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© 2013 Nicolaus Copernicus University. All rights reserved.  
http://www.dem.umk.pl/dem 

D Y N A M I C  E C O N O M E T R I C  M O D E L S  
DOI: http://dx.doi.org/10.12775/DEM.2013.003  Vol. 13 (2013) 51−68 

Submitted October 15, 2013  ISSN 
Accepted December 30, 2013 1234-3862 

Monika Papież, Sławomir Śmiech* 

Economic Growth and Energy Consumption  
in Post-Communist Countries: a Bootstrap Panel 

Granger Causality Analysis∗∗ 

A b s t r a c t. The aim of this paper is to identify Granger causality between energy consump-
tion and economic growth in post-communist countries in the period 1993 to 2011. Bootstrap 
panel Granger causality test was used as a research tool in order to accommodate for country-
specific heterogeneity and to avoid the problem of cross-sectional dependence.  
The analysis allowed for the verification of the hypothesis regarding the links between eco-
nomic growth and energy consumption in nine countries. The hypotheses were confirmed: the 
growth hypothesis in three countries and the feedback hypothesis in one country. 
K e y w o r d s: energy consumption, economic growth, bootstrap panel Granger causality 
test, energy efficiency.  

J E L Classification: C33, Q43. 

Introduction 
 Improving energy security is the priority of the EU policy. Growing de-
pendence of the EU on energy import (its volume is predicted to increase 
from 50% at present to 80% in 2035) makes it imperative to limit energy 

                                                 
* Correspondence to: Monika Papież, Cracow University of Economics, Department of 

Statistics, Rakowicka 27, 31-510 Krakow, Poland e-mail: papiezm@uek.krakow.pl; Sławomir 
Śmiech, Cracow University of Economics, Department of Statistics, Rakowicka 27, 31-510 
Krakow, Poland e-mail: smiechs@uek.krakow.pl. 

∗∗ We are grateful for the financial support of the Polish National Science Centre (the pro-
ject DEC-2011/03/B/HS4/01134). 



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consumption (measured by CO2 emission) and replace non-renewable en-
ergy sources with renewable ones. On the other hand, energy consumption is 
inextricably linked with economic growth (the Environmental Kuznets 
Curve). Dynamically developing countries consume more and more energy, 
and curbing energy consumption can lead to stagnation or the drop in eco-
nomic growth rate.  
 The analysis of links between economic growth and energy consumption 
was addressed by numerous research studies beginning with a pioneering 
study by Kraft and Kraft (1978). Four hypotheses regarding causal relations 
between energy consumption and economic growth can be found in the lit-
erature dealing with this topic: the growth hypothesis, the conservation hy-
pothesis, the feedback hypothesis and the neutrality hypothesis. The growth 
hypothesis assumes that there are countries in which the growth of energy 
consumption is an important element of their economic development. In this 
case, the energy conservation policy advocating the introduction of limits in 
energy consumption will have a negative impact on economic growth. The 
growth hypothesis is based on unidirectional Granger causality running from 
energy consumption to economic growth. The conservation hypothesis 
claims that the changes in energy consumption stem from the changes in 
economic activity, and energy conservation policy does not negatively affect 
economic growth. This hypothesis is confirmed if unidirectional Granger 
causality running from GDP growth to energy consumption can be observed. 
The feedback hypothesis assumes that there are countries with bi-directional 
Granger causality between energy consumption and economic growth. The 
neutrality hypothesis states that there are countries in which GDP does not 
depend on energy consumption and vice versa.   
 As Karanfil (2009) demonstrates in his survey of empirical literature 
devoted to this issue, the relations between economic growth and energy 
consumption are not unambiguous. The differences can be attributed to dif-
ferent econometric approaches, differently specified time frames and differ-
ent sets of variables used in each of those studies. 
 The occurrence of the relationship between economic growth and energy 
consumption is related to the changes in energy efficiency. Energy efficiency 
aims at reducing the amount of energy required to provide products and ser-
vices in a given country. Most studies underline a positive influence of en-
ergy efficiency on economy and the environment. For example, Sarkar and 
Singh (2010) show that energy efficiency programmes can: conserve natural 
resources, reduce the environmental pollution and carbon footprint of the 
energy sector, reduce a country’s dependence on fossil fuels, thus enhancing 
its energy security, ease infrastructure bottlenecks and impacts of temporary 



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power shortfalls, as well as improve industrial and commercial competitive-
ness through reduced operating costs. However, rebound effects (see Turner, 
2009; Turner and Hanley, 2011 for a recent review) can appear. When en-
ergy becomes more productive, and its price falls, the increase of energy use 
through the substitution effect can be observed.  
 This paper examines the relationship between energy consumption and 
economic growth in nine Eastern European countries and the Baltic states. 
It focuses on the production side model of the energy consumption-growth 
nexus, with labour and capital included in it (Stern, 1993). The analysis cov-
ers the period from 1993 to 2011. Such a choice was dictated by the need to 
cover the relations in the analysed countries, all of which witnessed rapid 
political and economic changes in the 1990s. 
 The aim of the analysis was to investigate the relations between energy 
consumption and economic growth in selected Eastern European countries 
and the Baltic states on the basis of the relations between overall energy 
efficiency gains (industry, transport, households) in 2000–20101 and coun-
tries' economic growth in the same period. The following research hypothe-
ses were formulated: 

a) Countries which increased energy efficiency the most will confirm the 
growth hypothesis, feedback hypothesis or conservation hypothesis (bi-
directional unidirectional causality between energy consumption and 
economic growth).  

b) Countries in which energy efficiency was not considerably increased 
will confirm the neutrality hypothesis.  

 The hypotheses result from the following reasoning. The former group of 
countries had to bear the costs of the increase in energy efficiency on the one 
hand, and, on the other hand, modernisation allows for reducing the amount 
of energy used and, consequently, its costs, which should result in the ap-
pearance of causal relations between energy production and economic 
growth. In the latter group of countries the effects mentioned are non-
existent, which rules out any relations between them.  
 We applied a bootstrap panel causality approach proposed by Kónya 
(2006), which allows for simultaneous examination of cross-sectional de-
pendence.  
 The paper consists of the following sections. Section 1 presents the most 
important findings from studies dealing with energy-economy nexus in Cen-
tral and Eastern European countries. In Section 2 the relations between over-

                                                 
1 The choice of the period for the analysis was dictated by the availability of the data pro-

vided by  http://www.odyssee-indicators.org. 



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54

all energy efficiency gains and economy growth are described. Section 3 
introduces methodology used in the study, and Section 4 presents the data 
and results obtained. The paper ends with the conclusion and the interpreta-
tion of the results.  
 The paper contributes to the existing literature because the analysis fo-
cuses on countries from Eastern Europe and the Baltic states. Most of them 
have not been analysed from this angle before. They have similar GDP and 
a similar level of energy consumption. As member states of the EU, they are 
obliged to follow a common energy policy. Thanks to these similarities, the 
data used were characterised by cross-sectional dependence. The application 
of the methodology suggested by Kónya (2006) made correct inference on 
causalities in this situation possible. 
 Two additional variables, labour and capital, were used to compare the 
models, which makes this study more general in scope than other studies. 
Our findings may provide valuable information for developing more effec-
tive energy policies with respect to both energy consumption and environ-
mental protection.  

1. Review of Literature 
 Existing literature offers a wide range of perspectives and insights into 
the issue of energy consumption-growth nexus, which sometimes report 
contradicting results. It can be divided into country-specific case studies and 
multi-country studies (Karanfil, 2009). In both types various econometric 
methods, the choice of the period analysed, and the choice of control vari-
ables can be found.  
 Taking into consideration the methodological perspective, four genera-
tions of contributions were identified (Belke et al., 2011; Costantini and 
Martini, 2010). The first-generation studies were based on VAR methodol-
ogy (Kraft and Kraft, 1978) and assumed that the time series were stationary. 
The second-generation studies accounted for non-stationarity and applied 
Engle-Granger two-step procedure to test pairs of variables for cointegrating 
relationships. The third-generation studies used multivariate estimators 
(Johansen, 1991). This approach allowed for more than two variables in 
cointegration relationship and for analysing causality both in the short- and 
long-run simultaneously. The fourth-generation studies were based on panel 
methods testing for unit roots, cointegration and Granger causality. Using 
panel cointegration has several advantages. It allows for higher degrees of 
freedom, reduces multicollinearity between regressors, and improves the 
power of the cointegration test, especially in case of annual data. The main 



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55

disadvantage of this approach is the need to assume cross-sectional inde-
pendence, which is difficult to satisfy in a panel data. What is more, different 
countries are treated as an entity. As a result, it is impossible to identify the 
difference in the dynamic relationship between energy consumption and 
economy (slope homogeneity). As a result, it is impossible to identify the 
difference in the dynamic relationship between energy consumption and 
economy (slope homogeneity). What is more, in most studies based on panel 
models (except panel VAR, see surveys by Canova and Ciccarelli (2013), 
which has not been used in such analyses so far) different countries are 
treated as an entity. 
 In spite of a substantial number of studies concerning relations between 
energy consumption and economic growth, not all Eastern European coun-
tries were analysed, for example, the Baltic states were not included in any 
of them, and among the countries from Central and Eastern Europe only the 
following ones were studied: Poland (Gurgul and Lach, 2011a, 2011b, 
2012), Romania (Apergis and Danuletiu, 2012), Albania, Bulgaria, Hungary, 
and Romania (Ozturk and Acaravci, 2010). Gurgul and Lach (2011b) found 
that energy consumption Granger caused GDP in Poland during the last dec-
ade. They (Gurgul and Lach, 2011a) also investigated causal relations be-
tween coal consumption and economic growth. In another paper Gurgul and 
Lach (2012) investigated causal interdependences between electricity con-
sumption and GDP in Poland. Apergis and Danuletiu (2012) showed that 
energy consumption Granger caused GDP in Romania in the period 2000-
2011. However, Ozturk and Acaravci (2010) did not find any relationship 
between energy consumption and real GDP in Romania and Bulgaria, while 
found bidirectional strong Granger causality between these variables in 
Hungary in the period 1980–2006. Using a two-way fixed effects model, 
Menegaki and Ozturk (2013) confirmed bidirectional causality between 
growth and political stability, capital and political stability, and capital and 
fossil energy consumption for 26 European countries in a multivariate panel 
framework over the period 1975–2009. 

2. Energy Efficiency in Eastern European Countries  
and the Baltic States 

 Energy efficiency is considered to be one of the most cost effective ways 
of meeting the demands of sustainable development and lower fossil fuel 
dependence. So, the efficient use of energy is an important topic in public 
policy debates. Unfortunately, in literature dealing with energy issues there 
is no consensus on the appropriate method for defining and measuring en-



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56

ergy efficiency. The authors of this paper adopted the definition of energy 
efficiency given by the Odyssee project2. The “ODEX” energy efficiency 
indicator provides an overall perspective of energy efficiency trends by sec-
tor and combines the trends of indicators by end-use or sub-sector. It repre-
sents a better proxy to evaluate energy efficiency trends at an aggregate level 
(overall economy, industry, households, transport, services). The ODEX 
indicators by sector (industry, transport, households) are calculated from unit 
consumption trends by sub –sector (or end-use or mode of transport) by ag-
gregation of unit consumption indices by sub-sector in one index for the 
sector on the basis of the current weight of each sub-sector in the sector’s 
energy consumption. The ODEX can be defined as the ratio between the 
actual energy consumption of the sector in year t and the sum of fictive en-
ergy consumption of each underlying sub-sector/end-use that would have 
been observed in year t had the unit consumption of the sub- sector been that 
of a reference year. The energy efficiency gains are calculated from ODEX 
and reflect efficiency gains since 2000.  
 Figure 1 presents relations between overall energy efficiency gains (in-
dustry, transport, households) in the period 2000–2010 and real growth of 
GDP per capita in the same period for Eastern Europe and the Baltic states. 
This diagram identifies two groups of countries with similar levels of energy 
efficiency and a similar level of economic development. The first group 
comprises countries with high overall energy efficiency gains (above 18 
percent) and high (and medium) growth of GDP per capita in the period 
2000–2010. The second group includes countries with low overall energy 
efficiency gains (below 14 percent) and low (and medium) growth of GDP 
per capita in the period 2000–2010. 
 In the analysed Eastern European countries and the Baltic states in the 
period 2000–2010 the mean energy efficiency index for the whole economy 
(ODEX) decreased by 15.3 percent. Countries with the highest improvement 
in energy efficiency in the period analysed include: Poland (25.7 percent), 
Bulgaria (23 percent), Latvia (21 percent), Lithuania (19.8 percent), and 
Romania (19.4 percent). Countries with the lowest improvement in energy 
efficiency in the same period include: Slovakia (3.7 percent), the Czech Re-
public (5.2 percent), Estonia (7.3 percent), and Hungary (13 percent). 
It should be noticed that countries which did not improve their energy effi-
ciency substantially also had a lower increase growth in GDP per capita 

                                                 
2 ODYSSEE is a project between ADEME, the EIE programme of the European Commis-

sion/DGTREN and energy efficiency agencies, or their representative, in the 27 countries in 
Europe plus Norway and Croatia (http://www.odyssee-indicators. org). 



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(except for Slovakia3) than countries with a considerable increase in energy 
efficiency. 

 
Figure 1.  Relations between overall energy efficiency gains (industry, transport, 

households) in the period 2000-2010 and growth of GDP per capita in the 
period 2000–2010 

3. Methodology 
 The choice of a suitable method allowing for the analysis of causality for 
panel data requires the assessment of cross-sectional dependence. If cross-
sectional dependence exists, the seemingly unrelated regressions (SUR) are 
more efficient then the ordinary least-squares (OLS) (Zellner, 1962). Kónya 
(2006) proposed a method which takes into account the characteristics of 
cross-sectional dependence. Therefore, before considering causality, we 
investigated the characteristics of panel data. The tools used for bootstrap 
panel causality test are presented below. 

                                                 
3 A specific situation of Slovenia is a result of numerous phenomena which are described 

in detail in the study: Energy Efficiency Policies and Measures in Slovakia in 2012, 
ODYSSEE- MURE 2010,   
http://www.odyssee-indicators.org/publications/PDF/slovakia_nr.pdf [30.12.2013]. 



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3.1. Tests of Cross-Sectional Dependence 

 An important issue to be considered in a panel data analysis is testing for 
cross-sectional dependence across countries, because a shock that affects one 
country may spillover on other countries.  
 Let us consider the standard panel data model: 

ititiiit uy ++= x'βα ,  (1) 

where Ni ,,2,1 …=  represents the cross section dimension, Tt ,,2,1 …=  
refers to the time series dimension, itx  is a (K × 1) vector of observed re-
gressors (individual-specific as well as common regressors). The individual 
intercepts iα  and the slope coefficients iβ  are defined on a compact set and 

allowed to vary across i. For each i, ( )2,0~ iuit IIDu σ  for all t, although they 
may exhibit cross- sectional dependence. 
 The null hypothesis of no-cross-sectional dependence –

( ) 0:0 =jtituuCovH  for all t and ji ≠  – is tested against the alternative hy-

pothesis of cross-sectional dependence – ( ) 0:1 ≠jtituuCovH , for at least one 
pair of ji ≠ . In literature several tests for error cross-sectional dependence 
have been proposed.  
 Breusch and Pagan (1980) proposed a Lagrange multiplier (LM) statistic 
for testing the null hypothesis of no-cross-sectional dependence, which is 
defined as: 

∑ ∑
−

= +=

=
1

1 1

2ˆ
N

i

N

ij
ijTLM ρ ,  (2) 

where ijρ̂  is the sample estimate of the pair wise Pearson correlation coeffi-
cient of the residuals from the Ordinary Least-Squares (OLS) estimation of 
Eq. (1) for each i. LM is asymptotically distributed as chi-squared with 

2/)1( −NN degrees of freedom under the null hypothesis, as ∞→T , with 
N fixed. It is important to note that the LM test is valid for relatively small N 
and sufficiently large T. So, where ∞→T  and ∞→N , Pesaran (2004) 
proposed the following LM statistic for the cross-sectional dependence test 
(the so-called CD test): 

( ) ( )∑ ∑
−

= +=

−
−

=
1

1 1

2 1ˆ
1

1 N

i

N

ij
ijlm T

NN
CD ρ . (3) 



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Under the null hypothesis, the lmCD  test converges to the standard normal 
distribution. 
 However, this test is likely to exhibit substantial size distortions for N 
large and T small, a situation that can frequently arise in empirical applica-
tions. To overcome this problem, Pesaran (2004) proposed the following 
simple alternative test, which is based on the pair-wise correlation coeffi-
cients rather than their squares used in the LM test:  

( ) ⎟
⎟
⎠

⎞
⎜
⎜
⎝

⎛

−
= ∑ ∑

−

= +=

1

1 1

ˆ
1

2 N

i

N

ij
ijNN

TCD ρ , (4) 

and showed that under the null hypothesis of no cross-sectional dependence 
( )1,0NCD d→  for ∞→N  and T sufficiently large. Pesaran (2004) con-

cluded that it is also clear that, since the mean of CD is exactly equal to zero 
for all fixed 1+> KT  and N, the test is likely to have good small sample 
properties (for both N and T small). 
 In Pesaran et al. (2008) the authors concluded that the CD test has an 
important drawback, namely it will lack power in certain situations where 
the population average pairwise correlations are zero, although the underly-
ing individual population pairwise correlations are non-zero. That is why 
Pesaran et al. (2008) proposed a bias-adjusted test, which is a modified ver-
sion of the LM test, by using the exact mean and variance of the LM statis-
tic. The bias-adjusted LM test is as follows: 

( )
( )

∑ ∑
−

= +=

−−

−
=

1

1 1

2ˆ
1

2 N

i

N

ij Tij

Tijij
adj v

kT
NN

LM
μρ

,  (5) 

where Tijμ  and 2
Tijv  are respectively the exact mean and variance of 

( ) 2ˆijkT ρ−  provided in Pesaran et al. (2008 p.108). Pesaran et al. (2008) 
showed that under the null hypothesis of no cross-sectional dependence with 

∞→T  first followed by ∞→N , the statistics adjLM  follow an asymp-
totic standard normal distribution. 

3.2. Bootstrap Panel Granger Causality Test  

 Taking into account cross-sectional dependence and heterogeneity across 
country groups requires a method of analysis which would be able to capture 
both these features. The bootstrap panel causality approach proposed by 
Kónya (2006) seemed to be a suitable method. This approach uses seemingly 



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60

unrelated regression (SUR) and, therefore, is able to deal with cross-
sectional dependence across the members of the panel.  
 The test for the direction of causality is based on Wald tests with coun-
try-specific bootstrap critical values. That is why it does not impose a joint 
hypothesis across all members of the panel. Using Kónya (2006) approach 
allows for the identification of specific countries in which a Granger causal 
relationship exists. What is more, Kónya (2006) claimed that 'this approach 
does not require pretesting for unit roots and cointegration', which is impor-
tant 'since the unit-root and cointegration tests in general suffer from low 
power'. On the other hand, ignoring potential (common) stochastic trends 
results in a situation in which the results of the suggested procedure can be 
used only for the evaluation of short-term causality (one-period-ahead fore-
cast).  
 Kónya's (2006) panel causality approach models the data as a system of 
two sets of the following equations4: 

1 1 1

1

1, 1,1 1,1, 1, 1,1, 1, 1,1, 1,
1 1 1

1,1, 1, 1,1,
1

,

mly mlx mlz

t l t l l t l l t l
l l l

ml

l t l t
l

y y x z

v
ν

α β δ γ

ϑ ε

− − −
= = =

−
=

= + + + +

+ +

∑ ∑ ∑

∑
 

…. (6) 
1 1 1

1

, 1, 1, , , 1, , , 1, , ,
1 1 1

1, , , 1, ,
1

,

mly mlx mlz

N t N N l N t l N l N t l N l N t l
l l l

mlv

N l N t l N t
l

y y x z

v

α β δ γ

ϑ ε

− − −
= = =

−
=

= + + + +

+ +

∑ ∑ ∑

∑
 

and 
2 2 2

2

1, 2,1 2,1, 1, 2,1, 1, 2,1, 1,
1 1 1

2,1, 1, 2,1,
1

,

mly mlx mlz

t l t l l t l l t l
l l l

mlv

l t l t
l

x y x z

v

α β δ γ

ϑ ε

− − −
= = =

−
=

= + + + +

+ +

∑ ∑ ∑

∑
 

…. (7) 

                                                 
4 It is possible to include a deterministic component into the system of equations.  



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61

2 2 2

2

, 2, 2, , , 2, , , 2, , ,
1 1 1

2, , , 2, ,
1

,

mly mlx mlz

N t N N l N t l N l N t l N l N t l
l l l

mlv

N l N t l N t
l

x y x z

v

α β δ γ

ϑ ε

− − −
= = =

−
=

= + + + +

+ +

∑ ∑ ∑

∑
 

where tiy ,  denotes economic growth (in country i and t period), tix ,  refers to 
energy consumption, tiz ,  is the capital formation, tiv ,  is the labour participa-
tion rate5, N denotes the number of countries in the panel  
( Ni ,,2,1 …= ), t is time period ( Tt ,,2,1 …= ), and l is the number of lags in 
equations. titi ,,2,,1 ,εε  are supposed to be correlated contemporaneously 
across equations (due to common random shocks).  
 The system of equations allows for testing unidirectional and bi-
directional Granger causality for each country separately. There is unidirec-
tional Granger causality running from economic growth to energy consump-
tion (the equivalent of the conservation hypothesis) if in (7) not all i,2β 's are 
zero, but in (6) all i,1δ 's are zero. There is unidirectional Granger causality 
running from energy consumption to economic growth in country i (the 
equivalent of the growth hypothesis) if not all i,1δ 's are zero, but all  

i,2β 's are zero in (7). There is bi-directional Granger causality between en-
ergy consumption and economic growth if neither all i,1δ 's nor all i,2β 's are 
zero. Finally, there is no Granger causality between energy consumption and 
economic growth if all i,1δ 's and all i,2β 's are zero.  
 The country-specific bootstrap6 critical values are obtained as follows7: 

1. A system of equations (6) is estimated under the null hypothesis of non-
causality running from energy consumption to economic growth (that is 
imposing the 0,,1 =liδ  restriction for all i and l) and the residuals are ob-
tained: 

                                                 
5 Z and ν are treated as an auxiliary variable, and they will not be directly involved in the 

Granger causality analysis. 
6 On bootstrapping in general see e.g. Horowitz (2003). On bootstrapping in SUR models 

see Atkinson et. al (1992), and Rilstone and Veall (1996). 
7 We present a procedure for testing Granger causality running from X to Y. Similar steps 

are required for testing causality running from Y to X. 



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DYNAMIC ECONOMETRIC MODELS 13 (2013) 51–68  

62

1 1 1

0 , , , 1, 1, , 1, 1, , 1, 1, , 1,
1 1 1

ˆ ˆˆ ˆ ,
mly mlz ml

H i t i t i i l t l i l t l i l t l
l l l

e y y z v
ν

α β γ ϑ− − −
= = =

= − − − −∑ ∑ ∑  (8) 

for 1, ,i N= …  and Tt ,,1…= . From these residuals N×T matrix [ ]tiHe ,,0
 

is developed. 
2. These residuals are re-sampled by randomly selecting a full column 

form the matrix [ ]tiHe ,,0
, and denote the selected bootstrap residuals as 

[ ]*
,,0 tiHe  where *1,2,3,..., .t T=  

3. The bootstrap sample of Y is generated under the assumption of no cau-
sality running from energy consumption to economic growth, i.e.: 

1 1 1

0

* * *
, 1, 1, , , 1, , 1, 1, , 1, , ,

1 1 1

ˆ ˆˆ ˆ .
mly mlz ml

i t i i l i t l i l t l i l t l H i t
l l l

y y z v e
ν

α β γ ϑ− − −
= = =

= + + + +∑ ∑ ∑  (9) 

4. *
,tiy  is substituted for tiy ,  and a system of equations is re-estimated 

(without any restrictions). The Wald test for each country is implied by 
the no-causality null hypothesis.   

5. The empirical distributions of the Wald test statistics are developed by 
repeating steps 2 – 4. The bootstrap critical values are specified by se-
lecting appropriate percentiles of these sampling distributions.  

 Eventually, Wald test statistics obtained from original series are com-
pared with the bootstrap critical values. 
 Specifying the number of lags in all equations is a crucial step in Kónya's 
approach. Following Kónya (2006), we decided to allow for different lags in 
each system but did not allow for different lags across countries. Assuming 
that the number of lags ranges from 1 to 4, we estimated all equations and 
used the Akaike Information Criterion (AIC) to determine the optimal solu-
tion. The Akaike Information Criterion8 (AIC) was evaluated as: 

T
qNAICk

22||ln += W , (10) 

where W stands for estimated residual covariance matrix, N is the number of 
equations, q is the number of coefficients per equation, T is the sample size.  

                                                 
8 Kónya (2006) presented also Schwartz Information Criterion. 



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4. Data and Empirical Results 
 The analysis of causal relationship between energy consumption and 
economic growth based on the annual panel data was carried out over the 
period 1993–2011 for nine European countries: Bulgaria, the Czech Repub-
lic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia. 
Two variables from the World Bank Development Indicators were chosen 
for the analysis: real Gross Domestic Product per capita (GDP) in constant 
2000 U.S. dollars and energy consumption (EC), represented by energy use 
in kg of oil equivalent per capita.  
 Taking into consideration rapid economic changes experienced by the 
countries analysed, a set of variables was extended to include real gross 
fixed capital formation per capita (K) in constant 2000 US dollars as a proxy 
of capital and labour participation rate (L)9. All variables were in natural 
logarithms.  
 Till 1989 Eastern European countries and the Baltic states were under the 
communist rule with centrally planned economies. In 1989 communism fell 
in Bulgaria, Czechoslovakia, Hungary, Poland, and Romania. After the dis-
solution of the Soviet Union in 1991, Estonia, Latvia, and Lithuania reap-
peared on the map, and in 1993 Czechoslovakia was divided into two coun-
tries: the Czech Republic and Slovakia. That is why year 1993 was chosen as 
an initial period of the analysis of causality between economic growth and 
energy consumption. 

Table 1.  Cross-sectional dependence tests  

Variable Cross-sectional dependence test 
LM CDLM CD LMadj 

GDP 150,62*** 13,51*** 6,23*** 21,654*** 
EC 324,84*** 30,50*** 17,84*** 36,375*** 

Note: ***, **, and * indicate significance at the 1, 5, and 10% levels, respectively. 

 The first step in analysing panel data Granger causality is testing for 
cross-sectional dependence. Table 1 shows the results obtained for four dif-
ferent cross-sectional dependence test statistics: LM (Breusch and Pagan, 
1980), CDLM (Pesaran, 2004), CD (Pesaran, 2004), and LMadj (Pesaran et al., 
2008). The results indicate that for all countries with significance level  
p = 0.05 we reject the null hypothesis of no cross-sectional dependence 
among the four variable examined. These findings show that a shock which 
                                                 

9 The use of real gross fixed capital as a proxy of capital follows the works by Sari and 
Soytas (2007) in assuming that under the perpetual inventory method with a constant depre-
ciation rate, the variance in capital is closely related to the change in investment. 



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occurred in one post-communist country will be transmitted to other coun-
tries.  
 The existence of cross-sectional dependence in these countries means 
that it is justified to use the bootstrap panel Granger causality testing 
method. For each system of equations the number of lags was chosen ac-
cording to the AIC criterion10. Additionally, specifications incorporating 
deterministic trend were taken into account. 
 The results from the bootstrap11 panel Granger causality analysis are 
reported in Table 2 and Table 3. 

Table 2.  The bootstrap panel Granger causality analysis  

Countries 

H0: Energy consumption does not Granger cause GDP  
(H1: EC → GDP) 

Wald statis-
tics  Bootstrap critical value 

10% 5% 1% 
Bulgaria 12.915**  7.638 11.186 21.785 

Czech Republic 0.686  7.715 11.176 19.899 
Estonia 0.574  10.351 16.084 35.753 
Hungary 0.317  7.751 11.784 20.686 
Latvia 9.265*  8.842 13.331 24.626 

Lithuania 3.378  8.856 14.397 29.298 
Poland 18.917**  6.597 10.029 20.283 

Romania 8.372*  8.245 11.548 25.975 
Slovakia 2.235  7.584 12.652 23.370 

Note: ***, **, and * indicate significance at the 1, 5, and 10% levels, respectively. Bootstrap critical 
values are obtained from 10,000 replications. 

 Table 2 and Table 3 present the results obtained for nine transition coun-
tries in Eastern Europe and the Baltic states. The results confirm the growth 
hypothesis for Bulgaria, Poland (both at the significance level 5%) and Ro-
mania (at the significance level 10%). This means that economies in those 
three countries can be called ‘energy dependent’, and that energy consump-
tion plays an important role in their economic growth, both directly and indi-
rectly in the production process as a complementary factor to labour and 
capital. Consequently, we may conclude that energy is a limiting factor to 
economic growth and, hence, shocks to energy supply will have an impact 

                                                 
10 We used the AIC criterion to compare the specifications with and without a linear trend. 

Finally, we constructed SUR with one lag and without a linear trend.  
11 Following the original paper of Kónya (2006) and several others, e.g. Nazlioglu et. al 

(2011), we used 10000 replications in the procedure. Andrews and Buchinsky (2000) provide 
an exact method of evaluating the adequacy of the chosen number of replications. 



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on economic growth. Additionally, it can be claimed that excessive energy 
protection and a reduction in energy consumption may lead to stagnation.  

Table 3.  The bootstrap panel Granger causality analysis  

Countries 

H0: GDP does not Granger cause energy consumption 
(H1: GDP → EC) 

Wald statis-
tics  Bootstrap critical value 

10% 5% 1% 
Bulgaria 0.425  13.234 20.697 41.039 

Czech Republic 0.330  12.162 18.667 37.114 
Estonia 0.696  8.989 13.623 29.087 
Hungary 1.244  12.947 20.043 42.007 
Latvia 23.841**  13.824 22.365 61.726 

Lithuania 4.246  9.716 14.063 26.881 
Poland 12.580  17.470 25.420 51.422 

Romania 2.910  11.334 17.488 34.534 
Slovakia 0.632  10.430 15.556 31.457 

Note: ***, **, and * indicate significance at the 1, 5, and 10% levels, respectively. Bootstrap critical 
values are obtained from 10,000 replications. 

 The feedback hypothesis was confirmed only for Latvia. This means that 
energy consumption and economic growth are jointly determined and af-
fected at the same time. 
 The results support the neutrality hypothesis for 5 countries: the Czech 
Republic, Hungary, Estonia, Lithuania, and Slovakia. The neutrality hy-
pothesis states that energy consumption and economic growth are not sensi-
tive to one another. Therefore, any policy with respect to the consumption of 
energy, conservative or expansive, is expected to have a negligible effect on 
economic growth.  

Conclusions and Discussion 
 We investigated the relations between energy consumption and eco-
nomic growth. Labour and real gross fixed capital formations were added to 
the analysis in order to avoid the problem of impact of omitted-variables 
bias. The methodology used in the study, Kónya's procedure (2006), firstly, 
allowed for the assessment of causality in countries with cross-sectional 
dependence, and, secondly, avoided the problem of incorrect specification 
connected with unit root and cointegration.  
 Empirical results confirm the linkages between energy consumption and 
economic growth in four of nine countries. The growth hypothesis was con-
firmed in 3 countries: Bulgaria, Poland, and Romania. Energy consumption 
seems to be the bottleneck in their economic growth, and, hence, shocks to 



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energy supply will have an impact on this growth. Latvia is the only country 
for which the feedback hypothesis was confirmed. In such countries energy 
consumption and economic growth are complementary to each other. 
 The results obtained in the study indicate that causality between energy 
consumption and economic growth can be equated with energy efficiency. 
The group of countries with the best energy efficiency indicators consisted 
of almost the same countries as the group in which the growth hypothesis, 
the conservation hypothesis or the feedback hypothesis were confirmed (the 
only exception was Latvia). Thus, it can be assumed that the hypotheses 
formulated in the introduction were confirmed. Unfortunately, the empirical 
strategy used in this study did not allow for showing the reactions of eco-
nomic growth to the changes in energy consumption and vice versa. So, it is 
not possible to conclude whether modernisation of economies, which can be 
equated to energy efficiency, exerts a positive or negative impact on econ-
omy (the rebound effect).  
 In conclusion, a special situation of Poland and Bulgaria, countries con-
firming the growth hypothesis, should be mentioned. They rely on coal as 
the most important source of energy. In 2011 in Poland it accounted for 56.2 
percent and in Bulgaria for 42.3 percent of the country’s total primary en-
ergy consumption. The pressure on those countries is especially heavy be-
cause of the amount of their emission of carbon dioxide, which are the high-
est for coal. The necessity of limiting the use of this energy source without 
access to alternative energy sources can be a serious threat for their econo-
mies.  

References  
Apergis, N., Danuletiu, D. (2012), Energy Consumption and Growth in Romania: Evidence 

from a Panel Error Correction Model, International Journal of Energy Economics and 
Policy, 2 (4), 348-356, http://www.econjournals.com/index.php/ijeep/article/view/316 

Andrews, D. W. K., Buchinsky, M. (2001), Evaluation of a Three-step Method for Choosing 
the Number of Bootstrap Repetitions, Journal of Econometrics, 103, 345–386, 
DOI: http://dx.doi.org/10.1016/S0304-4076(01)00047-1. 

Atkinson, S. E., Wilson P.W. (1992), The Bias of Bootstrapped versus Conventional Standard 
Errors in the General Linear and SUR Models, Econometric Theory, 8, 258-275,   
DOI: http://dx.doi.org/10.1017/S0266466600012792. 

Belke, A., Dobnik, F., Dreger, C. (2011), Energy Consumption and Economic Growth: New 
Insights into the Cointegration Relationship, Energy Economics, 33 (5), 782–789, 
DOI: http://dx.doi.org/10.1016/j.eneco.2011.02.005. 

Breusch, T., Pagan, A. (1980), The Lagrange Multiplier Test and its Application to Model 
Specifications in Econometrics, Reviews of Economics Studies, 47, 239–253. 

Canova, F., Ciccarelli, M. (2013), Panel Vector Autoregressive Models: A Survey, Working 
Paper Series 1507, European Central Bank, http://ssrn.com/abstract=2201610. 



Economic Growth and Energy Consumption in Post-Communist Countries...  

DYNAMIC ECONOMETRIC MODELS 13 (2013) 51–68  

67

Costantini, V., Martini, C. (2010), The Causality between Energy Consumption and Eco-
nomic Growth: a Multi-sectoral Analysis Using Non-stationary Cointegrated Panel 
Data, Energy Economics, 32 (3), 591–603,   
DOI: http://dx.doi.org/10.1016/j.eneco.2009.09.013. 

Gurgul, H., Lach, Ł. (2011a), The Role of Coal Consumption in the Economic Growth of the 
Polish Economy in Transition, Energy Policy, 39, 2088–2099,   
DOI: http://dx.doi.org/10.1016/j.enpol.2011.01.052. 

Gurgul, H., Lach, Ł. (2011b), The Interdependence Between Energy Consumption and Eco-
nomic Growth in the Polish Economy in the Last Decade. Managerial Economics, 9, 
25–48, http://www.managerial.zarz.agh.edu.pl/EM_on_line/Managerial%20Econo-
mics%20%209%20%282011%29.pdf. 

Gurgul, H., Lach, Ł. (2012), The Electricity Consumption versus Economic Growth of the 
Polish Economy, Energy Economics, 34(2), 500–510,   
DOI: http://dx.doi.org/10.1016/j.eneco.2011.10.017. 

Horowitz, J. L. (2003), The Bootstrap in Econometrics, Statistical Science, 18, 211–218,  
DOI: http://dx.doi.org/10.1214/ss/1063994976. 

Johansen, S. (1991), Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian 
Vector Autoregressive Models, Econometrica, 59 (6), 1551–1580,   
DOI: http://dx.doi.org/10.2307/2938278. 

Karanfil, F. (2009), How Many Times Again Will We Examine the Energy–income Nexus 
Using a Limited Range of Traditional Econometric Tools?, Energy Policy, 36,  
1191–1194, DOI: http://dx.doi.org/10.1016/j.enpol.2008.11.029. 

Kónya, L. (2006), Exports and Growth: Granger Causality Analysis on OECD Countries with 
a Panel Data Approach, Economic Modelling, 23, 978–992,   
DOI: http://dx.doi.org/10.1016/j.econmod.2006.04.008. 

Kraft, J., Kraft, A. (1978), On the Relationship between Energy and GNP, Journal of Energy 
Development, 3, 401–403. 

Menegaki, A. N., Ozturk, I. (2013), Growth and Energy Nexus in Europe Revisited: Evidence 
from Fixed Effects Political Economy Model, Energy Policy, 61, 881–887,   
DOI: http://dx.doi.org/10.1016/j.enpol.2013.06.076. 

Nazlioglu, S., Lebe, F., Kayhan, S. (2011), Nuclear Energy Consumption and Economic 
Growth in OECD Countries: Cross-sectionally Dependent Heterogeneous Panel Cau-
sality Analysis, Energy Policy, 39, 6615–6621,   
DOI: http://dx.doi.org/10.1016/j.enpol.2011.08.007. 

ODYSSEE- MURE 2010, (2012), Energy Efficiency Policies and Measures in Slovakia in 
2012, Monitoring of EU and National Energy Efficiency Targets, Slovak Innovation 
and Energy Agency, Bratislava,   
http://www.odyssee-indicators.org/publications/PDF/slovakia_nr.pdf [30.12.2013] 

Ozturk, I., Acaravci, A. (2010), The Causal Relationship between Energy Consumption and 
GDP in Albania, Bulgaria, Hungary and Romania: Evidence from ARDL Bound Test-
ing Approach, Applied Energy, 87, 1938–43,   
DOI: http://dx.doi.org/10.1016/j.apenergy.2009.10.010. 

Pesaran, M. H. (2004), General Diagnostic Tests for Cross Section Dependence in Panels. 
Cambridge Working Papers in Economics No. 0435. Faculty of Economics, University 
of Cambridge, DOI: http://www.dspace.cam.ac.uk/handle/1810/446. 

Pesaran, M. H., Ullah, A., Yamagata, T. (2008), A Bias-adjusted LM Test of Error Cross-
section Independence, The Econometrics Journal, 11, 105–127,   
DOI: http://dx.doi.org/10.1111/j.1368-423X.2007.00227.x. 



 Monika Papież, Sławomir Śmiech 

DYNAMIC ECONOMETRIC MODELS 13 (2013) 51–68  

68

Rilstone, P., Veall, M. R. (1996), Using Bootstrapped confidence intervals for improved 
inferences with seemingly unrelated regression equations, Econometric Theory, 12, 
569–580, DOI: http://dx.doi.org/10.1017/S026646660000685X. 

Sarkar, A., Singh, J. (2010), Financing Energy Efficiency in Developing Countries—Lessons 
Learned and Remaining Challenges, Energy Policy, 38, 5560–5571,   
DOI: http://dx.doi.org/10.1016/j.enpol.2010.05.001. 

Soytas, U., Sari, R. (2007), The Relationship Between Energy and Production: Evidence from 
Turkish Manufacturing Industry, Energy Economics, 29, 1151–1165,   
DOI: http://dx.doi.org/10.1016/j.eneco.2006.05.019. 

Stern, D. I. (1993), Energy and Economic Growth in the USA, Energy Economics, 15,  
137–150, DOI: http://dx.doi.org/10.1016/0140-9883(93)90033-N. 

Turner, K. (2009), Negative Rebound and Disinvestment Effects in Response to an Improve-
ment in Energy Efficiency in the UK Economy, Energy Economics, 31,  
648-666, DOI: http://dx.doi.org/10.1016/j.eneco.2009.01.008. 

Turner, K., Hanley, N. (2011), Energy Efficiency, Rebound Effects and the Environmental 
Kuznets Curve, Energy Economics, 33, 709–720,   
DOI: http://dx.doi.org/10.1016/j.eneco.2010.12.002. 

Zellner, A. (1962), An Efficient Method of Estimating Seemingly Unrelated Regressions and 
Tests for Aggregation Bias, Journal of the American Statistical Association, 57,  
348–368, DOI: http://dx.doi.org/10.1080/01621459.1962.10480664. 

Wzrost gospodarczy i zużycie energii w krajach postkomunistycznych 
- bootstrapowa panelowa analiza przyczynowości 

Z a r y s  t r e ś c i. Celem artykułu jest identyfikacja zależności przyczynowych (w sensie 
Grangera) pomiędzy zużyciem energii i wzrostem gospodarczym w krajach Europy Środko-
wo Wschodniej oraz w krajach Bałtyckich w okresie 1993–2011. Jako narzędzie badawcze 
wykorzystano procedurę bootstrapowej panelowej analizy przyczynowości zaproponowaną 
przez Kónya (2006). Procedura ta pozwala na wnioskowanie w przypadku występowania 
zależności przestrzennych w badanej próbie i nie wymaga wstępnej analizy stacjonarności 
oraz umożliwia opis relacji dla poszczególnych analizowanych obiektów. Przeprowadzone 
badanie wskazuje na prawdziwość hipotezy wzrostu w przypadku trzech krajów oraz hipotezy 
sprzężenia zwrotnego w przypadku jednego kraju. Otrzymane wyniki pozwalają podejrzewać, 
że zależność przyczynowa pomiędzy wzrostem gospodarczym i konsumpcją energii jest 
związana z zmianami efektywności energetycznej.  

S ł o w a  k l u c z o w e: zużycie energii, wzrost gospodarczy, bootstrapowa panelowa analiza 
przyczynowości, efektywność energetyczna.  

Acknowledgements 
We thank the anonymous referee for providing constructive comments and suggestions and 
help in significantly improving the contents of this paper.  




