GDP, energy consumption and [606865]

GDP, energy consumption and
financial development in Italy
Cosimo Magazzino
Department of Political Sciences, Roma Tre University, Rome, Italy
Abstract
Purpose –This study aims to explore the relationship among energy consumption, real income, financial
development and oil prices in Italy over the period 1960-2014.
Design/methodology/approach –Different econometric techniques –such as the General Methods of
Moment (GMM) or the AutoRegressive Distributed Lags (ARDL) bounds test –are usually used in the
empirical analysis. Moreover, both the Toda and Yamamoto causality tests and the Granger causality tests
are applied to the data.
Findings –The results of unit root and stationarity tests show that the variables are non-stationary at
levels, but stationary in first-differences form, or I(1). The ARDL bounds F-test reveals an evidence of a long-
run relationship among the four variables at 1% signi ficance level. Moreover, an increase in real GDP and oil
prices has a signi ficant effect on energy consumption in the long run. The coef ficients of estimated error
correction term are also negative and statistically signi ficant. In addition, the paper explores the causal
relationship between the variables by using a VAR framework, with Toda and Yamamoto but also Grangercausality tests, within both multivariate and bivariate systems. The findings indicate that energy
consumption is affected by real GDP.
Originality/value –The study also filled the literature gap of applying ARDL technique to examine this
relevant issue for Italy.
Keywords GDP, Regression, Italy, Time series analysis, Energy consumption,
Financial development
Paper type Research paper
1. Introduction
Italy has some indigenous production of oil and natural gas, but both oil and gas production
will progressively decline in the coming years. In 2012, Italy ’s total domestic oil production
met only 7.7 per cent of its domestic demand. Italy relies heavily on imports and is theworld ’s largest net importer of electricity ( Magazzino, 2014a ). In 2011, 47.5 billion kWh was
imported, and only 1.8 billion kWh exported, this net import level being typical of the pastdecade. As of 2011, the transport sector contributed most to Italy ’sfinal energy
consumption, with 30.4 per cent, with the residential (24.8 per cent) and industrial (22.8 percent) sectors also contributing signi ficantly. Thus, it should be clear why it is crucial for
Italy to analyze the relationship among energy consumption, financial development and real
income ( Magazzino, 2012 ).
The nexus between economic growth and financial development, as well as energy
consumption and economic growth, has been the subject of intense research in the pastdecades. Recent studies have documented that financial development can affect energy use.
The author thanks the participants to the conference “Energy Quest 2016 ”, International Conference
on Energy Production and Management in the 21st Century, 6-8 September 2016, Ancona, Italy, for
their useful comments and advices. The author is also indebted to the anonymous referees and theeditor for their valuable suggestions. However, the usual caveats apply.IJESM
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International Journal of Energy
Sector Management
Vol. 12 No. 1, 2018
pp. 28-43
© Emerald Publishing Limited
1750-6220DOI 10.1108/IJESM-01-2017-0004The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1750-6220.htm
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Financial development helps industrial growth, creates demand for new infrastructure and,
thus, positively impacts energy use. Nevertheless, the empirical evidence remains
controversial and ambiguous. Financial development may attract foreign direct investment,
so as to boost economic growth and increase carbon emissions. Financial development is
important because it can increase the economic ef ficiency of a country ’sfinancial system
and this can affect economic activity and the demand for energy. If financial development is
found to affect the demand for energy, then this relationship can affect energy policy and
carbon emissions strategies. Improvement in monetary transmission mechanism, as a result
offinancial liberalization, also encourages savings and investment and enhances economic
growth. Literature shows that liberalization of financial markets leads to economic growth.
Although several papers examine the relationship among energy consumption, economic
growth and financial development, few studies concern Italy.
In the present paper, the relationship among real GDP, energy consumption and financial
development in Italy has been investigated for the period 1960-2014, using time-series
methodologies. The results might help to de fine and implement the appropriate energy
development policies in Italy.
Different econometric techniques –such as the General Methods of Moment (GMM) or
the AutoRegressive Distributed Lags (ARDL) bounds test –are usually used in the
empirical analysis. Our study also filled the literature gap of applying the ARDL technique
to examine this relevant issue for Italy. Moreover, we apply both the Toda and Yamamoto
causality tests and the Granger causality tests to our data. In fact, previous studies devoted
to the Italian case analyzed the relationship among economic activity, energy consumption
andfinancial development through standard Granger causality approach.
Besides, the Introduction, the outline of this paper proceeds as follows: Section 2 provides
a survey of the economic literature on the nexus among energy consumption, real income
andfinancial development. Section 3 contains an overview of the applied empirical
methodology and a brief discussion of the data used. Section 4 discusses our empirical
results. Section 5 presents some concluding remarks, and finally, Section 6 gives
suggestions for future research.
2. Literature review
The relationship between financial development and energy consumption has newly started
to be discussed in energy economics literature ( Çoban and Topcu, 2013 ). As shown in the
survey of the literature presented below, both time-series and panel data studies have been
published in the past decade.
As concerns time-series studies, Aliet al. (2015) , using an ARDL bounds test framework,
analyzed the dynamics of financial development, economic growth, energy prices and
energy consumption in Nigeria for the period of 1972Q1-2011Q4. The error correction model
(ECM) results show that in the short run, financial development has a signi ficant negative
impact on fossil fuel consumption. However, energy prices have a positive and signi ficant
influence on the consumption of fossil fuel. Mahalik and Mallick (2014) investigated the
relationship among energy consumption, economic growth and financial development in
India using annual data for the period 1971-2009. The ARDL approach suggests that energy
consumption is positively and signi ficantly affected by proportion of urban population in
total population, while the same is negatively and signi ficantly impacted by financial
development, economic growth and proportion of industrial output in total output. Salman
and Atya (2014) test the causality flow among financial development, economic growth and
energy consumption in Algeria, Egypt and Tunisia. The ECM results are mixed. The study
ofAbalaba and Dada (2013) analyzed the relationship among energy consumption, realFinancial
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output, financial development, monetary policy rate and consumer prices, providing –via
standard Granger causality tests –weak evidence in support of a long-run relationship
between energy consumption and economic growth. Moreover, energy consumption
positively in fluenced output growth in the short run. Islam et al. (2013) studied the
relationship among financial development, energy consumption and GDP in Malaysia,
covering the years 1971-2009. The results of the vector error correction model (VECM)Granger causality approach suggest that energy consumption is in fluenced by economic
growth and financial development, both in the short and the long run, but the population –
energy relation holds only in the long run. Ozturk and Acaravci (2013) examined the causal
relationship among financial development, trade, economic growth, energy consumption
and carbon emissions in Turkey for the period 1960-2007. The bounds test yields evidence ofa long-run relationship among per capita carbon emissions, per capita energy consumption,per capita real income, the square of per capita real income, openness and financial
development. Results for the existence and direction of Granger causality show that neithercarbon emissions per capita nor energy consumption per capita causes real GDP per capita,but employment ratio causes real GDP per capita in the short run. Jalil and Feridun (2011)
investigated the impact of financial development, economic growth and energy
consumption on environmental pollution in China from 1953 to 2006, using the ARDLbounds testing procedure. The results of the Granger causality tests reveal a negative signfor the coef ficient of financial development, suggesting that financial development in China
has not taken place at the expense of environmental pollution. On the contrary, financial
development has led to a decrease in environmental pollution. Farhani and Ozturk (2015)
examined the causal relationship among CO
2emissions, real GDP, energy consumption,
financial development, trade openness and urbanization in Tunisia over the period of 1971-
2012. The results of the Granger causality tests reveal a positive sign for the coef ficient of
financial development, suggesting that the financial development in Tunisia has taken place
at the expense of environmental pollution, with a positive monotonic relationship betweenreal GDP and CO
2emissions.
With regard to panel data analyses, Chang (2015) analyzed the nonlinear effects of financial
development and income on energy consumpt ion in a sample of 53 countries for the period
1999-2008. It emerges a single-threshold effect on energy consumption when private credit,
domestic credit, value of traded stocks and stock market turnover are used as financial
development indicators. Zeren and Koc (2014) analyzed seven newly industrialized countries,
over the period 1971-2010, reaching mixed results via Hacker –Hatemi causality tests and
Hatemi-J asymmetric causality tests. Çoban and Topcu (2013) investigated the financial
development –energy consumption nexus in the EU over the period 1990-2011 by using the
system-GMM model. No signi ficant relationship is found in the EU-27. The empirical results,
however, provide strong evidence of the impact of the financial development on energy
consumption in the old members. Shahbaz et al. (2013a) e x a m i n e dt h el i n k a g e sa m o n g
economic growth, energy consumption, financial development, trade openness and CO 2
emissions over the period 1975Q1-2011Q4 for Indonesia. The results of the VECM Granger
causality analysis indicate that economic grow th and energy consumption increase emissions,
while financial development and trade openness compact it. Shahbaz et al. (2013b) investigated
the relationship between energy use and econom ic growth in the case of China over the period
1971-2011. The ARDL bounds testing a pproach showed that energy use, financial
development, capital, exports, imports and in ternational trade have a positive impact on
economic growth. The Granger causality analysis revealed a unidirectional causal relationship
running from energy use to economic growth. Fi nancial development and energy use Granger-
cause each other. Al-mulali and Sab (2012a) explored the impact of energy consumption andIJESM
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CO2emission on the economic and financial development in 19 countries in the period 1980-
2008. The empirical findings show that energy consumption enables these countries to achieve
high economic and financial development. However, the high development that these countries
have achieved in the pate three decades increased the CO 2emission. A related study was
conducted by Al-mulali and Sab (2012b) , where the impact of energy consumption and CO 2
emission on GDP growth and financial development in 30 sub-Saharan African countries was
investigated, from 1980 to 2008. The results indicate that energy consumption had played an
important role to increase both economic growth and the financial development in those
economies, although with the consequence of high pollution. Shahbaz and Lean (2012) assessed
the relationship among energy consumption, financial development, economic growth,
industrialization and urbanization in Tunisi a from 1971 to 2008. The ARDL bounds testing
approach results con firm the existence of a long-run relat ionship among energy consumption,
economic growth, financial development, industrializati on and urbanization in Tunisia. The
VECM Granger causality analysis shows the presence of a bidirectional causality betweenfinancial development and industrialization, which reveals that financial development and
industrialization are complementary. Sadorsky ’s (2011) study examined the impact of financial
development on energy consumption in a sample o f nine Central and Eastern European frontier
economies. The empirical results sho w a positive relationship between financial development
a n de n e r g yc o n s u m p t i o n . Sadorsky (2010) , using a GMM estimation technique, explored the
impact of financial development on energy consumpt ion in a sample of 22 emerging countries,
in the years 1990-2006. The empirical results show a positive and statistically signi ficant
relationship between financial development and energy consumption.
With regard to studies on the Italian case, Magazzino (2012) explored the relationship
between disaggregate energy production and real aggregate income in Italy by undertaking
cointegration analyses and Granger causality tests using annual data from 1883 to 2009. The
long-run causality analysis shows a bi-directional flo wb e t w e e ne a c hs o u r c eo fe n e r g ya n dG D P
in the years 1946-2009, except for the nucleo-thermoelectric energy. Magazzino (2014c)
examined the relationship between CO
2emissions, energy consumption and economic growth
in Italy over the period 1970-2006, showing a lack of cointegration among these three variables.Lee and Chien (2010) studied the dynamic linkages among en ergy consumption, capital stock
and real income in G-7 countries. A unidire ctional relationship running from energy
consumption to real income was observed, u sing Granger causality tests based on the Toda
and Yamamoto (1995) procedure. Chontanawat et al. (2008) tested for Granger causality
between energy and GDP using a data set of 30 O ECD and 78 non-OECD countries. For Italy,
they showed evidence of causality from energy to GDP. Zachariadis (2007) applied bivariate
energy use –economic growth causality tests for G-7 co untries. Bidirectional Granger causality
emerges for most sectors. Lee (2006) explored the causality relationship between energy
consumption and GDP in G-11 countries using t he Toda and Yamamoto procedure. The results
indicate that unidirectional causality running fr om GDP to energy consumption exists in Italy.
Soytas and Sari (2006) analyzed the relationship between energy consumption and income in
G-7 countries. For Italy, they found that Gr anger causality seems to run both ways. Soytas and
Sari (2003) investigated the time-series properties of energy consumption and GDP in 10
emerging markets and G-7 countries. Granger causality running from GDP to energy
consumption was discovered for Italy.
3. Methodology and data
The ARDL bounds testing approach of cointegration is developed by Pesaran and Shin
(1999) and Pesaran et al. (2001) . This approach has several advantages over the
traditional cointegration approaches of Engle and Granger (1987) ,Johansen (1988) andFinancial
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Johansen and Juselius (1990) . This takes care of small sample properties and
simultaneity biasness in relationship among variables. The main constraint in theapplication of the conventional cointegration techniques is that they require all thevariables included in the model to be non-stationary at levels but should be integratedof the same order. The present ARDL approach to cointegration method surmounts this
problem, as it is applicable irrespective of o rder of integration of regressors, whether
I(0) or I(1) or mixture of both. Apart from that, the ARDL model also has advantages in
selecting suf ficient numbers of lags to capture the data generating process in a general-
to-speci fic modeling framework. These meritorious features justify the use of the ARDL
model to obtain robust estimates.
Following the empirical literature ( Farhani and Ozturk, 2015 ), the standard log-linear
functional speci fication of a long-run relationship among energy consumption, economic
growth, financial development and oil prices in Italy may be expressed as:
EC
t¼b0țb1EGtțb2FDtțb3OPtț«t (1)
Basically, the ARDL bounds testing approach of coi ntegration involves two steps for estimating
a long-run relationship. The first step is to investigate the existence of a long-run relationship
among all variables in the e quation. The ARDL model for equation (1) may follow as:
DECt5a0țXp
e51a1eDECt/C0ețXq
f50a
2fDRGDP t/C0fțXr
g50a
3gDFDt/C0g
țXs
h50a4hDOPt/C0hțd1ECt/C01țd2RGDP t/C01țd3FDt/C01țd4OPt/C01ț«1t
(2)
where «1tandDare the white noise term and the first difference operator, respectively. The
bounds testing procedure is based on the joint F-statistics or Wald statistics that tested
the null of no cointegration, H0:dr=0, against the alternative of H1:dr=0,r=1 ,2 , …,4 .I f
the calculated F-statistics lies above the upper level of the band, the null is rejected,
indicating cointegration. If the calculated F-statistics is below the upper critical value, we
cannot reject the null hypothesis of no cointegration. Finally, if it lies between the bounds, aconclusive inference cannot be made without knowing the order of integration of theunderlying regressors. The next step is to test for stability of the long-run coef ficients as
well as the dynamics of the short-run ones following Pesaran (1997) , performing the general
error-correction representation of the selected ARDL model of equation (2) .
In this study, two causality tests are considered. First, the Granger non-causality test is
carried out following the Toda and Yamamoto (1995) long-run causality test. Furthermore, a
“standard ”Granger causality analysis has been developed. A time series X
tis said to
Granger-cause another time series Ytif the prediction error of current ydeclines by using
past values of Xin addition to past values of Y(Granger, 1988 ).
In our analysis, the log transformations of the variables have been derived. The empirical
analysis uses the time-series data of the fossil fuel energy consumption (per cent of total,EC), domestic credit to private sector (per cent of GDP, FD), real per capita GDP (2011 US
dollars per capita, RGDP ) and oil price (dollars per barrel, OP) for Italy in the period 1960-
2014. The data are obtained from the World Development Indicator (WDI)[ 1]. The choice of
the starting period was constrained by domestic credit to private sector data availability.Figure 1 shows the dynamic of our series. In the right-side panel, the first-differences series
are graphed.IJESM
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A visual inspection of the series in logarithmic form shows that there is an upward trend for
real aggregate income and oil prices.
Some descriptive statistics are summarized in Table II as a preliminary analysis. The
mean value of our four variables is positive. Moreover, financial development has positive
value of skewness, indicating that the distribution is skewed to the right ( Table I ).
The energy consumption is statistically positively correlated with real GDP ( r= 0.42),
and negatively with financial development ( r=/C00.61), while the oil prices variable is
statistically negatively correlated with real income ( r=/C00.66). In addition, these results are
broadly con firmed by cross correlations analysis.
4. Empirical results
As can be grasped by the panel in the left-hand side of Figure 1 above, the four analyzed
series do not seem to have stationary properties in the levels, contrarily to the relative firstFigure 1.
Energy consumption,
real per capita GDP,
financial
development and oil
prices for Italy
(1960-2014, log-scale)0 2 4 6 8 10
1960 1980 2000 2020
Year
lec lrgdp
lfd lop
Data from WDI database.
–0.5 0 0.5 1
1960 1980 2000 2020
Year
D.lec D.lrgdp
D.lfd D.lop
Data from WDI database.
Table I.
Exploratory data
analysisVariable Mean Median SD Skewness Kurtosis Range IQR 10-Trim
EC 4.5140 4.5237 0.0309 /C01.8793 6.5356 0.1531 0.0288 4.520
RGDP 10.0188 10.1003 0.3701 /C00.6723 2.2310 1.2475 0.5951 10.060
FD 4.1332 4.0801 0.1992 0.6364 2.4412 0.6983 0.2585 4.114OP 2.7479 2.9576 1.1033 /C00.2802 2.0159 3.5293 2.0602 2.788
Note: IQR: Inter-quartile range
Source: Author ’s calculations on WDI dataFinancial
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differences. Table II contains the results of common unit root and stationarity tests, to
determine the order of integration of our variables. Here, we applied four different tests: ingeneral, they indicate that energy consumption, real GDP, financial development and oil
prices are all non-stationary at levels, but stationary at first differences.
In fact, for all series, we reject the hypothesis of non-stationarity at the 5 per cent level of
significance, both with constant and constant plus trend deterministic speci fication. We
therefore can conclude that all our series are integrated of order one, or I(1). The lag-order
selection has been chosen according to the Akaike ’s information criterion (AIC), the
Schwarz ’s Bayesian information criterion (SBIC) and the Hannan –Quinn information
criterion (HQIC).
The bounds F-test for cointegration yields evidence of a long-run relationship among
energy consumption, real income, financial development and oil prices at 1 per cent
significance level ( Table III ).
Table II.
Results for unit rootsand stationarity testsVariableUnit root and stationarity tests
Deterministic component ADF ERS PP KPSS
EC Constant 0.2641 /C00.3642 /C00.1378 0.3477*
RGDP Constant /C05.7760*** /C01.1587 /C06.1585*** 0.9246***
FD Constant /C01.1517 /C01.0798 /C00.7128 0.3472*
OP Constant /C00.6667 0.3815 /C00.6606 0.8163***
EC Constant, trend 1.2620 0.3399 /C00.0891 0.2171***
RGDP Constant, trend /C00.4072 /C00.1110 0.0730 0.2473***
FD Constant, trend /C01.4320 /C01.5342 /C01.0398 0.2006**
OP Constant, trend /C01.7670 /C01.8064 /C01.8837 0.1199*
DEC Constant /C02.4261 0.4450 /C04.4619*** 0.7296**
DRGDP Constant /C04.7346*** 1.0069 /C04.7167*** 0.9610***
DFD Constant /C02.8399* /C02.7733*** /C03.4928** 0.1880
DOP Constant /C07.0112*** /C06.9330*** /C07.0111*** 0.0816
DEC Constant, trend /C03.6601** /C02.9090* /C06.2580*** 0.1309*
DRGDP Constant, trend /C06.0832*** /C06.4744*** /C07.9507*** 0.1063
DFD Constant, trend /C03.5391** /C02.9500* /C03.4808* 0.1047
DOP Constant, trend /C06.9387 /C07.0560*** /C06.9385 0.0832
Notes: The tests are performed on the log-levels of the variables. ADF; ERS; PP; and KPSS refer,
respectively, to the Augmented Dickey –Fuller test; the Elliot, Rothenberg and Stock point optimal test; the
Phillips –Perron test; and the Kwiatkowski, Phillips, Schmidt and Shin test. When it is required, the lag
length is chosen according to the HQIC; ***p<0.01; **p<0.05; *p<0.10
Table III.
ARDL bound test
estimation resultsModel for estimation Lag length F-statistics Significance levelCritical bound
F-statistics
I(0) I(1)
FECRGDP,FD,OP2 8.1949*** 1 3.65 4.66
5 2.79 3.67
10 2.37 3.20
Notes: Asymptotic critical value bounds are obtained from table F-statistic in Pesaran et al. (2001) ;***p<
0.01; ** p<0.05; * p<0.10IJESM
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The long-run elasticity estimate of ene rgy consumption with respect to economic
growth is b1>0, at 1 per cent signi ficance level, in line with previous findings in
Shahbaz et al. (2013b) ,b u ti nc o n t r a s tt o Mahalik and Mallick (2014) . This means that as
per capita real income increases, per capita energy consumption increases as well.Moreover, it implies that energy demand plays a relevant role to enhance economic
growth in Italy. Financial development variable has no signi ficant effect on energy
consumption in the long run. This result is similar to Aliet al. (2015) . Oil prices are
negative and signi ficant at 1 per cent, which means that a 1 per cent increase in oil
prices could trigger consumption of energy to decrease by 0.32 per cent; therefore, an
increase in oil prices leads to an increase in the cost of energy as well as a reduction in
energy consumption in Italy in the long run ( Table IV ).
The coef ficients of the estimated error correction term (ECT) are also negative and
statistically signi ficant at 1 per cent con fidence level. These values indicate that any
deviation from the long-run equilibrium between variables is corrected for each period to
return to the long-run equilibrium level. In the long run, advancement of the financial sector
has an insigni ficant adverse effect on energy consumption, while economic growth has a
significant in fluence on energy consumption, and oil prices affected energy consumption in
Italy ( Table V ).
The short-run dynamics show that financial development and oil prices do not have
significant effects on energy consumptions, whereas economic activity has a signi ficant
positive effect on energy consumptions in Italy. This implies that an increase in the energyconsumption should not lead to a decline in the financial development and oil prices in the
short run.
The ECT is less than one, negative and signi ficant as expected. Banerjee et al. (1998)
reported that the ECM value con firms the integrity of a long-run relationship. This rati fies
the above long-run nexus among the variables, which implies that energy consumption iscorrected from the short-run toward reaching long-run equilibrium at 1.66 per cent everyyear.
Table IV.
Long-run coef ficients
estimationRegressors Coefficient Standard error
RGDP 0.5074 0.1776***
FD /C00.6340 1.1532
OP /C00.3183 0.1123***
Constant 2.6088 0.7637***
Notes: ***p<0.01; **p<0.05; *p<0.10
Table V.
Estimated short-run
coefficients from
ECMRegressors Coefficient Standard error
DECt-1 0.2530 0.1220**
DRGDP 0.1638 0.0292***
DFD /C00.0108 0.0193
DOP /C00.0284 0.0337
ECM t-1 /C00.0166 0.0026***
Notes: ***p<0.01; **p<0.05; *p<0.10Financial
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Table VI presents the estimated ARDL model that has passed several diagnostic tests,
which indicate no evidence of non-normality, serial correlation, heteroskedasticity and
autoregressive conditional heteroskedasticity.
In addition, owing to the structural changes in the Italian economy –especially during
the eighties ( Magazzino ,2015, 2014b )–it is likely that macroeconomic series may be subject
to one or multiple structural breaks. For this purpose, the stability of the short-run and long-
run coef ficients is checked through the cumulative sum (CUSUM) and cumulative sum of
squares (CUSUMSQ) tests proposed by Brown et al. (1975) . Unlike the Chow test, which
requires break point(s) to be speci fied, the CUSUM and CUSUMSQ tests are quite general
tests for structural change in that they do not require a prior determination of where thestructural break takes place. Figures A1 andA2in the Appendix present the plot of CUSUM
and CUSUMSQ tests statistics that fall inside the critical bounds of 5 per cent signi ficance.
This implies that the estimated parameters are stable over the periods.
Granger causality test following the Toda and Yamamoto approach requires the estimation
of an augmented VAR( kțd)m o d e l ,w h e r e kis the optimal lag length and dis the order of
integration of the series. For the multivariate speci fication, all tests suggest the inclusion of one
l a gi naV A Rm o d e la n dt h u s k=1 ;h e n c e ,t h e final model to be estimated is VAR(2). To ensure
that the VAR model is well speci fied and does not suffer from any normality or serial
correlation problems, additional tests are carri ed out. Although the results are not reported to
save space, diagnostic tests suggest the gener al absence of problems in the estimated VAR(2)
model, with regard to normality and autocorrela tion in the residuals, stability condition and
lag-exclusion.
The results of Toda and Yamamoto Grange r non-causality tests are presented in Table VII .
If we want to reject the null hypothesis of “Xdoes not Granger cause Y”at a 5 per cent level of
significance, then the P-value should be less than 0.05. The left column in the table represents
the dependent variable, while variables liste d in the row are the independent variables (source
of causation). To provide robust conclusions , both multivariate and bivariate tests are
considered. For the multivariate model, empirical findings show that energy consumption is
driven by financial development and oil prices. In addition, financial development is caused by
energy consumption, too. Thus, energy consump tion helped Italy to achieve high economic and
financial development. However, countries with similar structure regarding issues of energy
can achieve economic and
financial development when they present high levels of energy
consumption. The bivariate system exhibits a unidirectional causality flow running from real
income to energy. This is in line with the findings of Lee (2006) andSoytas and Sari (2003) .
However, it is interesting to note that the bivariate results roughly con firm the previous of the
multivariate system. In fact, again, energy consumption is caused by financial development
and oil prices, while oil prices are sensible to financial development. In addition, energy
Table VI.
ARDL Diagnostic
tests resultsTest statistics LMversion Fversion
1: Serial correlation x2= 0.6438 (0.7248) 0.2663 (0.7676)
2: Functional form 11.1963 (0.0018)***
3: Normality 3.5866 (0.1664)4: Heteroskedasticity 0.9105 (0.4968) 5.6398 (0.4647)5: ARCH 0.0785 (0.7806) 0.0818 (0.7749)
Notes: 1 = Lagrange-Multiplier test of residual serial correlation; 2 = Ramsey ’s RESET test using squared
of the fitted values; 3 = Jarque –Bera test for normality; 4 = Breusch –Pagan –Godfrey heteroskedasticity
test; 5 = AutoRegressive Conditional Heteroskedasticity test;
***p<0.01; ** p<0.05; * p<0.10IJESM
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Multivariate Bivariate
Independent variables Independent variables
Dep. var. EC RGDP FD OP Dep. var. EC RGDP FD OP
Granger tests
EC – 7.402*** (0.007) 0.058 (0.810) 10.302*** (0.001) EC – 4.649** (0.031) 7.548** (0.023) 7.581*** (0.006)
RGDP 2.838* (0.092) – 0.091 (0.763) 8.631*** (0.003) RGDP 4.035** (0.045) – 11.508*** (0.003) 9.975*** (0.002)
FD 0.046 (0.830) 0.895 (0.344) – 1.478 (0.224) FD 1.379 (0.502) 1.561 (0.458) – 0.945 (0.331)
OP 0.246 (0.620) 1.498 (0.221) 0.107 (0.743) – OP 0.040 (0.842) 2.242 (0.326) 0.006 (0.940) –
Toda and Yamamoto tests
EC – 1.399 (0.497) 6.363** (0.042) 8.862** (0.012) EC – 12.312*** (0.002) 10.767** (0.013) 28.467*** (0.000)
RGDP 1.007 (0.604) – 2.187 (0.335) 4.095 (0.129) RGDP 3.500 (0.174) – 14.892*** (0.002) 10.457*** (0.005)
FD 9.535*** (0.009) 3.362 (0.186) – 8.167** (0.017) FD 3.333 (0.343) 2.257 (0.521) – 2.077 (0.354)
OP 0.449 (0.799) 4.926* (0.085) 10.735*** (0.005) – OP 0.706 (0.703) 3.375 (0.185) 5.290* (0.071) –
Notes: Wald tests ( p-values in parentheses); ***p<0.01; **p<0.05; *p<0.10
Table VII.
Results of causality
testsFinancial
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consumption increased the financial development and GDP growth in Italy, with a high
pollution consequence. Of course, apart from positive impacts, there are also negative
externalities like environmental pollution, whi ch could be the subject for a further research.
Furthermore, the results of the standard Granger causality tests are similar to those
obtained via the Toda and Yamamoto approach. They indicate that energy consumption isaffected by real GDP, both in the multivariate tests and in the bivariate ones. Again, in thebivariate system, real income is driven by financial development and oil prices.
In addition to the relationship between income and energy consumption, however, it is
important for policymakers to take financial development into consideration when
formulating energy policy. Financial development is particularly important to businessinvestment because it allows businesses access to additional sources of funding and equityfinancing. The results from causality tests show that an increase in domestic credit to
private sector affects energy consumption. Financial development can lower energyconsumption by achieving ef ficiency in its use. In fact, financial development can provide
efficientfinancial service in foreign banking markets, and improve the access of both foreign
and domestic firms to financial goods and services. Developed financial market enhances
participation by consumer and business, promotes economic activity and boosts energy use.Moreover, financial development enhances domestic production through investment
activities and boost economic growth.
5. Concluding remarks and policy implications
This study has extended the research on the causal relationship among energy
consumption, real income and financial development using annual data for Italy in the years
1960-2014. The results of unit root tests reveal that all variables are integrated of order one,I(1), as each of them is non-stationary in its level form, and stationary in first differences.
The ARDL bounds F-test evidences the existence of a long-run relationship among energy
consumption, real income, financial development and oil prices at 1 per cent signi ficance
level. The long-run coef ficients estimation results show that an increase in real GDP and oil
prices has a signi ficant effect on energy consumption, although with an opposite sign.
Moreover, the coef ficients of estimated ECT are also negative and statistically signi ficant.
The results of the analysis reveal a negative sign for the coef ficient of financial development,
suggesting that financial development in Italy has not taken place at the expense of energy
consumption. Finally, causality analyses in general reveal that energy consumption isdriven by real income, financial development and oil prices.
Causality analyses indicate that a unidirectional causality running from real income to
energy exists, in line with “conservation hypothesis ”. This means that continuous economic
growth simultaneously generates a continuous rise in energy consumption, and the policy ofconserving energy consumption may be implemented with little or no adverse effect oneconomic growth, such as in a less energy-dependent economy. In this case, energyconsumption is fundamentally driven by income, and as such, taking measures to conserveenergy may be feasible without compromising economic growth. Beyond this, it is impliedthat a strategy for sustainable development with a lower level of CO
2emissions may, indeed,
be appropriate in Italy ( Magazzino, 2014c ). Financial development Granger causes energy
consumption, which reveals that adoption of energy conservation policy would notadversely affect economic growth. Again, the financial sector must fix its focus on the
allocation of funds to those firms which adopt environment-friendly technologies and
encourage the firms to use more energy-ef ficient technology for production purpose and
hence to save environment from degradation.IJESM
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The main recommendation suggested by this study, for Italy, is to increase energy
productivity by increasing energy ef ficiency, implementation of energy-saving projects,
energy conservation and energy infrastructure outsourcing to achieve its financial
development and GDP growth. Therefore, it is important that the country increase its lowinvestments on energy projects to achieve the full energy potential. This leads to reductionin emissions and improvement of the financial development factor. Moreover, as causality
analysis pointed out, an energy policy that is focused solely on the relationship betweenenergy demand and income would provide an inaccurate estimate of energy demandbecause it fails to consider the development of the stock market. Emerging market anddeveloping countries whose stock markets continue to develop should thus anticipategrowth in energy demand above and beyond that caused by increasing income alone.
6. Suggestions for future research
Further analysis might be conducted to analyze the effect of energy consumption, real
income and financial development on carbon emissions in Italy, with the ARDL bounds test
approach.
Note
1. See, for more details: http://data.worldbank.org/data-catalog/world-development-indicators
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Appendix
Figure A1.
Plot of cumulative
sum (CUSUM) ofrecursive residuals–20–15–10–505101520
1970 1975 1980 1985 1990 1995 2000 2005 2010
CUSUM 5% Significance
Figure A2.
Plot of cumulative
sum (CUSUM) of
squares of recursive
residuals–0.4–0.20.00.20.40.60.81.01.21.4
1970 1975 1980 1985 1990 1995 2000 2005 2010
CUSUM of Squares 5% SignificanceIJESM
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Figure A3.
Model selection
criteria–7.26–7.24–7.22–7.20–7.18–7.16–7.14
ARDL(2, 1, 0, 0)
A R D L ( 1 ,1 ,0 ,0 )
ARDL(2, 1, 0, 1)
ARDL( 2, 2, 0, 0)
ARDL (1, 1, 0, 1)
ARDL(2, 1, 1, 0)
ARDL(2, 1, 1, 1)
AR D L ( 1 ,1 ,1 ,0 )
ARDL(2, 2, 1, 0)
ARDL(2, 2, 0, 1)
ARDL(1,2,0, 0)
ARDL(1,1,1,1)
ARDL(2, 2, 1, 1)
ARDL(2, 1, 0 ,2)
ARDL(1, 1, 0, 2)
ARDL(1,2,0,1)
ARDL(2,1,2 ,0)
ARDL(2,1, 2, 1)
ARDL(2, 1, 1 ,2)
ARDL(1, 2, 1, 0)Hannan-Quinn Criteria (top 20 models)
Table AI.
Correlation matrixEC RGDP FD OP
EC 1.000 0.4198** /C00.6135*** /C00.1882
RGDP 0.4198** 1.0000 /C00.3908** /C00.6601***
FD /C00.6135*** /C00.3908** 1.0000 0.2522
OP /C00.1882 /C00.6601*** 0.2522 1.0000
Notes: Sidak ’s correction applied; ***p<0.01; **p<0.05; *p<0.10Financial
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