“economic growth and energy consumption in g7 countries: ms-var and ms-granger causality...

8/13/2019 Economic Growth and Energy Consumption in G7 Countries: MS-VAR and MS-Granger Causality Analysis, by Melike Bildirici

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THE JOURNAL OF ENERGY

AND DEVELOPMENT

Melike Bildirici,

Economic Growth and Energy

Consumption in G7 Countries:

MS-VAR and MS-Granger Causality Analysis,

Volume 38, Number 1

Copyright 2013


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ECONOMIC GROWTH AND ENERGY

CONSUMPTION IN G7 COUNTRIES: MS-VAR

AND MS-GRANGER CAUSALITY ANALYSIS

Melike Bildirici*

Introduction

Standard macroeconomics textbooks consider capital and labor as inputs intheir production functions, but not energy.1 However, in the aftermath of thefirst energy crisis of the 1970s, economists began to pay greater attention to en-

ergy. Energy does not appear explicitly on the payments side of national accounts.

The payments to energy is equated to revenues of certain industries, such as

coal mining, petroleum and gas drilling (and value added in refining), and elec-

tricity generation and distribution. Using this approximation, it turns out that

energys share of payments in industrial countries is negligibleaccounting, in

most cases, to not more than a few percentage points of a nations gross domestic

product (GDP). Most economists in the past have assumed that energy could not

be very productive relative to the traditional variables of capital or labor,

depending on the income allocation theorem. To address this dilemma, it was

assumed that energy is an intermediate product of the economy. Thus, capital and

labor produces the output and energy serves an intermediate role as it is converted

*Melike Bildirici, Professor at the Yildiz Technical University in Istanbul, Turkey, holds a B.S.

from Marmara University (Istanbul) and earned both his M.A. and Ph.D. degrees in economics from

that institution. Dr. Bildiricis studies have appeared in such publications as The Journal of Energy

and Development, Expert Systems with Applications, Energy, Family History, Energy Economics,

JRSE,AEID,Economic Research,Emerging Markets Finance and Trade,The International Journal

of Applied Econometrics and Quantitative Studies, JESR, METU Studies in Development, YKER,

andIIF.

The author wishes to thank Helen El Mallakh for her assistance and contributions.

The Journal of Energy and Development, Vol. 38, Nos. 1 and 2

Copyright 2013 by the International Research Center for Energy and Economic Development(ICEED). All rights reserved.

1


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provided a basis for the outgrowth of causality researchboth in its expansion and

diversificationinto other fields of causality research. The main findings of these

studies are determined with causality methodology. The results obtained are dif-

ferentiated based upon the direction of causality, which can be evaluated under

four hypotheses. First is the neutrality hypothesis, which assumes that no

causality exists between GDP and energy (electricity) consumption; therefore,

the energy (electricity) consumption is not correlated with GDP. Second is the

conservation hypothesis, which assumes that a uni-directional causality exists

that runs from GDP to energy (electricity) consumption. Third is the growth

hypothesis, which assumes that there is a uni-directional causality that runs from

energy (electricity) consumption to GDP. Last, we have the feedback hypothe-

sis, which assumes the existence of a bi-directional causality flow between GDP

and energy (electricity) consumption.

Thus, the energy causality literature is prolific (table 1)the relationship being

investigated by different studies, for various countries, over numerous time

frames. Despite the use of the very same variables, the end results offered different

coefficients and causality relationshipseven in the studies for the same coun-

tries. Econometric techniques, the structure of time series, and the business cycle

of the case countries studied may be the cause of obtaining different results for the

same nations. The most important point is the nonlinear structure of the economic

time series particularly as GDP, which has been used as the measure of economic

performance, fluctuates with the business cycles. With respect to energy eco-nomics, these models assume parameters to be constant over the sample period,

which means the relationship between GDP and energy consumption is stable. Of

course, this assumption of stability is not very reflective of the real world eco-

nomic situation during the past decades as can be witnessed by a significant

number of economic crises and meltdowns: the energy crises (1974, 1979), the

Exchange Rate Mechanism (ERM) crisis, the southeast Asia crisis of the 1990s,

the Great Recession of 2008, along with national-level crises. Clearly, the business

cycle affects the relationship between GDP and energy or electricity consumption.

In time-series analysis, the phase of the business cycle must be taken into account;otherwise, the estimated parameters would be incorrect and misleading.

One way to overcome these problems is to divide the sample into sub-samples,

based on the structural breaks; however, in most cases the exact dates of these

changes are not known and the researcher must determine them endogenously

based upon the data. But there is no guarantee that the relationship between GDP

and energy consumption changes at the same time as the break dates of the var-

iables themselves.10

J. D. Hamilton proposed a simple nonlinear framework for modeling economic

time series with a permanent component and a cyclical component as an alter-native to a stationary linear autoregressive model.11 M. Clements and H. Krolzig,

M. Holmes and P. Wang, A. Cologni and M. Manera, and M. Bildirici, E. Alp, and

G7 COUNTRIES: GROWTH & ENERGY CONSUMPTION 3


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Table1

CAUSALITYLITERATURE

(Y=GNP,ENR=energyconsumption,andEC=electricityconsump

tion/supply)

Author(s)

C

ountry

Period

Methodology

MainVariables

Causality

Conserva

tionhypothesis

J.KraftandA.Kraft

UnitedSta

tes

19471974

Simscausalityanalysis

grossnationalproduct

(GNP),energy

consumption

Y

/

ENR

U.ErolandE.Yu

Germany,

Italy

19521982

Simscausalityanalysis,

Grangercausality

grossdomesticproduct

(GDP),energy

consumption

Y

/

ENR

C.Maga

zzino

Italy

19702009

Vectorautoregressive

(VAR),errorcorrection

model

GDP,energyconsumptionY

/

ENR

E.Yuan

dB.Hwang

UnitedSta

tes

19471979

Simscausalityanalysis

GNP,electricity

consumption

Y

/

EC

B.Cheng

Japan

19521995

HsiaosGrangercausality

GDP,electricity

consumption

Y

/

EC

T.Zacha

riadis

Canada,U

nitedKingdom

19602004

Grangercausality,VAR,

errorcorrection,

autoregressivedistributed

(ARDL)

GDP,electricitysupply

Y

/

EC

(continued)

THE JOURNAL OF ENERGY AND DEVELOPMENT4


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Table1(

continued)

CAUSALITYLITERATURE

(Y=GNP

,ENR=energyconsumption,andEC=electricityconsumption/supply)

Author(

s)

C

ountry

Period

Methodology

MainVariables

Causality

C.Lee

France,Ita

ly,Japan

19602001

VAR,Todaand

Yamamoto


Y

/

EC

S.Abose

draand

H.Baghestani

UnitedSta

tes

19471987

Cointegrationand

Grangercausality


Y

/

EC

GrowthH

ypothesis

J.KraftandA.Kraft

UnitedSta

tes

19471974

Simscausalitytest

GNP,energy

Consumption

E

NR/

Y

U.ErolandE.Yu

Canada

19521983

Simscausalityanalysis,

Grangercausality

GDP,energy

consumption

E

NR/

Y

D.Stern

UnitedSta

tes

19471990

MultivariateVARmodel

GNP,electricity

consumption

E

C/

Y

D.Stern

UnitedSta

tes

19481994

Cointegration,Granger

causality

GNP,electricity

consumption

E

C/

Y

N.Bowd

enandJ.Payne

UnitedSta

tes

19492006

TodaandYamamoto

long-runcausalitytests,

Grangercausality

GNP,electricity

consumption

E

C/

Y

(continued)



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Table1(

continued)

CAUSALITYLITERATURE

(Y=GNP


Author(s)

C

ountry

Period

Methodology

MainVariables

Causality

A.CiarretaandA.Zarraga

12Europe

anUnion

countries

19702007

Panelcointegrationand

panelsystemgeneralized

methodofmoments

(GMM)

GDP,electricity

consumption

E

C/

Y

K.Ghali

andM.El-Sakka

Canada

19611997

Cointegration,VEC,

Grangercausality

GDP,electricity

consumption

E

C/

Y

J.Ang

France

19602000

Multivariatecausality

GDP,electricity

consumption

E

C/

Y

P.Naray

an,R.Smyth,and

A.Prasad

G7countries

19722002

Panelcointegration

GDP,electricity

consumption

E

C/

Y

C.Leea

ndC.Chang

22developed

19652002

PanelVARs

GDP,electricity

consumption

E

C/

Y

M.Thom

a

UnitedSta

tes

19732000

Causality

GDP,electricity

consumption

E

C/

Y

N.Bowd

enandJ.Payne

UnitedSta

tes

19492006

TodaandYamamoto

causalitytest

GDP,electricity

consumption

E

C/

Y

(continued)



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Table1(

continued)

CAUSALITYLITERATURE

(Y=GNP


Author(s)

C

ountry

Period

Methodology

MainVariables

Causality

B.Cheng

UnitedSta

tes

19471990

Cointegrationand

Grangercausality

GNP,electricity

consumption

n

one

T.Zacha

riadis

UnitedSta

tes

19602004

Grangercausality,VAR,

errorcorrection,ARDL

GDP,electricity

consumption

n

one

C.Lee

Germany,

United

Kingdom

19602001

VAR,Todaand

Yamamoto

GDP,electricity

consumption

n

one

J.Payne

UnitedSta

tes

19492006

TodaandYamamoto

causalitytest

GDP,electricity

consumption

n

one

Sources:J.KraftandA.Kraft,Onthe

RelationshipbetweenEnergyandGNP,TheJournalofEnergyandDevelopment,vol.3,no.2(spring1978),pp.

40103;

U.ErolandE.S.H.Yu,OntheCausalRelationshipbetween

EnergyandIncomeforIndustri

alizedCountries,TheJournalo

fEnergyand

Developm

ent,vol.13,no.1(autumn1987

),pp.11322;C.Magazzino,E

nergyConsumptionandAggrega

teIncomeinItaly:Cointegration

andCausality

Analysis,MPRAPaperno.28494,UniversityLibraryofMunich,Munich,Germany,2011;E.S.H.YuandB

.K.Hwang,TheRelationshipbetweenEnergy

andGNP

:FurtherResults,EnergyEconomics,vol.6,no.3(1984),pp.186

90;B.S.Cheng,EnergyConsu

mption,EmploymentandCausalityinJapan:A

MultivariateApproach,IndianEconomic

Review,vol.33,no.1(1998),p

p.1929;T.Zachariadis,Explo

ringtheRelationshipbetweenEnergyUseand

Economi

cGrowthwithBivariateModels:NewEvidencefromG-7Countries,EnergyEconomics,vol.29

,no.6(2007),pp.1233253;C.C.Lee,The

Causality

RelationshipbetweenEnergyCo

nsumptionandGDPinG-11CountriesRevisited,EnergyPolicy,vol.34,no.9(2006),pp.108693;S.Abosedra

andH.B

aghestani,NewEvidenceontheCausalRelationshipbetweenU

nitedStatesEnergyConsumptionandGrossNationalProduct,T

heJournalof

EnergyandDevelopment,vol.14,no.2(sp

ring1989),pp.28592;J.KraftandA.Kraft,op.cit.;U.ErolandE

.S.H.Yu,op.cit.;D.I.Stern,E

nergyUseand

Economi

cGrowthintheUSA:A

MultivariateApproach,EnergyEconomics,vol.15,no.2(1993),

pp.13750;David.I.Stern,A

Multivariate

CointegrationAnalysisoftheRoleofEnergyintheUSMacroeconomy,EnergyEconomics,vol.22,no.2(20

00),pp.26783;N.BowdenandJ.Payne,The

CausalR

elationshipbetweenU.S.Energy

ConsumptionandRealOutput:

ADisaggregatedAnalysis,JournalofPolicyModeling,vol.31,no.2(2009),

pp.1808;A.CiarretaandA.Zarraga,EconomicGrowthandElectricityC

onsumptionin12EuropeanCountries:ACausalityAnalysisUsingPanelData,

BILTOK

I,no.2008-4,UniversidaddelPasVasco,DepartamentodeEconomaAplicadaIII(EconometrayEstadstica),Gipuzkoa,Spain,200

8;K.H.Ghali

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T. Bakrtasx used Markov-Switching Autoregression (MS-AR) and Markov-

Switching Vector Autoregression (MS-VAR) models to test the impact of oil

shocks on GDP.12 F. Falahi and M. Bildirici used the MS-VAR model to assess the

relationship between energy consumption and economic growth.13

In this paper, the MS-VAR model is selected to analyze the relationship be-

tween energy consumption and economic growth. Although this study can be

defined as complementary to the previous empirical papers, it differs from the

existing literature in some respects. First, it is distinguished from the previous

works as it employs the Markov-Switching VAR method. Second, it uses the

Markov-Switching Granger (MS-Granger) causality analysis. The MS-Granger

causality approach allows for the analysis of Granger causality under different

regimes of the business cycle.

This article is laid out as follows: the econometric theory and methodology are

identified in the second section. The third section consists of the empirical results

while the last section includes conclusions and policy implications.

Data and Methodology

Data: In this study, the relationship between energy consumption (EC), which is

taken asLEC= log(ECt/ECt-1), and economic growth (Y), which is represented as

LY= log(Yt/Yt-1), is investigated by the MS-VAR method. This study examines all

G7 countries, which represent seven of the eight wealthiest nations on earth (China

excluded), not by GDP but by global net wealth. The G7 countriesand our case

studiesare Canada, France, Germany, Italy, Japan, the United States, and the

United Kingdom, which we evaluate for the period from 1961 to 2011 (1970 to

2011 for Germany). The data are taken from GAPMINDER, the International

Energy Agency (IEA), the Organization for Economic Cooperation and De-

velopment (OECD), and the U.S. Energy Information Administration (EIA).

MethodologyMS-VAR Analysis: The MSI(.)-VAR(.) model is given as:

yt= m st Xq

k= 0Ai st yt1+ ut;

1

whereut/st;NID(0, S(st)) andAi(.) show the coefficients of the lagged values of

the variable in different regimes, andS shows the variance of the residuals in each

regime. m(st) defines the dependence of the mean m of the K-dimensional time

series vector on the regime variablest.

In an MS-VAR model, stis governed by a Markov chain and

Pr stj st1f g

i = 1; yt1f g

i = 1 =Pr stjst1; rf g; 2

where P includes the probability parameters. That is, the state in periodtwoulddepend only on the state in periodt1. On the other hand, the conditional prob-

ability distribution ofytis independent ofst-1, that is, P(ytjYt-1, St-1) = Pr(ytjYt-1).



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It is assumed thatsfollows an irreducible ergodicMstate Markov process with

the transition matrix defined as,

P =

p11 p12 . . . p1M

p21 p22 . . . p2M

.

.

...

.

. . . .

.

.

PM1 pM2 . . . pMM

2666437775: 3

The Markov chain is ergodic and irreducible; a two-state Markov chain with

transition probabilitiespijhas an unconditional distribution given by

Pr st= 1 = 1p22

2p11p22;Pr st= 2 =

1p11

2p11p22:

There are different ways to estimate the MS models, such as the maximum

likelihood estimate (MLE) and the expectation maximization (EM) suggested by

J. D. Hamilton. The EM algorithm has been designed to estimate the parameters of

a model where the observed time series depends on an unobserved or a hidden

stochastic variable. The iterative method was utilized with t = 1, 2,., T, while

taking the previous value of this probability jit1= Pr[st-1= ijWt-1;u] as an input.

MethodologyThe MS-Granger Causality Analysis: A. Warne and Z. Psaradakis

et al. proposed a different approach to causality based on Granger causality.14

F. Falahi utilized short-run or weak Granger causalities for MSIA(.)-VAR(.) models

by following the Granger causality in the context of Markov switching.15

Based on the coefficients of the lagged values ofLYtandLECtin the equations,

we could determine the existence of causalities between these two variables. In the

equation vector where the dependent variable is LECt, if any of the coefficients of

lagged variables of LYt are statistically different from zero, the obtained test

process will result in the acceptance of the causality. In any of the regimes, basedon the coefficients of the lagged values ofLYtandLECtin the equation forLECtandLYt, we could determine the existence of nonlinear causality between these

two variables. In the equation forLECt, if any of the coefficients ofLYtare sig-

nificantly different from zero in any of the regimes, then:

LYtLECt

= m1;stm2;st

+Xq

k= 1

f k 11;st f

k 12;st

f k 21;st f

k 22;st

" # LYtkLECtk

+

etet

: 4

It is concluded thatLYt(LECt) is a Granger cause ofLECt(LYt) in that regime.

Granger causalities are detected by testing H0:f12(k)= 0 andH0:f21

(k)= 0.16



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Table 2UNIT ROOT TEST RESULTS FOR THE ANALYZED COUNTRIES

MZa MZt MSB MPT

Elliott-Rothenberg-

Stock Test Statistic

Canada

Yt 0.3015 0.20579 0.68247 27.7535 8.81800

DLYt 22.2136 3.32903 0.14986 4.12441 0.27882

LECt 1.1997 0.21456 0.91152 1.11979 13.13388

DLECt 41.9643 4.50664 0.10739 2.55705 0.72314

France

Yt 0.1020 0.11866 1.16321 75.0503 15.32227

DLYt 48.8874 4.88210 0.09986 0.65938 0.92796

LECt 1.0337 0.69755 0.47943 7.79672 14.02522DLECt 23.6136 3.31302 0.14030 1.44698 0.91139

Germany

Yt 5.9115 16.09150 2.72205 26.0134 10.59653

DLYt 52.1639 5.06384 0.09708 1.95569 1.89406

LECt 4.7587 1.68529 2.89265 4.28986 25.172

DLECt 19.3187 3.07745 0.15930 1. 90235 0.6792

Italy

Yt

2.1987 7.60796 3.46020 29.4379 14.99924

DLYt 22.1623 4.45790 0.10417 1.12893 1.99786

LECt 2.5945 0.97778 7.37686 29.6216 14.99339

DLECt 15.7096 5.11987 0.17209 1.22746 0.18504

Japan

Yt 3.3268 1.27490 6.38322 7.35287 8.84752

DLYt 78.3240 6.00182 0.07663 0.83755 1.84402

LECt 6.8627 1.71296 5.24960 13.4133 8.57977

DLECt 27.0904 5.52795 0.11432 1.16167 2.07875

United KingdomYt 4.0614 3.49978 5.16691 33.2538 14.89574

DLYt 23.0553 3.09856 0.13440 2.03108 0.60158

LECt 1.30490 1.85771 3.72267 13.3919 15.17786

DLECt 24.4220 3.45138 0.14132 1.14660 0.17259

United States

Yt 0.7812 1.36799 1.75110 18.9961 14.08532

DLYt 29.1558 3.81481 0.13084 0.85069 0.81632

LECt 1.5266 2.26011 1.48048 16.0145 15.93971

DLECt 97.5578 6.85076 0.07022 0.50102 0.71664

(continued)



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The methodology requires the estimation of either an MSIA(.)VAR(.) or an

MSIAH(.)VAR (.) model.

Empirical Results

For the determination of the LYandLECintegration order, in this study we

utilized the point optimal tests of both G. Elliott et al. and of S. Ng and P. Perron.17

The results from the unit root tests are presented in table 2. According to the result

in table 2, the first difference ofLYandLECappear to be stationary. After the unit

root test, the Johansen procedure was used to determine the possible existence of

cointegration between LECandLY. The Johansen cointegration result in table 2

determined that the null hypothesis of no cointegration was not rejected. If thevariables are not cointegrated, the first difference or innovations of their variables,

DLYandDLEC, can be used to test for MS-Granger causality.

MSIA(.)VAR(.) models were selected for Canada, France, Italy, and the

United States; MSIAH(.)VAR(.) models were used for Germany, Japan, and

United Kingdom. MS models were selected based on the Akaike Information

Criteria (AIC) and LR test. In selected models, in order to determine the number of

regimes, a linear VAR is first tested against a MS-VAR with two regimes; theH0hypothesis, which hypothesizes linearity, was rejected by using the LR test sta-

tistics. Since it was observed that the two-regime models were insufficient inoverruling the linear model in explaining the relationships between the chosen

variables, three-regime models were considered next. A MS-VAR model with two

Table 2 (continued)UNIT ROOT TEST RESULTS FOR THE ANALYZED COUNTRIES

MZa MZt MSB MPT

Elliott-Rothenberg-

Stock Test Statistic

Asymptotic critical values *ERS Test CV

1% level 13.8000 2.58000 0.17400 1.78000 1% level (1.870000)

5% level 8.10000 1.98000 0.23300 3.17000 5% level (2.970000)

10% level 5.70000 1.62000 0.27500 4.45000 10% level (3.910000)

Johansen Cointegration Result

Canada

r = 0 3.147

r 1 1.194 France

r = 0 3.959

r 1 1.794 Germany

r = 0 3.623

r 1 0.423

Italy r = 0 5.426r 1 0.475 Japan r = 0 9.754r 1 2.658 United Kingdom r = 0 11.458r 1 2.578

United States

r = 0 10.193

r 1 5.480



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regimes was tested against a MS-VAR model with three regimes; the H0 hy-

pothesis, which specifies that there are two regimes, was rejected and the MS-VAR with three regimes was accepted as the optimal model because the LR

statistic was greater than the 5-percent critical value ofx2.

Table 3DATA ANALYSIS

(year: quarter)

Canada ECRIa

France ECRI1973 1975 1965 1965

1981 1983 1981:2 1982:4 1974 1975 1973:4 1975:1

1990 1992 1990:1 1992:1 1981 1981 1992:2 1994:1

2008 2009 2008:1 2009:3 1997 1999 1997:1 1999:3

2000 2003 2000:3 2003:2

2008 2009 2008:2 2009:2

Coin : 3/3 = 100% Coin : 4/5 = 80%

Germany ECRI Japan ECRI

1966:1 1967:2 1974 1975 1973:4 1975:1

1974 1975 1973:3 1975:3 1992 1993 1992:2 1994:1

1980 1982 1980:1 1982:4 1997 1999 1997:1 1999:3

1991 1993 1991:1 1994:2 2000 2002 2000:3 2003:2

2001 2002 2001:1 2003:3 2007 2010 2008:2 2009:1

2009 2009 2008:4 2009:1 2010:2 2011:2

Coin : 4/6 = 66.6% Coin : 4/6 = 66.6%

Italy ECRI United Kingdom ECRI

1964:1 1965:1 1974 1975 1974:3 1975:2

1970 1971 1970:4 1971:3 1979 1981 1979:3 1981:21973 1975 1974:2 1975:2 1990 1992 1990:2 1992:1

1980 1982 1980:2 1983:2 2008 2009 2008:2 2010:1

1993 1993 1992:1 1993:4

2007 2009 2007:3 2009:1

Coin : 4/5 = 80% Coin : 4/4 = 100%

United States ECRI

1969:4 1970:4

1973 1975 1973:4 1975:1

1980 1982 1980:1 1980:3

1990 1991 1990:3 1991:12001:1 2001:4

2008 2009 2007:4 2009:2

Coin : 4/6 = 66.6 %

aECRI = Economic Cycle Research Institute.



16/31

For the MS-VAR models selected, the periods of economic expansion (rep-

resented by regime 2 and regime 3) had a longer time duration than the duration of

the recessionary period (regime 1), which was expected. Thus, the asymmetry

between periods of expansion and recession can be understood based upon the

differences in length of time.

The transition probability matrix is ergodic and cannot be irreducible. The

ergodic transition probability matrix confirms stationarity of the regime. As is

discussed in greater detail in the works of J. D. Hamilton and R. Gallager, the

ergodic transition probabilities matrix is always covariance-stationary.18

The Business Cycle Characteristic: Table 3 shows the business cycle dates im-

plied by the Markov-switching model as well as those provided by the Economic

Cycle Research Institute (ECRI) for the sample countries. To determine how well

the estimated models perform in business cycle dating, a measure of coincidencewas used, which was put forth by F. Canova et al. and S. Altug and M. Bildirici to

calculate the number of instances in which our peak or trough dates are plus or

minus one (two) quarters away from the ECRI dates.19

Thus, allowing for a maximum discrepancy of two (three) quarters, the average

coincidence between model dating and ECRI dating for the selected countries is

82.57 percent.

Regime 1 is a recession or crisis regime, regime 2 the moderate growth regime,

and regime 3 is the high growth regime. The persistence of regimes changes from

country to country in this study. The models track fairly well the oil crisis of 19741975 (the first oil recession), 19791980 (the second oil recession), 19891991,

and the recent 2008 crisis (see table 3).

In the estimated MS-VAR models, the total length of time for the expansion

period (regime 2 and regime 3) is longer than the length of time for the recession

(regime 1), as one would expect. The results indicated the presence of significant

levels of asymmetries for the business cycles experienced by the countries ana-

lyzed in this research.

For the first modeled country, the MSIA(3)VAR(1) model is estimated for

Canada and the results are given in table 4. The first regime tends to last 2.03 years

on average, while regime 2 is comparatively more persistent with a duration of

4.57 years. Finally, regime 3which corresponds to the high growth period

tends to last 11.33 years on the average. The moderate growth regime is the most

persistent regime in Canadas economy. The computed probability of Prob(st =

3jst1 = 1) = 0.102 reflects the probability that a recession is to be followed bya period of high growth. Further, as the calculated regime probabilities are Prob(st=

1jst1 = 1) = 0.5063, Prob(st = 2jst1 = 2) = 0.7811, and Prob(st = 3jst1 = 3) =0.9018, the persistence of each regime is significantly high. Based upon our re-

sults, we witness the presence of important asymmetries in the Canadian business

cycle.



17/31

For our next G7 nation, France, the results of the MSIA(3)VAR(4) model

are presented in table 5. The transition probabilities, Prob(st = 1jst1 = 1) =0.6220, Prob(st = 2jst1 = 2) = 0.8305, and Prob(st = 3js t1 = 3) = 0.7952, de-

termine the persistence of the high growth regime (regime 3). For France, themoderate growth regime has the longest duration (8.68 years), followed by

the high growth (7.87 years) and then the recessionary regime (4.7 years). The

Table 4CANADA: MSIA(3)VAR(1) MODEL

a

(Estimation sample 1963 to 2010)

Regime 1 Regime 2 Regime 3

Variables: DLYt DLECt DLYt DLECt DLYt DLECt

Regime-specific intercept

Constant

0.005803

(1.0996)

0.000234

(2.00048)

0.004027

(2.296)

0.022415

(3.458)

0.072324

(1.1598)

0.015251

(1.4236)

Regime-specific autoregressive coefficients

DLYt-1

0.723966

(2.432)

0.864329

(1.142)

0.035845

(2.968)

0.051353

(-2.0102)

0.696373

(0.996)

0.320710

(5.786)

DLECt-10.424580(2.2486)

0.144509(3.578)

0.241773(5.245)

0.134035(4.758)

0.021715(2.285)

0.134124(1.1098)

Regime-specific standard error (SE)

SE 0.19328 0.011496

Duration and probabilities of regimes Transition probabilities

Probabilities

Duration

(in years) Regime 1 Regime 2 Regime 3

Regime 1 0.2872 2.03 Regime 1 0.5063 0.3917 0.1020

Regime 2 0.6028 4.57 Regime 2 0.2189 0.7811 0.0170

Regime 3 0.1100 11.33 Regime 3 0.0882 0.0099 0.9018

at-statistics are given in ( ) parentheses. Significance at 1 percent, 5 percent, and 10 percent are

denoted with ***, **, and *, respectively.

Log-likelihood = 257.5000, Linear system = 240.9113, AIC criterion = 9.6042, Linear system =

9.6630, LR linearity test = 33.1774, Chi(12) = [0.0009]**, Chi(18) = [0.0159]*, DAVIES = [0.0226]*

StdResids: Vector portmanteau(5): Chi(16) = 148.240 [0.5376], Vector normality test: Chi(4) =

36.850 [0.4503], Vector hetero test: Chi(12) = 143.001 [0.2820] F(12,103), Vector hetero-X test:

Chi(15) = 154.942 [0.4164] F(15,105)

PredError: Vector portmanteau(5): Chi(16) = 107.417 [0.8251], Vector normality test: Chi(4) =

28.113 [0.5899], Vector hetero test: Chi(12) = 341.837 [0.0006] F(12,103), Vector hetero-X test:Chi(15)= 364.164 [0.0015] F(15,105)

VAR Error: Vector portmanteau(5): Chi(16) = 87.197 [0.9245], Vector normality test Chi(4) =


Chi(15) = 304.941 [0.0103] F(15,105)



18/31

Table 5FRANCE: MSIA(3)VAR(4) MODEL

a





Constant

0.008122

(2.0123)

0.018382

(1.995)

0.020432

(0.9863)

0.022456

(0.7253)

0.008333

(1.986)

0.013747

(2.2586)


DLYt-1

0.309444

(1.1758)

0.221704

(2.24008)

0.227400

(1.998)

0.077770

(2.2263)

0.096939

(1.953)

0.102216

(3.2456)

DLYt-22.463520(2.2458)

0.556667(3.326)

0.486376(1.899)

0.056714(2.8956)

1.604223(1.1758)

0.093397(-2.9450)

DLYt-3

0.095537

(1.758)

0.148185

(4.8569)

0.023783

(1.2458)

0.088586

(1.1245)

0.644533

(2.0253)

0.019457

(2.9463)

DLYt-4

0.304734

(1.758)

0.704424

(2.986)

0.355334

(1.0087)

0.237879

(2.589)

0.460744

(2.968)

0.072321

(1.9958)

DLECt-1

0.857763

(2.875)

0.273319

(1.2453)

0.350418

(2.201)

0.088443

(1.1102)

1.916549

(2.80079)

0.353649

(1.0425)

DLECt-2

2.736808

(5.4258)

0.564504

(2.2456)

0.122757

(3.045)

0.186121

(1.9856)

0.891362

(5.0236)

0.125925

(1.7583)

DLECt-3

1.157364

(2.2458)

0.155886

(1.9998)

0.297510

(2.6347)

0.284682

(2.0128)

1.543321

(3.853)

0.357691

(2.1425)

DLECt-41.618901

(3.2458)

1.34241

(0.02536)

0.560385

(3.7856)

0.413202

(2.698)

0.434833

(2.02736)

0.494614

(2.0863)


SE 0.18606 0.005257


Probabilities

Duration


Regime 1 0.2301 4.70 Regime 1 0.6220 0.2156 0.1624

Regime 2 0.5369 8.68 Regime 2 0.1495 0.8305 0.02

Regime 3 0.2330 7.87 Regime 3 0.09691 0.1079 0.7952




8.9618, LR linearity test = 152.6053, Chi(36) = [0.0000]**, Chi(42) = [0.0000]**, DAVIES =

[0.0000]**

StdResids: Vector portmanteau(9): Chi(20) = 38.8372 [0.0070]**, Vector normality test:

Chi(4) = 16.5106 [0.0024]**, Vector hetero test: Chi(48) = 28.2283 [0.9898] F(48,57) =



19/31

Prob(st = 2jst1 = 1) = 0.2156 and Prob(st = 3jst1 = 1) = 0.1624 show the pos-sibilities of a recession being followed by a period of moderate and high growth,

respectively.

In the case of Germany, the MSIAH(3)VAR(1) model implies important re-

sults for the country, which are given in table 6. The result of Prob(s t= 1jst1= 1) =0.5735, Prob(st= 2jst1= 2) = 0.8607, and Prob(st= 3jst1= 3) = 0.7015 indicatethe persistence of the regimes. The ergodic probabilities point to regime 2 as being

the most dominant. Germanys transition probabilities; p11= 0.2456, p22= 0.5663,

and p33= 0.1881, highlight significant asymmetries in the nations business cycle.

The moderate growth phase is the most dominant in terms of duration lasting an

average of 7.74 years in comparison to the recessionary phase (1.90 years on

average) and the high growth phase (3.85 years on average). The computed

probabilities indicate that there is a higher probability that the German economy

goes from a crisis/recessionary phase into a moderate economic growth regime

(Prob(st= 2jst1= 1) = 0.2980) than into a high economic growth regime (Prob(st=3jst1= 1) = 0.1285).

The model selected for Japan is a MSIAH(3)VAR(4) and the results are

provided in table 7. Regime probabilities are calculated as Prob(st= 1jst1= 1) =0.6196, Prob(st = 2jst-1 = 2) = 0.8066, and Prob(st = 3jst1 = 3) = 0.7404, whichsuggest the persistence and dominance of the moderate growth regime (regime 2).

The high growth period tends to last 3.85 years on average, the crisis/recessionary

period lasts an average of 2.63 years, while the moderate growth regime has an

average duration of 5.17 years. The computed probability Prob(s t= 3jst-1 =1) =0.1566 reflects the chances that a recession is followed by a period of high growth;on the other hand, the computed probability Prob(st= 2jst1= 1) = 0.2237 indicatesa slighting higher probability that a recession is followed by a period of moderate

growth (regime 2).

The results obtained for Italy are reported in table 8. A MSIA(3)VAR(1)

model provided the best econometric performance. The transition probabilities,

Prob(st= 1jst1= 1) =0.6404, Prob(st= 2jst1= 2) = 0.7889, and Prob(st= 3jst1=3) = 0.8115, suggest the persistence of the high growth regime. The moderate

growth phase (regime 2) was found to last on average 4.97 years, while the highgrowth phase (regime 3) had a higher average duration of 5.48 years. Regime 2

was found to be the most dominant based upon the ergodic probabilities. As is the

0.3236 [0.9999], Vector hetero-X test: Chi(132) = 135.8676 [0.3910] F(132, 26) = 0.1511

[0.0000]**

PredError: Vector hetero test: Chi(48) = 87.7206 [0.0004]** F(48,57) = 2.5593 [0.0004]**, Vector

hetero-X test: Chi(132) = 137.3505 [0.3572] F(132,26) = 0.1973 [0.0000]**

VAR Error: Vector portmanteau(9): Chi(20) = 17.2954 [0.6337], Vector normality test: Chi(4) =

5.4305 [0.2459], Vector hetero test: Chi(48) = 51.5558 [0.3365] F(48,57) = 0.7588 [0.8360], Vector

hetero-X test: Chi(132) = 137.3908 [0.3563] F(132,26) = 0.1973 [0.0000]**



20/31


21/31

Table 7JAPAN: MSIAH(3)VAR(4) MODEL

a





Constant

0.015741

(0.4236)

0.014728

(1.129)

0.001124

(2.145)

0.012550

(0.4258)

0.016537

(1.1758)

0.132496

(2.0078)


DLYt-1

0.632372

(1.1463)

0.733449

(2.9906)

0.387051

(1.1432)

0.031183

(2.1789)

0.391575

(1.1423)

0.865093

(1.22)

DLYt-20.216545(1.9998)

0.573408(3.9876)

0.105819(1.856)

0.207683(1.245)

0.227146(1.5789)

0.639052(1.105)

DLYt-3

0.425351

(2.096)

0.637290

(4.046)

0.102303

(1.987)

0.061795

(2.5789)

0.465404

(2.013)

0.612293

(0.05)

DLYt-4

0.207578

(2.2156)

0.486229

(5.5602)

0.533471

(1.1452)

0.157559

(2.203)

0.359704

(1.996)

0.137021

(0.007)

DLECt-1

0.281258

(2.369)

0.624296

(1.986)

0.186757

(2.22)

0.588414

(1.478)

1.26326

(1.9985)

0.29986

(1.1145)

DLECt-2

3.024316

(3.863)

3.172938

(2.8753)

0.155116

(2.0078)

0.213996

(1.889)

0.267757

(1.11039)

1.27785

(0.7859)

DLECt-3

2.655488

(2.9862)

4.453353

(2.2094)

0.087111

(1.995)

0.225551

(2.009)

0.202555

(2.2539)

0.97718

(1.0998)

DLECt-41.026729

(4.72603)

1.927023

(0.8756)

0.877936

(2.2359)

0.027598

(1.1239)

0.529844

(1.996)

1.02541

(2.007)


SE 0.016485 0.014101


Probabilities

Duration


Regime 1 0.3259 2.63 Regime 1 0.6196 0.2237 0.1566

Regime 2 0.3770 5.17 Regime 2 0.1242 0.8066 0.0692

Regime 3 0.2971 3.85 Regime 3 0.2296 0.0301 0.7404



Log-likelihood = 250.8904, Linear system = 208.0836, AIC criterion = 8.169, Linear system = 8.1341,

LR linearity test = 85.6136, Chi(36) = [0.0000]**, Chi(42) = [0.0001]**, DAVIES = [0.0003]**



Chi(132) = 121.4073 [0.7353] F(132,26)



22/31


23/31

In table 5 of the estimated MSIA(3)VAR(4) model for France, all coefficients

are statistically significant at the conventional significance levels. The estimated

coefficients of energy consumption innovations (DLEC) and economic growth

innovations (DLY) are statistically significant in the first regime, second, and thirdregimes. Some evidence of bi-directional Granger causality was found between

energy consumption and GDP in all regimes for France. In the first equation (the

Table 8ITALY: MSIA(3)VAR(1) MODEL

a





Constant

0.009274

(2.0236)

0.003568

(1.0998)

0.016073

(1.1986)

0.014636

(2.008)

0.074890

(2.20801)

0.057690

(0.007586)


DLYt-1

1.021431

(1.0468)

0.803932

(3.2536)

0.572182

(2.122)

0.195541

(5.5783)

0.016491

(2.0543)

0.439013

(4.4496)

DLECt-10.390035(2.98604)

0.021130(2.2046)

0.414446(3.33068)

0.446809(2.2073)

0.383500(4.4369)

0.595042(2.20259)


SE 0.19044 0.014610


Probabilities

Duration


Regime 1 0.3122 2.78 Regime 1 0.6404 0.2837 0.07589

Regime 2 0.5581 4.97 Regime 2 0.1911 0.7889 0.0200

Regime 3 0.1298 5.48 Regime 3 0.0270 0.1615 0.8115




8.9208, LR linearity test = 49.4514, Chi(12) = [0.0000]**, Chi(18) = [0.0001]**, DAVIES =

[0.0001]**



Chi(15) = 76.057 [0.9386] F(15,108)

PredError: Vector portmanteau(5): Chi(16) = 188.770 [0.2751], Vector normality test: Chi(4) =282.744 [0.0000], Vector hetero test: Chi(12) = 241.171 [0.0196] F(12,106), Vector hetero-X test:

Chi(15) = 270.629 [0.0282] F(15,108)



Chi(15) = 195.886 [0.1883] F(15,108)



24/31

Table 9UNITED KINGDOM: MSIAH(3)VAR(4) MODEL

a





Constant

0.380167

(1.10896)

0.000158

(0.005896)

0.003923

(1.11254)

0.019485

(2.00251)

0.022122

(0.1152)

0.100437

(1.2469)


DLYt-1

0.436595

(0.075963)

0.896621

(2.00569)

0.081322

(0.12483)

0.122694

(2.22204)

0.416995

(1.5697)

0.216265

(2.22536)

DLYt-21.401115(2.2226)

0.457842(3.853)

0.094191(1.11086)

0.042535(2.2262)

0.703466(1.11253)

0.438603(2.22297)

DLYt-3

0.565699

(1.00425)

0.567982

(1.1425)

0.098390

(2.20278)

0.026324

(3.33073)

0.032868

(4.0086)

0.712336

(3.8862)

DLYt-4

1.226295

(2.2228)

0.425156

(2.20136)

0.030951

(2.002)

0.139165

(4.1425)

0.350411

(2.11486)

0.395766

(6.7856)

DLECt-1

0.719688

(3.856901)

0.526953

(1.2269)

0.317718

(5.22204)

0.688120

(1.1158)

0.826936

(2.00523)

0.976558

(1.1146)

DLECt-2

2.664664

(3.397622)

1.234223

(2.0996)

0.279991

(3.00236)

0.408941

(2.00853)

1.025574

(4.44253)

0.968645

(2.0024)

DLECt-3

1.591862

(2.22896)

1.051740

(1.11526)

0.150576

(2.90919)

0.214273

(1.998)

0.619190

(1.996)

0.393493

(1.1146)

DLECt-4

0.007379

(2.0869)

0.005800

(0.00586)

0.248102

(2.00785)

0.256829

(1.11425)

0.316469

(2.00252)

1.260654

(3.00316)


SE 0.380167 0.000158


Probabilities

Duration


Regime 1 0.2798 3.40 Regime 1 0.7001 0.2839 0.016

Regime 2 0.5039 4.01 Regime 2 0.1242 0.7505 0.1253

Regime 3 0.2163 3.43 Regime 3 0.0908 0.2011 0.7081




8.9036, LR linearity test = 154.3188, Chi(42) = [0.0000]**, Chi(48) = [0.0000]**, DAVIES = [0.0000]**



Chi(132) = 135.1896 [0.4068] F(132,26)



25/31

equation of LY) for all three regimes, the EC appears to be the Granger cause of

economic growth. It is determined that Granger causality exists from EC to Y for

equation 1 in the first, second, and third regimes. According to the second equation

obtained for all three regimes, Y appears to be the Granger cause of energy

consumption and the direction of causality is from LY to EC for equation 2. Thus,

we found some evidence of bi-directional Granger causality between energy

consumption and GDP in the recessionary, moderate growth, and high growth

periods for France.

For Germany, the estimated coefficients of DLY and DLEC in the MSIAH(3)

VAR(1) model are statistically significant at conventional levels (table 6). The

coefficient estimates of DLEC in equation 1 are positive for all three regimes;

however, the coefficient estimates of DLY in equation 2 are negative for regimes 1

and 3. The results of equation 1 for all three regimes indicate that energy con-

sumption is the Granger cause of GDP. According to the second equation, that is,

the equation for LEC, GDP appears to be the Granger cause of energy consumption

for all three regimes. The evidence suggests that a bi-directional Granger causalityrelationship exists between energy consumption and GDP for Germany.

For Japan, the results for the selected MSIAH(3)VAR(4) model are reported

in table 7. The estimated coefficients of energy consumption innovations (DLEC)

for equation 1 in regime 1 are very high with a number around 3 for DLECt-2,

approximately 2.7 for DLECt-3, and around 1.1 for DLECt-4; they are statistically

significant and show that energy consumption is the Granger cause of GDP. In the

second equation, the GDP appears to be the Granger cause of energy consumption

in the first and second regimes. For equation 2 and in regime 3, the coefficients of

DLY are statistically insignificant. Thus, GDP is not found to have a Grangercausality relationship with EC in regime 3 (the high growth regime). To sum up

the findings on Japan, we found a bi-directional Granger causality relationship

existing between energy consumption and GDP.

The MSIA(3)VAR(1) model presented the best econometric performance for

Italy, with results given in table 8. The estimated coefficients of energy con-

sumption innovations (DLEC) in equation 1 are significant in the first, second, and

third regimes. The DLY coefficients in equation 2 of all regimes are statistically

significant at conventional levels. The results suggest evidence of Granger cau-

sality from EC to Y and from Y to EC. In the first equation (the DLY equation) inall of the regimes, the EC is determined to be the Granger cause of economic

growth. For the second equation (the DLEC equation) in all of the regimes, the LY

PredError: Vector portmanteau(9): Chi(20) = 40.8673 [0.0039], Vector normality test Chi(4) =


Chi(132) = 137.8064 [0.3471] F(132,26)



Chi(132) = 135.5807 [0.3977] F(132,26)



26/31

Table 10UNITED STATES: MSIA(3)VAR(3) MODEL

a





Constant

0.018723

(1.114203)

0.012428

(0.7586)

0.018723

(1.114203)

0.012428

(0.7586)

0.026923

(1.4586)

0.044614

(2.0523)


DLYt-1

0.360077

(0.000253)

0.301208

(2.2536)

0.562099

(2.0012)

0.115414

(5.01869)

0.140557

(1.1466)

0.193369

(4.786)

DLYt-20.202334(2.22078) 0.435906(3.23605) 0.759883(1.1425) 0.525693(2.22053) 0.104795(1.11425) 0.083259(2.2536)

DLYt-3

1.532886

(1.0869)

0.889402

(2.05236)

0.281971

(1.999)

0.193628

(2.3336)

0.380794

(2.2207)

0.124456

(2.789)

DLECt-1

0.53089

(2.2436)

0.330024

(2.2536)

0.354439

(2.8096)

0.284957

(1.11569)

0.015562

(2.4856)

0.153418

(1.9986)

DLECt-2

0.207090

(3.0526)

0.152566

(1.14207)

0.352799

(2.22628)

0.408762

(2.0046)

0.250051

(0.4205)

0.220788

(0.0489)

DLECt-3

1.954054

(2.25369)

0.131855

(1.0634)

0.251444

(3.332)

0.357008

(2.1103)

0.069054

(2.4093)

0.248252

(1.7716)


SE 0.098830 0.009675


Probabilities

Duration


Regime 1 0.2514 3.09 Regime 1 0.6662 0.0556 0.3138

Regime 2 0.3663 17.96 Regime 2 0.05569 0.9343 0.010

Regime 3 0.3823 4.70 Regime 3 0.1595 0.05336 0.7871




8.9036, LR linearity test = 154.3188, Chi(42) = [0.0000]**, Chi(48) = [0.0000]**, DAVIES = [0.0000]**



hetero-X test: Chi(81) = 77.7634 [0.5813] F(81,33) = 0.5751 [0.9768]

PredError: Vector portmanteau(7): Chi(16) = 35.1850 [0.0037]**, Vector normality test: Chi(4) =

19.6451 [0.0006]**, Vector hetero test: Chi(36) = 44.8911 [0.1470] F(36,77) = 1.1411 [0.3091],

Vector hetero-X test: Chi(81) = 104.5442 [0.0403]*, F(81,33) = 1.4761 [0.1061]



hetero-X test: Chi(81) = 107.5708 [0.0258]*, F(81,33) = 1.5388 [0.0837]



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is determined to be the Granger cause of energy consumption. Thus, for Italy theresults point to bi-directional causality between EC and GDP in all regimes.

For United Kingdom, a MSIAH(3)VAR(4) was accepted as the best model (see

table 9). The estimated coefficients of the innovations for the energy consumption

(DLEC) are statistically significant in equation 1 for the first, second, and third re-

gimes. The DLY coefficients are statistically significant at the conventional signifi-

cance levels in equation 2 for all regimes. In the first equation in all regimes, the EC is

determined to be the Granger cause of economic growth. The LY appeared to be the

Granger cause of energy consumption in the second equations for all of the regimes.

In the MSIA(3)VAR(3) model for the United States, the estimated coefficientsof energy consumption innovations (DLEC) in equation 1 are statistically signifi-

cant at conventional level in all regimes (table 10). The parameter estimates of EC

in equation 1 of regimes 1 and 3 are negative. According to the first equation, GDP

appears to be the Granger cause of energy consumption in all regimes, and GDP is

found to be the Granger cause of energy consumption in the second equation in all

regimes evaluated. To conclude, we found some evidence of bi-directional Granger

causality between energy consumption and GDP for the United States.

Traditional Linear Granger Causality Results: For comparative purposes, stan-dard Granger causality test results are reported for the same data sets and the

results are given in table 11. Unless the business cycle is analyzed in assessing the

Table 11TRADITIONAL LINEAR GRANGER CAUSALITY RESULTS

Countries

DY/ D EC

DEC/ D Y

Canada

16.8052

1.4562

France

55.731

0.7453

Germany

2.578

2.389

Italy

3.6649

42.563

Japan

2.8086

32.445

United Kingdom

2.8037

1.1128

United States

2.2456

50.986



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relationship between energy consumption and economic growth, different results

will be obtained. There is evidence that supports the growth hypothesis for Italy,

Japan, and the United States. There is a uni-directional relationship from energy

consumption to real GDP, which means that energy consumption acts as a stimulus to

economic growth. The conservation hypothesis is suggested by our findings for Canada

and France. The conservation hypothesis is supported if an increase in Y causes an

increase in EC. There is evidence to support the neutrality hypothesis for Germany and

the United Kingdom. When comparing the traditional Granger causality results in this

study to the findings of the academic papers presented in table 1, we see a consistency

between our results and the outcomes found in some of the previous research.

Conclusion

In many papers the relationship between energy consumption and economic

growth has been investigated repeatedly employing different models for numerous

countries over a variety of time frames; however, despite the usage of the same

variables, we encounter various results with different coefficients and causality

relationships even in the studies for the same countries. Johansen, Engle-Granger,

and autoregressive distributed lag (ARDL) cointegration methods are used in-

tensively, but they have their shortcomings. One weakness is the avoidance of the

nonlinear structure of the time series, especially of the nonlinear GDP series, thatis evaluated as a measure of economic performance under business cycles. From

the perspective of energy economics, these models parameters were assumed to

be constant over the sample period, which suggests the relationship between GDP and

energy consumption is stable although real world experiences teach us otherwise with

numerous crises developing (1974 and 1979 oil crises, 2008 global recession).

In this paper, MS-VAR models were estimated to analyze the relationship

between energy consumption and economic growth. The MS-VAR and MS-

Granger causality approaches were utilized to evaluate causality in three different

regimes of the business cycle. In the first step in testing for causality, we determinethe integration order of LGDP and LEC using the ERS (Elliott, Rothenberg and

Stock) and Ng and Perron tests. The results indicate that the first difference of

LGDP and LEC appear to be stationary. The LGDP and LEC are integrated of

order one, I(1). Since the variables are integrated, the maximum likelihood pro-

cedure of Johansen cointegration was used to examine the possible existence of

cointegration between LGDP and LEC. According to the results, the null hy-

pothesis of no cointegration was not rejected. Although the variables are I(1), they

are not cointegrated and the first difference or innovations of the variables,

DLGDP and DLEC, will be used to test for MS-Granger causality.To analyze Granger causality between energy consumption and economic

growth in the G7 countriesCanada, France, Germany, Italy, Japan, the



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United States, and the United Kingdomthe Markov-Switching VAR method

was used. Changes in the behavior of the variables of the MS-VAR models were

possible to detect. In this study, different MS-VAR models were estimated and the

best model was selected based on AIC and LR tests. By incorporating the business

cycles into the models, the Granger causality between energy consumption and

economic growth was investigated by MS-VAR and MS-Granger causality. The

first difference of these variables was used in the modeling process.

Causality in different regimes of business cycle and the changes in the behavior

of the variables with MS-VAR models were possible to detect. MSIA(3)-VAR(.)

models were selected for Canada, France, Italy, and the United States, while

MSIAH(3)-VAR(.) models were chosen for Germany, Japan, and the United

Kingdom. According to the first equation, the GDP appears to be the Granger

cause of energy consumption in all regimes and GDP was determined to be the

Granger cause of energy consumption in the second equation in all regimes. In

summation, we found some evidence of bi-directional Granger causality between

energy consumption and GDP. For Japan, GDP was not found to be the Granger

cause of EC in the high growth regime; however, overall, there is a bi-directional

Granger causality relationship between energy consumption and GDP. For com-

parative purposes, standard Granger causality test results were reported for the

same data sets. There is evidence that supports the growth hypothesis for Italy,

Japan, and the United States. The conservation hypothesis was best supported by

the results for Canada and France. Last, the neutrality hypothesis was supported bythe evidence from Germany and the United Kingdom.

According to the results of this paper, an increase in energy consumption di-

rectly affects economic growth and that economic growth also stimulates further

energy consumption in that country. With these findings, energy policies aimed at

improving the energy infrastructure and increasing the energy supply are the

appropriate options for these countries since energy consumption increases the

income level. Moreover, the policy of conserving energy consumption might be

implemented with little or no adverse effect on the economic growth of an

economy such as a less energy-dependent economy.

NOTES

1Please see orthodox macroeconomic books.

2R. U. Ayres, H. Turton, and T. Casten, Energy Efficiency, Sustainability and Economic

Growth,Energy, vol. 32, no. 5 (2007), pp. 63448.

3R. Ayres et al., op. cit.

4See N. Rosenberg, The Effects of Energy Supply Characteristics on Technology and Eco-

nomic Growth, inEnergy, Productivity, and Economic Growth, eds. S. Schurr, S. Sonenblum, and

D. Wood (Cambridge, Massachusetts: Oelgeschlager, Gunn and Hain, 1983); N. Rosenberg, The



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Role of Electricity in Industrial Development, The Energy Journal, vol. 19, no. 2 (1998), pp. 724;

and M. E. Bildirici, T.Bakrtasx, and K. Kaykc, Economic Growth and Electricity Consumption: An

ARDL Analysis, 31st CIRET Conference, Vienna, Austria, September 58, 2012, available at

https://www.ciret.org/conferences/vienna_2012/papers/upload/p_44-584075.pdf .

5R. Ferguson, W. Wilkinson, and R. Hill, Electricity Use and Economic Development,

Energy Policy, vol. 28, no. 13 (2000), pp. 92334.

6J. Darmstadter, J. Dunkerley, and J. Alterman, How Industrial Societies Use Energy (Wash-

ington, D.C.: Resources for the Future, 1979); S. H. Schurr, Energy Efficiency and Productive

Efficiency: Some Thoughts Based on American Experience, Energy Journal, vol. 3, no. 3 (1982),

pp. 314; and N. Rosenberg, The Effects of Energy Supply Characteristics on Technology and

Economic Growth.

7R. H. Rasche and J. A. Tatom, The Effects of the New Energy Regime on Economic Capacity,

Production, and Prices, Federal Reserve Bank of St. Louis Review, vol. 59, no. 6 (1977), pp. 1024.8Hendrik S. Houthakker, Some Calculations of Electricity Consumption in Great Britain,

Journal of the Royal Statistical Society, series B, vol. 114, no. 3 (1951), pp. 35171; F. M. Fisher

and C. A. Kaysen, A Study in Econometrics: The Demand for Electricity in the United States

(Amsterdam: North-Holland, 1962); R. E. Baxter and R. Ress, Analysis of Industrial Demand for

Electricity, Economic Journal, vol. 78, no. 310 (1968), pp. 27798; H K. P. Anderson, Resi-

dential Demand for Electricity: Economic Estimates for California and the United States, Journal

of Business, vol. 46, no. 4 (1973), pp. 52653; S. Houthakker and L. D. Taylor, Consumer Demand

in the United States: Analyses and Projections (Cambridge, Massachusetts: Harvard University

Press, 1970); J. W. Wilson, Residential Demand for Electricity, Quarterly Review of Economics

and Business, vol. 11, no. 1 (1971), pp. 722; T. Bakrtasx, S. Karbuz, and M. Bildirici, An

Econometric Analysis of Electricity Demand in Turkey,ODTU Gelisxme Dergisi, vol. 27, no. 12

(2000), pp.2334; and M. Bildirici and T. Bakrtas, Econometric Model of Electricity Demand in

Turkey as a Measure of Economic Growth and Development, The Journal of Energy and De-

velopment, vol. 33, no. 1 (autumn 2007, copyright 2009), pp. 3348.

9J. Kraft and A. Kraft, On the Relationship between Energy and GNP, The Journal of Energy

and Development, vol. 3, no. 2 (spring 1978), pp. 40103; Ernst R. Berndt, The Demand for

Electricity: Comment and Further Results, MIT Energy Laboratory Working Paper no. MIT-

EL78-021WP, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, 1978;

A. T. Akarca and T. V. Long, Notes and Comments on the Relationship between Energy and GNP:A Re-examination,The Journal of Energy and Development, vol. 5, no. 2 (spring 1980), pp. 32631;

E. S. H. Yu and B. K. Hwang, The Relationship between Energy and GNP: Further Results, Energy

Economics, vol. 6, no. 3 (1984), pp. 18690; E. S. H. Yu and J. Y. Choi, The Causal Relationship

between Energy and GNP: An International Comparison,The Journal of Energy and Development,

vol. 10, no. 2 (spring 1985), pp. 24972; and U. Erol and E. S. H. Yu, On the Causal Relationship

between Energy and Income for Industrialized Countries,The Journal of Energy and Development,

vol. 13, no. 1 (autumn 1987), pp. 11322.

10F. Fallahi, Causal Relationship between Energy Consumption (EC) and GDP: A Markov-

Switching (MS) Causality, Energy, vol. 36, no. 7 (2011), pp. 4165170, and M. Bildirici, The

Relationship between Economic Growth and Electricity Consumption in Africa: MS-VAR and MS-

Granger Causality Analysis, The Journal Energy and Development, vol. 37, no. 2 (spring, 2011,

copyright 2012), pp. 179205.

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