutrality hypothesis is supported. Table 6 compares the results in this analysis with earlier studies which employ causality test. Soytas and Sari (2003), Fatai et al. (2004), Lee (2006) and Zachariadis (2007) use bivariate-real GDP and energy consumption-model. Lee and Chien (2010) employs the model which consists of three variables-real GDP per capita, real capital stock per capita, energy consumption per capita. Only the model of Payne (2009) includes real GDP, real gross fixed capital formation, employment and energy consumption. In the empirical studies, not only sample period but also included variables are changed. So these differences in the empirical analysis may explain the mixed findings. To investigate the effect of adding new variables on the findings, some variables are omitted in the analysis. It is found that adding new variables such as employment and gross fixed capital in the model, causal relationships turned to bidirectional for New Zealand, Norway and Spain. For Canada, causal nexus from real GDP to energy consumption appears whereas no causal relationship is found when employment and gross fixed capital are omitted. The causal relationship from GDP to energy consumption disappears when new variables is added for the UK. In the case of the other countries, there is no change in the causal relationships when employment and gross fixed capital are omitted. In the last step, to observe if the selection of lag length is important for the achieved results, different lag length selected by HQC and SBC are used to estimate the causal nexus between energy consumption and economic growth. Table 7 reports the causality tests results based on HQC.

According to Table 7, using HQC for lag selection changes only significance level of the MWALD test for Denmark. The choice of HJC leads to no causal relationship between energy consumption and real GDP, while HQC estimation finds unidirectional causal nexus from energy consumption to real GDP. Since HJC chooses optimal lag order for stable and unstable VAR model, it can be concluded that HJC is more rational for the selection of lag length. Causality test results based on SBC are indicated in Table 8. The results of causality test based on SBC tend to change causal relationship for Norway, Ireland and Spain. While the causality test based on HJC finds bidirectional causal relationship for Norway, the choice of HQC leads to unidirectional causal nexus from energy consumption to real GDP. When HQC is used to select lag length, the findings of bidirectional causal nexus with the selection of lag length by HJC disappear. The choice of SBC yields to no causal nexus for Spain. The HQC based estimation finds no causal relationship for Ireland, whereas the HJC based estimation finds unidirectional causal nexus from real GDP to energy consumption. Since HJC choose optimal lag order for stable and unstable VAR model, it can be concluded that HJC leads to more rational selection of lag length.

3. Conclusion and policy implications Previous studies on the causal relationship between energy consumption and growth are based on asymptotic distributions. However, if the assumption of normality is not fulfilled, asymptotic distributions perform inaccurately. Using the Toda and Yamamoto procedure and the leveraged bootstrapped simulation techniques, the potential causal relationship among energy consumption, growth, employment and gross fixed capital formation was investigated by using the data from 17 highly developed OECD countries. The Toda and Yamamoto procedure and the bootstrap-corrected causality tests reach the nearly same results. So there is very small bias due to the assumption of normality. But the selection of lag length is important for Denmark, Ireland, Norway and Spain. Since HJC chooses optimal lag order for stable and unstable VAR model, HJC leads to more rational selection of lag length. It is concluded that there exist bidirectional causal relationships between energy consumption and real GDP for Italy, New Zealand, Norway and Spain. Since the findings support the

E. Yildirim, A. Aslan / Energy Policy 51 (2012) 985–993

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Table 5 Causality test results based on HJC. Countries

Australia Austria Canada Denmark Finland France Germany Ireland Italy Japan New Zealand Norway Spain Sweden Turkey UK US

Energy consumption does not Granger cause growth

Growth does not Granger cause energy consumption

MWALD

MWALD

0.073428 1.451257 0.250923 0.776892 1.537151 2.461179 0.804821 1.391602 4.738348** 5.495255** 5.284696** 9.970662** 14.15573* 1.704473 0.48811 1.312862 0.276797

TY Prob.

0.7864 0.2283 0.6164 0.3781 0.215 0.1167 0.3697 0.4987 0.0295** 0.0191** 0.0215** 0.0068*** 0.0068*** 0.1917 0.4848 0.2519 0.8708

Bootstrap critical value 1%

5%

10%

7.628 7.711 7.938 7.8 7.805 7.405 7.724 12.529 8.101 8.425 8.031 12.272 30.696 7.516 8.158 8.375 13.47

4.15 4.236 4.348 4.28 4.273 4.085 4.267 7.314 4.41 4.594 4.371 7.287 16.577 4.218 4.385 4.494 7.827

2.88 2.926 2.998 2.95 2.927 2.826 2.956 5.402 3.022 3.144 2.998 5.371 12.058 2.888 3.004 3.066 5.76

TY Prob.

9.228006*** 0.158236 3.499349* 1.155247 0.029732 0.073663 0.565627 13.16988*** 16.29448*** 2.665696 6.48887** 11.44503** 21.13946** 0.976393 1.043487 0.042179 1.391213

0.0024*** 0.6908 0.0614* 0.2825 0.8631 0.7861 0.452 0.0014*** 0.0001*** 0.1025 0.0109** 0.0033*** 0.0003*** 0.3231 0.307 0.8373 0.4988

Bootstrap critical value 1%

5%

10%

7.618 7.79 7.832 7.876 7.816 7.591 7.802 12.453 8.16 8.311 8.137 11.49 32.84 7.797 8.039 8.384 13.095

4.196 4.234 4.309 4.338 4.292 4.208 4.282 7.401 4.471 4.462 4.394 6.924 17.82 4.241 4.365 4.543 7.619

2.926 2.931 2.992 2.97 2.949 2.928 2.963 5.42 3.058 3.055 3.003 5.141 12.961 2.942 2.989 3.092 5.618

a The notations ***, **, and * imply significance at the 1%, 5%, and 10% significance level, respectively, based on the bootstrap critical values. TY prob is estimated probability value by the Toda–Yamamoto procedure for the MWALD stat.

Table 6 Comparison of the our findings with those of previous studies. Countries/ studies

Soytas and Sari (2003)

Australia

Fatai et al. (2004)

Lee (2006)

Zachariadis (2007)

Payne (2009)

Lee and Chien (2010)

GDP-EC [1960–1999]

Austria Canada

GDP a EC [1950–1992]

GDP’EC [1965–2001]

GDP-EC [1960–2004]

GDP’EC [1965–2001]

GDP’EC [1950–1992] GDP’EC [1950–1992]

GDP-EC [1960–2001] GDP a EC [1971–2001]

GDP a EC [1960–2004] GDP a EC [1960–2004]

GDP-EC [1960–2001] GDP a EC [1971–2001]

GDP-EC [1950–1992] GDP’EC [1950–1992]

GDP-EC [1960–2001] GDP-EC [1960–2001]

GDP a EC [1960–2004] GDP’EC [1960–2004]

GDP’EC [1960–2001] GDP-EC [1960–2001]

GDP-EC [1960–2004] GDP a EC [1960–2004]

GDP’EC [1960–2001] GDP a EC [1960–2001]

Denmark Finland France Germany Ireland Italy Japan New Zealand

GDP-EC [1960–1999]

Norway Spain GDP a EC [1960–2001]

Sweden Turkey UK US

GDP’EC [1950–1992] GDP a EC [1950–1992] GDP a EC [1950–1992]

GDP a EC [1960–2001] GDP2EC [1960–2001]

GDP aEC [1949–2006]

Our findings

GDP-EC [1964–2009] GDP a EC [1971–2009] GDP-EC* [1971–2009] GDP a EC [1969–2009] GDP a EC [1971–2009] GDP a EC [1960–2009] GDP a EC [1971–2009] GDP-EC [1971–2009] GDP2EC [1971–2009] GDP’EC [1971–2009] GDP2EC* [1971–2009] GDP2EC* [1972–2009] GDP2EC* [1971–2009] GDP a EC [1962–2009] GDP a EC [1970–2009] GDP a EC* [1971–2009] GDP a EC [1971–2009]

a

Abbreviations are defined as follows: EC ¼energy consumption and GDP ¼real gross domestic product. EC-GDP means that the causality runs from energy consumption to growth. GDP-EC means that the causality runs from growth to energy consumption. EC2GDP means that bi-directional causality exists between energy consumption and growth. EC a GDP means that no causality exists between energy consumption and growth. * denotes the changing causal relationship when employment and gross fixed capital are omitted. b

feedback hypothesis, in aggregated level, energy conservation policy harms the economic growth for these countries. In addition, feedback relationship between energy consumption and

economic growth increases the effect of energy conservation on economic growth. First energy conservation leads to the reduction of economic growth and then low level of economic growth

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Table 7 Causality test results based on HQC. Countries

Denmark France Japan Sweden US

Energy consumption does not Granger cause growth

Growth does not Granger cause energy consumption

MWALD

MWALD

14.47742** 3.703521 6.475784* 1.701138 5.433158

TY Prob.

0.0059*** 0.157 0.0392** 0.4272 0.2457

Bootstrap critical value 1%

5%

10%

19.52 10.84 12.436 11.435 37.161

12.152 6.734 7.446 6.947 20.034

9.401 5.028 5.463 5.198 14.499

7.497697 0.110435 3.776056 1.148127 2.232171

TY Prob.

0.1118 0.9463 0.1514 0.5632 0.3283

Bootstrap critical value 1%

5%

10%

20.987 10.919 12.744 11.536 33.159

13.197 6.743 7.379 7.046 17.926

10.14 5.076 5.448 5.247 12.975

a

The notations ***, **, and * imply significance at the 1%, 5%, and 10% significance level, respectively, based on the bootstrap critical values. TY prob is estimated probability value by the Toda–Yamamoto procedure for the MWALD stat.

Table 8 Causality test results based on SBC. Countries

Ireland Norway Spain US

Energy consumption does not Granger cause growth

Growth does not Granger cause energy consumption

MWALD

MWALD

0.279238 9.617946*** 0.883166 0.032472

TY Prob.

0.5972 0.0019*** 0.3473 0.857

Bootstrap critical value 1%

5%

10%

8.425 7.793 8.181 8.267

4.548 4.3 4.422 4.461

3.107 2.979 3.043 3.072

0.042152 2.426778 0.085388 0.616941

TY Prob.

0.8373 0.1193 0.7701 0.4322

Bootstrap critical value %1

%5

%10

8.376 7.725 8.088 8.341

4.509 4.221 4.35 4.538

3.074 2.931 2.982 3.091

a

The notations ***, **, and * imply significance at the 1%, 5%, and 10% significance level, respectively, based on the bootstrap critical values. TY prob is estimated probability value by the Toda–Yamamoto procedure for the MWALD stat.

causes the lower level of energy consumption and again economic growth decreases. So these countries should not follow energy conservation policy for total economy since it causes two opposite effects on the economy. That is causal relationships between energy consumption and economic growth may change in disaggregated level. Therefore, while optimal energy policy may need conservation of energy consumption for some sectors or energy kinds, there may be no need for energy conservation policy for the others. For Japan, unidirectional causal nexus is found from energy consumption to economic growth. Since the growth hypothesis is supported, energy conservation policy should not be followed in Japan. But there is no feedback effect in the case of Japan, the effect of energy conservation policy on economic growth may be smaller than those of the patterns of Italy, New Zealand, Norway and Spain. For Australia, Canada and Ireland, there are unidirectional causal nexus from economic growth to energy consumption. Since the analysis in this study supports the conservation hypothesis in the sample periods, energy conservation policy may be recommended for these countries. Last, for all of the other countries, no causal nexus is found between energy consumption and economic growth. But these may be virtual findings. There may be break(s) in causal relationship between energy consumption and economic growth for these countries. Unfortunately subdividing the sample leads to inadequate observation number for the time series causality analyses.

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