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THE JOURNAL OF ENERGY

AND DEVELOPMENT

Melike E. Bildirici,

“The Relationship between Economic Growth

and Electricity Consumption in Africa:

MS-VAR and MS-Granger Causality Analysis,”

Volume 37, Number 2

Copyright 2012



THE RELATIONSHIP BETWEEN ECONOMIC

GROWTH AND ELECTRICITY CONSUMPTION

IN AFRICA: MS-VAR AND MS-GRANGER

CAUSALITY ANALYSIS

Melike E. Bildirici*

Introduction

The relationship between energy consumption and economic growth is of greatimportance for both developed and developing nations.1 The level of energy

consumption is a measure of economic development, and energy itself is a mainfactor of production in addition to the traditional inputs—capital and labor—amongothers (i.e., raw materials, technology). Energy and electricity consumption play

a vital role in the economic development of countries and, therefore, have becomea focus of many involved in the economics arena.Electricity consumption has been analyzed through a plethora of perspectives

within the field of energy economics. In much of the literature, energy con-sumption is a significant metric in assessing the level of economic development;other research has concentrated on energy as a key factor in the production pro-cess. The seminal works of H. Houthakker, F. Fisher and C. Kaysen, R. Baxter and R. Ress, H. Houthakker and L. Taylor, J. Wilson, T. Cargill and R. Mayer,K. Anderson, and T. Mount et al. have focused on energy demand and price and

*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 fromthat institution. Dr. Bildirici’s studies have appeared in such publications as The Journal of Energy

and Development , Expert Systems with Applications, Family History, Energy Economics, JRSE , Applied Econometrics and International Development , The International Journal of Applied

Econometrics and Quantitative Studies, The Journal of Economic and Social Research, METU

Studies in Development , YKER, Iktisat , and Isletme and Finans.

The Journal of Energy and Development , Vol. 37, Nos. 1 and 2

Copyright Ó 2012 by the International Research Center for Energy and Economic Development(ICEED). All rights reserved.

179



income elasticities of energy.2 In these pioneering studies, some papers empha-sized energy as a major factor of production. R. Rasche and J. Tatom’s work determined that the increase of energy prices stimulated the decreasing trends ongross national product (GNP) by using energy, land, labor, and capital.3 J. Kraft

and A. Kraft, A. Akarca and T. Long, E. Yu and J. Choi, and U. Erol and E. Yuanalyzed the causality relationship between electricity/energy consumption and economic growth.4 Subsequent studies that followed examined the causality be-tween electricity consumption and economic growth in various countries and regions.

When reviewing the results obtained from the academic research regarding therelationship between electricity consumption and economic growth, it was found that different conclusions about the direction of causality are obtained. The dif-ferences in these causality results can be categorized into four hypotheses:‘‘neutrality hypothesis,’’ ‘‘conservation hypothesis,’’ ‘‘growth hypothesis,’’ and ‘‘feedback hypothesis.’’ (1) The neutrality hypothesis suggests that there is nocausality between economic growth and energy (electricity) consumption. (2) The

feedback hypothesis states that a bi-directional causality exists running betweeneconomic growth and energy (electricity) consumption and between energyconsumption and economic growth. (3) The conservation hypothesis asserts thatcausality is uni-directional running from economic growth to energy (electricity)consumption. When causality runs from economic growth to energy consumption,

an economy is less dependent on energy; thus, energy conservation policies, suchas phasing out energy subsidies, may not adversely affect economic growth. (4)The growth hypothesis evaluates the existence of uni-directional causality fromenergy (electricity) consumption to economic growth.5 According to the growthhypothesis, a state’s economy is energy dependent. In this case, the reduction of energy (electricity) consumption will lead to a decline in economic growth be-cause energy consumption is a prerequisite for this growth; thus, energy is a directinput in the production process and/or is an indirect input that complements labor and capital inputs. This implies that a negative shock to electricity consumption

leads to higher electricity prices or electricity conservation policies and, in turn,will have a negative impact on GDP.6

To date, energy economists primarily have focused on causality between en-ergy and economic growth in European and Asian countries, with relatively fewer studies concentrating on African nations (A. Akinlo, A. Kouakou, N. Odhiambo,C. Jumbe, Y. Wolde-Rufael, G. De Vita et al., J. Squalli, K. Jefferis, M. Belloumi,S. Nondo et al., and C. Adebola).7 These papers primarily utilized conventionalmethods of analysis: autoregressive distributed lag (ARDL), Johansen and EngleGranger cointegration, and the like. However, these methods are not suitable when

attempting to model business cycle conditions. With these approaches, the pa-rameters are assumed to be constant over the sample period, which means therelationship between GDP and energy and/or electricity consumption is assumed

THE JOURNAL OF ENERGY AND DEVELOPMENT180



to be stable. Of course, this assumption of stability is not very reflective of the realworld economic situation during the past decades as can be witnessed by a sig-nificant 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 (SouthAfrica, Tunisia, Togo, and Zimbabwe among others). Clearly, the business cycleaffects the relationship between GDP and energy or electricty consumption. Intime-series analysis, the phase of the business cycle must be taken into account;otherwise, the estimated parameters would be incorrect and misleading.

As the focus of this study is Africa, we have reviewed several of the most pertinent works in the field. L. Esso used the Gregory and Hansen testing approachto determine the threshold cointegration for seven African states.8 E. Kebede,J. Kagochi, and C. Jolly estimated the dates of structural breaks for numerousAfrican countries.9 Their results suggested that the first structural breaks took

place between 1974 and 1979 (in Libya and Nigeria the first structural breaksoccurred in 1989 just after the stock market crash in the United States and just

prior to the Gulf War). However, threshold cointegration analysis is not particu-larly suitable in situations with multiple structural breaks.

One way to overcome these problems is to divide the sample into sub-samples based on the structural breaks; yet, the exact date of these changes are not knownand the researcher must determine it endogenously based on the data. However,

there is no guarantee that the relationship between GDP and energy/electricityconsumption changes at the same date as the break dates of the variables.10

In this paper, the Markov-Switching Vector Autoregression (MS-VAR) modelis used to analyze the relationship between electricity consumption and economicgrowth for nine African nations over a 40-year period (1970 to 2010). This studycan be described as complementary to the previous empirical papers; nonetheless,it differs from the existing literature in certain aspects. First, relative to the pre-vious academic research, it employs MS-VAR methodology. Second, it also uti-lizes the Markov-Switching Granger Causality (MS-Granger causality) analysis.

The MS-Granger causality approach allows for a deeper investigation of causalityunder different GDP regimes.The remainder of this paper is as follows: the data and methodology are

identified in the subsequent section. Thereafter, we present the empirical results.The last section includes the conclusions and policy implications.

Data and Methodology

Data: In this study, the relationship between electricity consumption (EC) and economic growth (GDP) was investigated using the MS-VAR method for nineAfrican countries over the period from 1970 to 2010. The countries chosen for this

ECONOMIC GROWTH & ELECTRICITY IN AFRICA 181



study include Algeria, Egypt, Morocco, Nigeria, South Africa, Sudan, Togo,Tunisia, and Zimbabwe. These countries were selected based upon the availabilityof data on the incorporated variables. In this study, Y represents the per-capitagross domestic product. Electricity consumption ( EC ) is taken as LEC = log( EC t /

EC t-1) and Y as LY = log(Y t /Y t-1). The data were taken from the World Bank,International Energy Agency (IEA), and the Organization for Economic Co-operation and Development (OECD).

Methodology—The MS-VAR Analysis: The Hamilton model, which allowsfor positive and negative shocks, is given as:11

yt ¼ m st ¼ f yt À1Àm st À1ð Þ þ ut ð1Þ

where m st is m1 £ 0 when st = 1, and m2 > 0 when st = 2, and where ut ; iid N(0,s 2)when |f| < 1. st is a discrete variable that takes on the values of 1 or 2.

The Markov chain is ergodic and irreducible; a two-state Markov chain withtransition probabilities pii has unconditional distribution given by

Pr st ¼ 1ð Þ ¼1À p22

2À p11À p22

; Pr st ¼ 2ð Þ ¼1À p11

2À p11À p22

: ð2Þ

As H.-M. Krolzig denoted, to obtain the impulse response functions in MS-VAR models, which have autoregressive dynamics that are independent from theregime, one utilizes the MS(M)–VAR(1) model below.12 The impulse-responsefunction for MS(M)–VAR(1) is

yt ¼ y0t ; . . . ; y0

t À Pþ1

À Á9, yt ¼ H j t þ Ayt À1 þ ut : ð3Þ

If {ut , x t , Y t –1}, the conditional expectation of yt +h is

yt þh t j ¼ H j t þh t j þ Ayt þhÀ1 t j , and j t þh t j ¼ F hj t and F ¼ P0: ð4Þ

In this situation, the impulse-response function is

ET =j hð Þ ¼ J Xh

k ¼0Ak HF hÀk

=j where J ¼ I K 0. . . 0½ ¼ i0

1 I K : ð5Þ

In the MSIA(M)–VAR(1) model, if g t is g t = xt5 yt, g t = Mxt–1 + Pg t–1 + et, and xt = Fxt–1 + ht. In matrix form, that is represented by equation (6):




g t

j t

!

|fflffl{zffl ffl} g Ãt

¼P M

0 F

!

|fflfflfflfflfflffl{zfflfflfflfflfflffl} PÃ

g t À1

j t À1

!

|fflfflfflffl{zfflfflfflffl} g Ãt À1

þet

ht

!

|fflffl{zfflffl} eÃt

ð6Þ

where the conditional expectation of g t is E g Ãt þhjg Ãt

Â Ã¼ PÃhg Ãt when yt ¼P M

i¼1 j it yt with the conditional expectation of yt +h,

E yt þhj yt ; j t

Â Ã¼X M

i¼1E j it þh yt þh yt ; j t jÂ Ã

¼ 10 M I K : 0 K ; M

À Á E g Ãt þh g Ãt

Â Ã¼ 10

M I K 0 K ; M

Â Ã P M

0 F

!h g t

j y

" #¼ 10

M I K 0 K ; M

Â ÃPÃh

g t

j y

" #:

ð7Þ

The impulse-response function is represented by equation (8)

ET =u hð Þ ¼ 10 M I K 0 K ; M

Â ÃPÃh

j t =u

0 M ;1

!and

ET =j hð Þ ¼ 10 M I K 0 K ; M

Â ÃPÃh

=j t yt

=j t

!:

ð8Þ

The Markov chain is ergodic, irreducible, and there does not exist an absorbingstate, i.e., j p 2 0; 1ð Þ for all m = 1, . . . , M , where j p is ergodic or unconditional

probability of regime q.

Methodology—The MS-Granger Causality Analysis: A. Warne and Z.Psaradakis et al. determined different definitions of causality based on Granger causality in the context of Markov-switching VAR models.13 F. Fallahi also uti-lizes Granger causalities in his analysis of the relationship between GDP and energy consumption.14 Based on the coefficients of the lagged values of LY and

LEC in the equation for LEC and LY , we could determine the existence of cau-salities between these two variables. In the equation for LEC , if any of the co-efficients of LY t are significantly different from zero, in any of the regimes, then:

LY t

LEC t

!¼

m1; st

m2; st

!þ Sq

k ¼1

fk ð Þ

11; st fk ð Þ

12; st

fk ð Þ

21; st fk ð Þ

22; st

" #LY t Àk

LEC t Àk

!þ

et

et

!: ð9Þ

It is concluded that LY ( LEC ) is a Granger cause of LEC (Y ) in that regime. Granger causalities are detected by testing H 0:f12

(k )= 0 and H 0:f21(k )= 0.




Empirical Results

Unit Root and Traditional Cointegration Result: For the determination of the LY and LEC integration order, in this study we utilized the point optimal tests

of both G. Elliott et al. and of S. Ng and P. Perron.15 The results from the unit roottests are shown in table 1. The results indicate that the null hypothesis of the unitroot cannot be rejected at the 5-percent level of significance for these variables;however, the first difference of LY and LEC appear to be stationary. Thus, it can beconcluded that the LY and LEC are integrated of order one, I(1). Since the vari-ables are integrated, we used the maximum likelihood procedure of Johansen toexamine the possible existence of cointegration between LY and LEC . If thevariables are I(1) and not cointegrated, then the first difference or innovations of the variables, DLY and DLEC , can be used to test for MS-Granger causality.

Business Cycle Characteristics and Model Selections: For each regime, the behavior of the regime probabilities was analyzed and the probability of durationwas computed. MSIA(p)–VAR(q) models were selected for Algeria, Egypt, Mo-rocco, Tunisia, Togo, and Zimbabwe; MSIAH(p)–VAR(q) models were chosenfor Sudan, Nigeria, and South Africa. The first difference or innovations of thevariables was used for the Markov Switching-Granger Causality analysis. The MSmodels were selected based on the Akaike Information Criteria (AIC) and LR

testing. In all models, in order to determine the number of regimes, a linear VAR istested against a MS–VAR with two regimes, and the H 0 hypothesis, which hy- pothesizes linearity, was rejected by using the LR test statistics. A MS–VAR model with two regimes was tested against a MS–VAR model with three regimes;the H 0 hypothesis, which specifies that there are two regimes, was rejected and theMS–VAR with three regimes was accepted as the optimal model as the LR statisticwas greater than the 5-percent critical value of c2. The first regime is indicative of an economic recession phase, the second regime represents a moderate growth

phase, and regime three the high growth phase in the models.

The estimated models show strong business cycle characteristics. The persis-tence of regimes is observed to change on a country-by-country basis. The modelstrack fairly well the oil crises of 1974–1975, 1979–1980, 1989–1991, and the 2008Great Recession (table 2).

As expected, the total length of time for the expansion period (regime 2 and regime3) is longer than the total length of time for the recessionary period (regime 1) in allmodels, with the exception of Zimbabwe.16 Regime 1 approximates recessionary dates,whereas regimes 2 and 3 show moderate and high growth periods, respectively.

The first model is the MSIA(2)–VAR(2) for Algeria with the results highlighted

in table 3. The result of Prob(st = 1jst–1 = 1) = 0.6018 and Prob(st = 2jst–1 = 2) =0.6472 demonstrates the persistence of the regimes. Furthermore, this situation isindicative of the presence of important asymmetries in the business cycle. The




Table 1RESULTS FROM UNIT ROOT TESTS

MZa MZt MSB MPT

Elliott-Rothenberg-

Stock Test Statistic

AlgeriaY 1.10654 0.88244 0.79748 47.5715 114.1140DLY –14.28310 –3.99331 0.12764 2.80788 3.466681EC –0.97627 –0.43031 0.44077 13.7498 17.28106DLEC –16.3625 –3.85730 0.10763 1.50841 1.451736

EgyptY –0.95545 –0.39435 0.41273 12.9442 47.5977DLY –12.1925 –3.41517 0.10981 2.21672 2.940906

EC –1.28896 –0.52654 0.40850 12.0605 92.0732DLEC –12.3129 –4.30449 0.10876 2.65211 2.576161

MoroccoY 2.56981 2.55057 0.99251 89.5931 27.3645DLY –13.8287 –3.61083 0.10888 1.84304 1.982849EC –3.32372 –1.25312 0.37702 7.34038 26.14827DLEC –11.9796 –4.27001 0.10899 2.71166 2.177358

NigeriaY –5.10763 –1.00239 0.33173 5.35116 36.1098DLY –25.0170 –3.41424 0.11348 1.37662 2.216993

EC 1.18349 1.02624 0.86713 55.7899 166.4172DLEC –15.8242 –3.77631 0.11545 1.68408 1.145021

South AfricaY –4.90696 –1.18356 0.33347 5.91110 24.28940DLY –13.9958 –3.63518 0.10828 1.78938 1.817332EC 1.31626 2.16748 1.64669 188.338 13.12355DLEC –12.6120 –3.19876 0.11434 3.07855 2.239174

SudanY 2.74492 1.42221 0.51812 30.0148 38.18072DLY –14.02088 –3.9897 0.12158 1.14379 2.46238EC 2.13023 1.84842 0.86771 65.5729 13.1952DLEC –17.9948 –3.99950 0.10669 1.36174 1.304168

TogoY –4.31891 –1.37718 0.31887 5.81185 26.06389DLY –17.8610 –3.98097 0.10690 1.39871 1.694243EC 0.12782 0.07211 0.56419 22.9675 37.16966DLEC –17.9447 –3.99392 0.10668 1.37066 1.374975

TunisiaY 2.43615 2.90967 1.19437 124.089 33.7311

DLY –13.04945 –3.64984 0.10622 2.10179 1.39136

(continued )




growth regime of the economy is the most persistent phase in the Algerian economy.While the regime 1 is estimated to last on average 2.51 years, regime 2 is 2.83 years.

In table 4, the MSIA(3)–VAR(1) offered the best econometric performance for Egypt. The transition probabilities, Prob(st = 1 jst–1 = 1) = 0.8511, Prob(st = 2 jst–1 =2) = 0.8491, and Prob(st = 3 jst–1 = 3) = 0.5681, suggest the persistence of therecessionary phase (regime 1) but also of the moderate growth phase (regime 2).

The ergodic probabilities point to regime 2 as being the most dominant as can beseen in the transition probabilities (p11 = 0.1573, p22 = 0.6448, and p33 = 0.1979),which also demonstrate the asymmetries within the business cycle in Egypt. Re-gime 2 is determined to last, on average, 6.63 years for Egypt, while the averageduration of the high growth phase is 1.75 years and the recessionary phase has anaverage duration of 1 year.

Turning to another North African nation, Morocco, we find that the MSIA(2)– VAR(2) model offered important results in the analysis, which are given in table 5.The regime 1 tends to last 2.18 years on average, while regime 2 also is persistent

(3.82 years). The results of Prob(st = 1 jst–1 = 1) = 0.5422 and Prob(st = 2 jst–1 = 2) =0.7382, which highlight a persistence of regimes in the case of Morocco. Theergodic probabilities indicate that regime 2 is the most dominant. The transition

Table 1 (continued)RESULTS FROM UNIT ROOT TESTS

MZa MZt MSB MPT

Elliott-Rothenberg-

Stock Test Statistic

EC 1.47309 3.57669 2.42803 414.066 15.12620DLEC –16.1047 –4.82165 0.10752 1.58086 1.153900

ZimbabweY –3.9915 –0.9313 0.63256 5.86575 11.05394DLY –14.2775 –2.62298 0.11871 1.90004 2.413181EC 0.96336 0.83486 0.86661 53.4779 10.8753DLEC –13.7844 –2.61710 0.11896 6.65750 1.7543

Asymptotic critical values

1-percent level –13.8000 –2.58000 0.17400 1.78000 1.8700005-percent level –8.10000 –1.98000 0.23300 3.17000 2.97000010-percent level –5.70000 –1.62000 0.27500 4.45000 3.910000

Johansen cointegration resultsr = 0 9.98 r = 0 10.125 r = 0 11.78

Algeria r £ 1 0.125 Egypt r £ 1 1.30 Morocco r £ 1 1.785

r = 0 10.06 r = 0 11.63 r = 0 10.41 Nigeria r £ 1 0.425 South Africa r £ 1 1.03 Sudan r £ 1 1.101

r = 0 12.165 r = 0 8.60 r = 0 11.64

Togo r £ 1 1.734 Tunisia r £ 0 1.12 Zimbabwe r £ 1 1.986




probabilities for Morocco are p11 = 0.3638 and p22 = 0.6362, which demonstratethe significance of asymmetries in Morocco’s business cycle. The results suggestthat regime 2 is the most persistent of the three regimes that were analyzed.

The MSIAH(3)–VAR(1) model was determined to be the best suited for

modeling Sudan (table 6). The first regime of the economy tends to last an averageof 2.48 years. Prob(st = 1 jst–1 = 1) = 0.5962, Prob(st = 2 jst–1 = 2) = 0.7445, and Prob(st = 3 jst–1 = 3) = 0.5996 suggest the persistence of the moderate growth

period. Finally, high growth periods tend to last 2.50 years on average, similar torecessionary periods (2.48 years). Regime 2 is found to be the most persistent,which also is confirmed by the average duration (3.91 years) of each regime. Thecomputed probability, (i.e., Prob(st = 3 jst–1 = 1) = 0.00095) reflects the low chancethat a recession is followed by a period of high growth; on the other hand, thecomputed probability, (i.e., Prob(st = 2 jst–1 = 1) = 0.4028) reflects the relatively

higher probability that a recession is followed by a moderate growth phase.In the case of Tunisia, the MSIA(3)–VAR(1) model provided the best econo-

metric performance (results are presented in table 7). The coefficient of the

Table 2REGIME 1 (RECESSIONARY PHASE) DATING ANALYSIS

Algeria Egypt Morocco

1975:1 – 1975:1 1973:1 – 1973:1 1977:1 – 1977:11979:1 – 1980:1 1987:1 – 1987:1 1981:1 – 1983:11985:1 – 1987:1 1990:1 – 1991:1 1987:1 – 1987:11991:1 – 1995:1 2009:1 – 2009:1 1992:1 – 1994:11997:1 – 1997:1 1995:1 – 1997:12000:1 – 2001:1 1999:1 – 2000:12005:1 – 2006:1 2009:1 – 2009:1

Nigeria South Africa Sudan1975:1 – 1975:1 1975:1 – 1975:1 1973:1 – 1973:11978:1 – 1979:1 1981:1 – 1983:1 1978:1 – 1980:1

1981:1 – 1981:1 1985:1 – 1985:1 1985:1 – 1985:11983:1 – 1984:1 1989:1 – 1992:1 1988:1 – 1990:11987:1 – 1987:1 1997:1 – 1998:1 2009:1 – 2009:12002:1 – 2002:1 2008:1 – 2008:1

Togo Tunisia Zimbabwe1975:1 – 1975:1 1974:1 – 1974:1 1975:1 – 1975:11979:1 – 1979:1 1978:1 – 1978:1 1977:1 – 1978:11981:1 – 1983:1 1981:1 – 1981:1 1983:1 – 1984:11991:1 – 1993:1 1988:1 – 1988:1 1987:1 – 1987:11999:1 – 2001:1 1994:1 – 1995:1 1992:1 – 1993:1

2009:1 – 2009:1 2002:1 – 2002:1 1995:1 – 1995:12009:1 – 2009:1 1997:1 – 2003:1

2006:1 – 2009:1




distributed-lag component of the EC variable is positive and statistically significantwhile the transition probabilities, Prob(st = 1jst–1 =1) = 0.5245, Prob(st = 2jst–1 = 2) =0.8690, and Prob(st = 3 jst–1 = 3) = 0.6603, suggest the persistence of the moderategrowth phase in the Tunisian economy. Regime 2 is determined to last on average7.67 years, whereas the average duration of regime 3 lasts an average of 2.94 years.

The ergodic probabilities demonstrate that regime 2 is the most dominant, and thetransition probabilities, p11 = 0.2477, p22 = 0.6582, and p33 = 0.0941, highlight theimportance of asymmetries in the Tunisian business cycle.

Table 3ALGERIA: MSIA(2)–VAR(2) MODEL RESULTS

(Estimation sample 1973 to 2010)

Regime 1 Regime 2

DLY DLEC DLY DLEC

Con (R.1)0.0289

(2.7884)0.0466

(2.2934) Con (R.2)0.0097

(1.0926)0.0343

(2.2146)

DLY-10.6480

(3.2157)2.0298

(5.3435) DLY-10.2956

(1.7233) –1.0316(–3.1290)

DLY-2 –0.0672(0.3602)

–1.2700(–3.5375) DLY-2

0.0982(1.3003)

–0.1306(–2.9299)

DLEC-10.3868

(3.2856) –0.6337(–2.4473) DLEC-1

0.1632(3.8304)

0.5728(1.9255)

DLEC-20.1103

(2.1536)0.7396

(3.5590) DLEC-2 –0.0924(–2.0750)

0.3114(1.9996)

Transition probabilities

Regime 1 Regime 2 ProbabilityDuration(in years)

Regime 1 0.6018 0.3982 Regime 1 0.4698 2.51Regime 2 0.3528 0.6472 Regime 2 0.5302 2.83

Log-likelihood = 168.6015, Linear system = 156.0004

AIC criterion = –7.9779, Linear system = –7.9445LR linearity test = 25.2021, Chi(10) = [0.0050], Chi(12) = [0.0139], DAVIES = [0.0942]StdResids: Vector portmanteau (5): Chi(12) = 10.0809 [0.6089]StdResids: Vector normality test: Chi(4) = 2.8850 [0.5773]StdResids: Vector hetero test: Chi(24) = 18.2282 [0.7918] F(24,61) = 0.5924 [0.9215]StdResids: Vector hetero-X test: Chi(42) = 32.8851 [0.8421] F(42,45) = 0.5019 [0.9870]PredError: Vector portmanteau (5): Chi(12) = 11.3509 [0.4991]PredError: Vector normality test: Chi(4) = 3.0280 [0.5532]PredError: Vector hetero test: Chi(24) = 51.4426 [0.0009] F(24,61) = 3.1931 [0.0001]PredError: Vector hetero-X test: Chi(42) = 64.2771 [0.0150] F(42,45) = 2.0803 [0.0085]VAR Error: Vector portmanteau (5): Chi(12) = 9.2748 [0.6793]

VAR Error: Vector normality test: Chi(4) = 2.2340 [0.6928]VAR Error: Vector hetero test: Chi(24) = 30.6747 [0.1634] F(24,61) = 1.1057 [0.3651]




The MSIAH(3)–VAR(4) offered the best econometric performance for mod-eling South Africa (results in table 8). The first regime tends to last 2.01 years onaverage, while regime 2 is persistent (2.59 years). High growth periods tend to last2.56 years on average. Prob(st = 1 jst–1 = 1) = 0.5020, Prob(st = 2 jst–1 = 2) = 0.6145,and Prob(st = 3 jst–1 = 3) = 0.6087 suggest the persistence of moderate growth in theSouth African economy. The computed probability, i.e., Prob(st = 3 jst–1 = 1) = 0.0922,reflects the low probability that a recession is followed by a period of high growth;

alternatively, Prob(st = 2 jst–1 = 1) = 0.4058 reflects the much greater probability thata recession is followed by a period of moderate growth. The ergodic probabilities

point to the dominance of regime 1. Similar to the findings in the other African

Table 4EGYPT: MSIA(3)–VAR(1) MODEL RESULTS


Regime 1 Regime 2 Regime 3

DLY DLEC DLY DLEC DLY DLEC

Con (R.1) –0.0268(2.0013)

–0.0136(2.1045)

Con (R.2) 0.0232(0.1425)

0.0509(1.78)

Con (R.3) 0.0050(1.112)

0.1546(0.125)

DLY-1 –2.7447(–1.0986)

6.0784(3.045)

DLY-1 0.2774(2.001)

0.3487(2.787)

DLY-1 0.2513(1.999)

0.6122(2.2212)

DLEC-1 2.345(3.7001)

2.868(2.4614)

DLEC-1 –0.0763(–2.549)

0.0799(1.897)

DLEC-1 0.5599(3.001)

–0.4495(1.110)


Regime 1 Regime 2 Regime 3 ProbabilitiesDuration(in years)

Regime 1 0.8511 0.0185 0.1304 Regime 1 0.1573 1.00Regime 2 0.0769 0.8491 0.0741 Regime 2 0.6448 6.63Regime 3 0.0001 0.0432 0.5681 Regime 3 0.1979 1.75

Log-likelihood = 182.9413, Linear system = 140.9782AIC criterion = –8.4293, Linear system = –7.1340LR linearity test = 83.9260, Chi(12) = [0.0000], Chi(18) = [0.0000], DAVIES = [0.0000]StdResids: Vector portmanteau (5): Chi(16) = 25.8103 [0.0568]

StdResids: Vector normality test: Chi(4) = 5.3610 [0.2522]StdResids: Vector hetero test: Chi(12) = 10.7119 [0.5538] F(12,74) = 0.7845 [0.6644]StdResids: Vector hetero-X test: Chi(15) = 15.2032 [0.4369] F(15,74) = 0.9280 [0.5378]PredError: Vector portmanteau (5): Chi(16) = 6.6390 [0.9796]PredError: Vector normality test: Chi(4) = 42.6151 [0.0000]PredError: Vector hetero test: Chi(12) = 24.8735 [0.0154] F(12,74) = 2.5883 [0.0063]PredError: Vector hetero-X test: Chi(15) = 28.8434 [0.0168] F(15,74) = 2.2949 [0.0099]VAR Error: Vector portmanteau (5): Chi(16) = 11.4574 [0.7804]VAR Error: Vector normality test: Chi(4) = 39.3376 [0.0000]VAR Error: Vector hetero test: Chi(12) = 38.1563 [0.0001], F(12,74) = 4.4077 [0.000]




nations, the transition probabilities for South Africa, p11 = 0.3007, p22 = 0.3165,and p33 = 0.3828, show the evidence of asymmetries in the country’s businesscycle.

For Nigeria, the MSIAH(3)–VAR(1) provided the best econometric modeling

(see results in table 9). The transition probabilities, Prob(st = 1 jst–1 = 1) = 0.6454,Prob(st = 2 jst–1 = 2) = 0.9302, and Prob(st = 3 jst–1 = 3) = 0.5167, suggest the

persistence of the moderate growth regime. The ergodic probabilities indicate that

Table 5MOROCCO: MSIA(2)–VAR(2) MODEL RESULTS


Regime 1 Regime 2

DLY DLEC DLY DLEC

Con (R.1)0.0502

(2.1027)0.0613(1.981) Con (R.2)

0.0800(1.1178)

0.0379(1.789)

DLY-1 –1.3660(–2.0012)

–0.2792(–2.2245) DLY-1

0.0069(1.9903)

0.1524(2.253)

DLY-2 –0.5329(–0.1327)

–0.3115(–2.0179) DLY-2

0.0706(0.7089)

0.5661(1.989)

DLEC-10.9676

(2.2207) –0.3502(–1.053) DLEC-1

–0.4061(2.0125)

–0.0031(–3.0128)

DLEC-20.4047

(1.9005)0.1732

(0.3054) DLEC-2 –0.3033(–3.786)

0.1066(1.1425)


Regime 1 Regime 2 ProbabilityDuration(in years)

Regime 1 0.5422 0.4578 Regime 1 0.3638 2.18Regime 2 0.2618 0.7382 Regime 2 0.6362 3.82

Log-likelihood = 134.8018, Linear system = 124.8006AIC criterion = –6.1001, Linear system = –6.2111LR linearity test = 40.0023, Chi(10) = [0.0029], Chi(12) = [0.0067], DAVIES = [0.0407]StdResids: Vector portmanteau (5): Chi(12) = 9.9319 [0.6219]StdResids: Vector normality test: Chi(4) = 36.2950 [0.000]StdResids: Vector hetero test: Chi(24) = 12.4049 [0.9750] F(24,61) = 0.3504 [0.9970]StdResids: Vector hetero-X test: Chi(42) = 25.8813 [0.9760] F(42,45) = 0.3596 [0.9994]PredError: Vector portmanteau (5): Chi(12) = 9.1543 [0.6897]PredError: Vector normality test: Chi(4) = 41.8366 [0.0000]PredError: Vector hetero test: Chi(24) = 23.4776 [0.4918] F(24,61) = 0.7959 [0.7272]PredError: Vector hetero-X test: Chi(42) = 37.4917 [0.6690] F(42,45) = 0.6497 [0.9193]

VAR Error: Vector portmanteau (5): Chi(12) = 9.3263 [0.6748]VAR Error: Vector normality test: Chi(4) = 35.6121 [0.0000]VAR Error: Vector hetero test: Chi(24) = 15.7027 [0.8985] F(24,61) = 0.4644 [0.9799]




T a b l e 6

S U D A N : M S I A H ( 3 ) – V

A R ( 1 ) M O D E L R E S U L T S

( E s t i m a t i o n s a m p l e 1 9 7 3 t o 2 0 1 0 )

R e g i m e

1

R e g i m e 2

R e g

i m e 3

D L Y

D L E C

D L Y

D L E C

D L Y

D L E C

C o n ( R . 1

)

– 0 . 0 3 1 0

( – 1 . 1 2 3 9 )

– 0 . 0 0 9 4

( – 2 . 0 1 2 )

C o n ( R . 2 )

0 . 0 2 6 7

( 1 3 . 3 3 0 )

0 . 0 4 0 2

( 0 . 5 5 9 )

C o n ( R . 3 )

– 0 . 0 2 3 2

( – 0 . 8 3 0 )

0 . 2 8 2 7

( 1 0 . 2 0 5 )

D L Y - 1

– 0 . 1 9 2 7

( – 0 . 1 2 5 9 )

– 0 . 0 4 4 1

( – 3 . 1 2 8 )

D L Y - 1

0 . 0 1 7 1

( 0 . 0 8 5 4 )

– 0 . 4 6 6 3

( – 6 . 4 4 0 )

D L Y - 1

0 . 5 5 4 1

( 1 9 . 9 5 3 )

– 2 . 0 0 1 1

( – 7 . 2 0 2 )

D L E C - 1

– 0 . 3 1 6 2

( – 5 . 7 8 6 1 )

0 . 1 8 3 3

( 0 . 1 4 2 5 )

D L E C - 1

0 . 6 8 7 7

( 3 . 3 7 0 )

– 0 . 1 0 6 7

( – 1 . 4 8 0 )

D L E C - 1

0 . 5 6 7 5

( 2 . 0 4 6 )

– 0 . 9 4 8 1

( – 1 . 4 0 2 )

T r a n s i t i o

n p r o b a b i l i t i e s R e g i m e 1

R e g i m e 2

R e g i m e 3

P

r o b a b i l i t i e s

D u r a t i o n

( i n y e a r s )

R e g i m e 1

0 . 5 9 6 2

0 . 4 0 2 8

0 . 0 0 0 9 5

R e g i m e 1

0 . 2 3 5 0

2 . 4 8

R e g i m e 2

0 . 1 0 0 0

0 . 7 4 4 5

0 . 1 5 5 5

R e g i m e 2

0 . 4 6 6 6

3 . 9 1

R e g i m e 3

0 . 3 1 7 9

0 . 0 8 2 4

0 . 5 9 9 6

R e g i m e 3

0 . 2 9 8 4

2 . 5 0

L o g - l i k e l i h o o d = 1 0 2 . 5 4 6 8 , L i n e a r s y s t e

m = 8 3 . 8 5 5 7 ; A I C c r i t e r i o n = –

3 . 7 5 9 3 , L i n e a r s y s t e m = – 4 . 0 4 6 3

L R l i n e a r i t y t e s t = 3 7 . 3 8 2 3 , C h i ( 1 8 ) = [ 0 . 0 0 4 7 ] , C h i ( 2 4 ) = [ 0 . 0 4 0 1 ] , D A

V I E S = [ 0 . 1 1 0 1 ] ; S t d R e s i d s : V

e c t o r p o r t m a n t e a u ( 5 ) :

C h i ( 1 6 ) = 2 5 . 0 3 4 8 [ 0 . 0 6 9 2 ]

S t d R e s i d

s : V e c t o r n o r m a l i t y t e s t : C h i ( 4 )

= 1 . 5 4 8 7 [ 0 . 8 1 8 1 ] ; S t d R e s i d s : V

e c t o r h e t e r o t e s t : C h i ( 1 2 ) = 4 . 8 8

2 2 [ 0 . 9 6 1 8 ] F ( 1 2 , 7 4 ) =

0 . 3 2 9 9 [ 0 . 9 8 1 3 ]

S t d R e s i d

s : V e c t o r h e t e r o - X t e s t : C h i ( 1 5 ) = 5 . 9 9 2 4 [ 0 . 9 7 9 9 ] F ( 1 5 , 7 4 ) =

0 . 3 1 8 1 [ 0 . 9 9 1 9 ]

P r e d E r r o

r : V e c t o r p o r t m a n t e a u ( 5 ) : C h i (

1 6 ) = 2 4 . 2 1 8 7 [ 0 . 0 8 4 8 ] ; P r e d E r r o r : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 6 . 7 8 9 5 [ 0 . 1 4 7 4 ]

P r e d E r r o

r : V e c t o r h e t e r o t e s t : C h i ( 1 2 ) =

1 1 . 9 2 4 8 [ 0 . 4 5 1 7 ] F ( 1 2 , 7 4 ) = 0 . 8 8 8 2 [ 0 . 5 6 2 4 ]

P r e d E r r o

r : V e c t o r h e t e r o - X t e s t : C h i ( 1 5 ) = 1 6 . 8 3 6 5 [ 0 . 3 2 8 7 ] F ( 1 5 , 7 4 ) =

1 . 0 3 8 7 [ 0 . 4 2 7 2 ]

V A R E r r

o r : V e c t o r p o r t m a n t e a u ( 5 ) : C h i ( 1 6 ) = 2 0 . 1 6 5 1 [ 0 . 2 1 2 9 ] ; V A R E r r o r : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 1 1 . 9 0 9 5 [ 0 . 0 1 8 0 ]

V A R E r r o r : V e c t o r h e t e r o t e s t : C h i ( 1 2 )

= 9 . 1 9 5 3 [ 0 . 6 8 6 2 ] F ( 1 2 , 7 4 ) = 0

. 6 7 8 4 [ 0 . 7 6 6 7 ]




T a b l e 7

T U N I S I A : M S I A ( 3 ) – V

A R ( 1 ) M O D E L R E S U L T S


R e g i m e 1

R e g i m e 2

R e g

i m e 3

D L Y

D L E C

D L Y

D L E C

D L Y

D L E C

C o n ( R . 1

)

0 . 0 2 2 2

( 1 . 9 4 7 )

0 . 0 3 5 1

( 1 . 1 2 5 )

C o n ( R . 2 )

0 . 0 6 1 1

( 5 . 3 6 0 )

0 . 0 3 0 3

( 0 . 9 7 3 )

C o n ( R . 3 )

0 . 0 2 1 5

( 1 . 8 8 8 )

0 . 1 7 2 6

( 2 . 2 2 5 )

D L Y - 1

– 0 . 6 0 0 5

( – 5 . 2 6 7 )

– 0 . 4 6 9 4

( 1 5 . 0 5 3 )

D L Y - 1

– 0 . 4 4 5

( – 3 . 9 1 2 )

– 0 . 0 2 5 7

( – 8 . 2 3 8 )

D L Y - 1

– 0 . 0 9 9 6

( – 8 . 7 0 3 )

3 . 6 1 6 7

( 1 1 . 5 9 9 )

D L E C - 1

0 . 2 4 0 8

( 2 . 1 1 2 )

0 . 1 7 6 3

( 5 . 6 5 5 )

D L E C - 1

– 0 . 0 9 4 0

( – 8 . 2 4 7 )

0 . 6 1 8 1

( 1 . 9 8 2 )

D L E C - 1

0 . 1 3 8 4

( 1 2 . 1 3 3 )

– 1 . 6 6 7 4

( – 5 . 3 4 8 )

T r a n s i t i o n p r o b a b i l i t i e s

R e g i m e 1

R e g

i m e 2

R e g i m e 3

P

r o b a b i l i t i e s

D u r a t i o n

( i n y e a r s )

R e g i m e 1

0 . 5 2 4 5

0 . 3 4 6 5

0 . 1 2 9 0

R e g i m e 1

0 . 2 4 7 7

2

. 1 0

R e g i m e 2

0 . 1 0 0 4

0 . 8 6 9 0

0 . 0 0 9 6

R e g i m e 2

0 . 6 5 8 2

7

. 6 7

R e g i m e 3

0 . 3 0 0 7

0 . 0 0 3 7

0 . 6 6 0 3

R e g i m e 3

0 . 0 9 4 1

2

. 9 4

L o g - l i k e l i h o o d = 1 7 1 . 5 9 2 8 , L i n e a r s y s t e

m = 1 4 5 . 5 8 7 5 ; A I C c r i t e r i o n =

– 7 . 8 1 5 8 , L i n e a r s y s t e m = – 7 . 3 8

3 1

L R l i n e a r i t y t e s t = 5 2 . 0 1 0 5 , C h i ( 1 2 ) = [ 0 . 0 0 0 0 ] , C h i ( 1 8 ) = [ 0 . 0 0 0 0 ] , D A

V I E S = [ 0 . 0 0 0 0 ] ; S t d R e s i d s : V


C h i ( 1 6 ) = 2 5 . 0 3 4 8 [ 0 . 0 6 9 2 ]

S t d R e s i d s : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 1 . 5 4 8 1 [ 0 . 8 1 8 1 ] ; S t d R e s i d s : V

e c t o r h e t e r o t e s t : C h i ( 1 2 ) = 4 . 8 8

2 2 [ 0 . 9 6 1 8 ] F ( 1 2 , 7 4 ) =

0 . 3 2 9 9 [ 0

. 9 8 1 3 ]

S t d R e s i d s : V e c t o r h e t e r o - X t e s t : C h i ( 1 5 ) = 5 . 9 9 2 4 [ 0 . 9 7 9 9 ] F ( 1 5 , 7 4 ) = 0 . 3 1 8 1 [ 0 . 9 9 1 9 ]

P r e d E r r o r : V e c t o r p o r t m a n t e a u ( 5 ) : C h i (

1 6 ) = 2 4 . 2 1 8 7 [ 0 . 0 8 4 8 ] ; P r e d E r

r o r : V e c t o r n o r m a l i t y t e s t : C h i (

4 ) = 6 . 7 8 9 5 [ 0 . 1 4 7 4 ]

P r e d E r r o r : V e c t o r h e t e r o t e s t : C h i ( 1 2 ) =

1 1 . 9 2 4 8 [ 0 . 4 5 1 7 ] F ( 1 2 , 7 4 ) = 0 . 8 8 8 2 [ 0 . 5 6 2 4 ]

P r e d E r r o r : V e c t o r h e t e r o - X t e s t : C h i ( 1 5 ) = 1 6 . 8 3 6 5 [ 0 . 3 2 8 7 ] F ( 1 5 , 7 4 ) =

1 . 0 3 8 7 [ 0 . 4 2 7 2 ]

V A R E r r

o r : V e c t o r p o r t m a n t e a u ( 5 ) : C h i

( 1 6 ) = 2 0 . 1 6 5 1 [ 0 . 2 1 2 9 ] ; V A R E r r o r : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 1 1 . 9 0 9 5 [ 0 . 0 1 8 0 ]

V A R E r r

o r : V e c t o r h e t e r o t e s t : C h i ( 1 2 ) = 9 . 1 9 5 3 [ 0 . 6 8 6 2 ] F ( 1 2 , 7 4 ) = 0 . 6 7 8 4 [ 0 . 7 6 6 7 ]




T a b l e 8

S O U T H A F R I C A : M S I A H ( 3 ) – V A R ( 4 ) M O D E L R E S U L T S


R e g i m e 1

R e g i m e 2

R e g i m

e 3

D L Y

D L E C

D L Y

D L E C

D L Y

D L E C

C o n ( R . 1

)

0 . 0 3 1 0

( 6 . 1 4 )

– 0 . 0 5 1 2

( – 3 . 9 1 )

C o n ( R . 2 )

0 . 0 2 5 9

( 5 . 1 2 4 )

0 . 0 1 5 9

C o n ( R . 3 )

0 . 0 2 2 4

( 0 . 4 4 3 7 )

0 . 0 3 7 5

( 2 . 8 6 )

D L Y - 1

1 . 0 0 6 6

( 1 9 . 9 3 )

– 0 . 6 5 5 5

( – 1 . 9 9 7 9 )

D L Y - 1

0 . 4 2 7 2

( 8 . 5 4 4 )

0 . 4 4 8 9

( 2 . 0 9 9 1 )

D L Y - 1

0 . 1 2 3 9

( 2 . 4 5 3 )

– 0 . 2 7 4 4

( – 2 . 0 9 8 )

D L Y - 2

– 0 . 0 2 3 3

( – 4 . 6 1 )

– 0 . 1 7 6 2

( – 2

. 8 2 6 )

D L Y - 2

0 . 2 4 6 3

( 3 . 8 7 8 9 )

0 . 1 6 3 1

( 4 . 8 7 7 )

D L Y - 2

0 . 3 0 6 4

( 6 . 0 6 3 8 )

– 0 . 6 1 9 9

( – 4 . 7 4 4 )

D L Y - 3

– 0 . 0 3 3 2

( – 6 . 5 7 )

0 . 5 5 1 0

( 4 . 2 1 4 3 )

D L Y - 3

0 . 3 2 4 6

( 6 . 4 4 5 )

– 0 . 5 2 4 3

( – 4 . 0 0 9 )

D L Y - 3

0 . 0 6 2 4

( 1 . 2 4 5 8 )

0 . 6 2 5 9

( 4 . 7 8 8 9 )

D L Y - 4

– 0 . 3 2 6 8

( – 6 . 4 7 )

– 0 . 7 2 8 4

( – 5 . 5 7 0 9 )

D L Y - 4

– 0 . 3 8 5 1

( – 7 . 6 2 7 )

– 0 . 2 1 6 8

( – 6 . 5 8 3 )

D L Y - 4

– 0 . 0 6 2 6

( – 2 . 4 0 6 )

– 0 . 5 0 9 3

( – 3 . 8 9 5 )

D L E C - 1

– 0 . 8 0 9 4

( – 1 6 . 0 3 )

1 . 2 9 3 8

( 9 . 8 9 9 5 )

D L E C - 1

– 0 . 2 3 7 0

( – 4 . 6 9 3 )

– 0 . 3 8 7 8

( – 2 . 9 6 )

D L E C - 1

0 . 0 3 2 3

( 6 . 4 0 6 2 )

0 . 3 9 2 4

( 3 . 0 0 1 5 )

D L E C - 2

0 . 1 0 4 8

( 2 . 7 5 5 )

0 . 2 6 9 4

( 2 . 0 6 0 5 )

D L E C - 2

– 0 . 2 9 3 9

( – 5 . 8 2 1 )

0 . 3 5 4 5

( 2 . 0 0 1 )

D L E C - 2

– 0 . 1 8 1 8

( – 3 . 6 0 0 )

– 0 . 2 4 6 0

( 1 . 8 8 1 3 )

D L E C - 3

0 . 3 2 6 9

( 6 . 4 7 5 )

– 0 . 1 5 9 4

( – 1

. 2 1 9 )

D L E C - 3

– 0 . 0 9 4 9

( – 1 . 8 7 9 )

0 . 3 6 0 0

( 2 . 7 5 3 )

D L E C - 3

– 0 . 0 4 9 0

( – 9 . 7 0 8 )

– 0 . 1 3 9 2

( – 1 0 . 6 4 8 )

D L E C - 4

– 0 . 3 7 6 9

( – 7 . 4 6 5 )

0 . 3 6 0 5

( 2 . 7 5 7 )

D L E C - 4

– 0 . 0 0 7 0

( – 1 . 3 7 8 )

0 . 3 1 8 4

( 2 . 4 3 3 )

D L E C - 4

0 . 2 0 1 5

( 3 . 9 9 1 )

0 . 4 3 6 2

( 3 . 3 3 6 )

( c o n t i n u e d )




T a b l e 8 ( c o n t i n u e d )

S O U T H A F R I C A : M S I A H ( 3 ) – V A R ( 4 ) M O D E L R E S U L T S


T r a n s i t i o

n p r o b a b i l i t i e s

R e g i m e 1

R e g i m e 2

R e g i m e 3

P r o b a b

i l i t i e s

D u r a t i o n ( i n y e a r s )

R e g i m e

1

0 . 5 0 2 0

0 . 4 0 5 8

0 . 0 9 2 2

R

e g i m e 1

0 . 3 0

0 7

2 . 0 1

R e g i m e

2

0 . 0 2 5

0 . 6 1 4 5

0 . 3 6 0 5

R

e g i m e 2

0 . 3 1

6 5

2 . 5 9

R e g i m e

3

0 . 3 6 1 2

0 . 0 3 0 1

0 . 6 0 8 7

R

e g i m e 3

0 . 3 8

2 8

2 . 5 6

L o g - l i k e

l i h o o d = 2 0 6 . 3 8 9 6 , L i n e a r s y s t e

m = 1 6 5 . 6 0 0 0

A I C = –

8 . 4 3 4 7 , L i n e a r s y s t e m = – 8 . 5 0 5 9

L R l i n e a

r i t y t e s t = 8 1 . 5 7 9 2 , C h i ( 3 6 ) = [

0 . 0 0 0 0 ] , C h i ( 4 2 ) = [ 0 . 0 0 0 2 ] , D A V I E S = [ 0 . 0 0 1 1 ]

S t d R e s i d

s : V e c t o r p o r t m a n t e a u ( 9 ) : C h i ( 2 0 ) = 3 2 . 5 3 0 8 [ 0 . 0 1 2 4 ]

S t d R e s i d

s : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 9 . 0 4 2 2 [ 0 . 0 6 0 1 ]

S t d R e s i d

s : V e c t o r h e t e r o t e s t : C h i ( 4 8 ) =

4 0 . 1 7 5 8 [ 0 . 7 8 1 6 ] F ( 4 8 , 2 1 ) = 0

. 3 1 2 1 [ 0 . 9 9 9 6 ]

P r e d E r r o

r : V e c t o r p o r t m a n t e a u ( 9 ) : C h i ( 2 0 ) = 2 2 . 1 4 7 6 [ 0 . 3 3 2 6 ]

P r e d E r r o


6 2 . 2 3 5 4 [ 0 . 0 8 1 3 ] F ( 4 8 , 2 1 ) = 0

. 8 1 9 7 [ 0 . 7 2 1 8 ]

P r e d E r r o

r : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 1 2 . 5 9 8 8 [ 0 . 0 1 3 4 ]

V A R E r r o r : V e c t o r p o r t m a n t e a u ( 9 ) : C h

i ( 2 0 ) = 2 3 . 3 6 4 9 [ 0 . 2 7 1 2 ]

V A R E r r o r : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 3 . 0 8 9 8 [ 0 . 5 4 2 9 ]


= 4 4 . 9 5 2 5 [ 0 . 5 9 8 5 ] F ( 4 8 , 2 1 ) =

0 . 3 7 8 0 [ 0 . 9 9 7 3 ]




the dominant regime is the second one. The transition probabilities, p11 = 0.2115, p22 = 0.6232, and p33 = 0.1654, mirror the other countries in our study with their asymmetric business cycles.

The results for the nation of Togo, for which we employed a MSIA(3)–VAR(4)

model, are presented in table 10. The first regime economy tends to last 2.60 yearson average, while regime 2—the moderate growth regime—is by far the most

persistent (8.43 years on average). Regime 3, which corresponds to the highgrowth economic scenario, has an average duration of 2.17 years. The resultssuggest a persistence of the economic regimes: Prob(st = 1 jst–1 = 1) = 0.6160,Prob(st = 2 jst–1 = 2) = 0.9426, and Prob(st = 3 jst–1 = 3) = 0.7599.

For the last country in our sample, Zimbabwe, the MSIA(2)–VAR(4) model presents the best econometric performance (see table 11 for results). In the case of Zimbabwe, Prob(st = 1 jst–1 = 1) = 0.6074 and Prob(st = 2 jst–1 = 2) = 0.5475suggest the persistence of the recessionary phase. Regime 2 has an average du-ration of 2.21 years; whereas, the average duration of the recessionary phase isslightly longer at 2.55 years.

MS-VAR and MS-Granger Causality Results: For the first country wemodeled, Algeria, we used the MSIA(2)–VAR(2) with the results presented in table3. In this model, the coefficients of electricity consumption (EC) are positive inregime 1. The estimated coefficients of electricity consumption innovations (DLEC)

in equation 1 are statistically significant at the conventional levels in the regimes. Inregime 2, the coefficients of the DLY-1 and DLY-2 for equation 2 are negative. Thedependent variable of the second equation is DLEC, that is, innovations of elec-tricity consumption. For the equation 1 in the regimes, that is, for the equation of LY, the EC appears to be the Granger cause of economic growth. Therefore, it isdetermined that Granger causality exists from EC to Y in equation 1 in both regimes1 and 2. According to the second equation obtained for the first and the second regime, which is the equation for LEC, Y appears to be the Granger cause of energyconsumption and the direction of causality is from LY to EC for equation 2. In

summation, we found some evidence of bi-directional Granger causality betweenenergy consumption and GDP in the recession and growth periods for Algeria.For Egypt, the MSIA(3)–VAR(1) model presented the best econometric per-

formance with the results and all of the coefficients being statistically signifi-cant at the conventional levels as can be seen in table 4. The coefficient of thedistributed-lag component of the EC variable is statistically significant at con-ventional levels. The estimated coefficients of GDP innovations (DLY) in equa-tion 2 are statistically significant and positive for all three regimes. The estimated coefficients of electricity consumption innovations (DLEC) in equation 1 are

statistically significant at the conventional level in all regimes with the coeffi-cients of DLEC for regime 2 being negative. The dependent variable of the second equation in all regimes is DLEC, which represents innovations of electricity




T a b l e 9

N I G E R I A : M S I A H ( 3 ) – V A R ( 1 ) M O D E L R E S U L T S


R e g i m e 1

R e g i m e 2

R e g i m e

3

D L Y

D L

E C

D L Y

D L E C

D L Y

D L E C

C o n ( R

. 1 )

– 0 . 0 5 4 2

( – 1 . 1 4 3 6 )

0 . 0

9 6 6

( 0 . 5

1 9 5 )

C o n ( R . 2 )

0 . 0 0 4 5

( 0 . 2 5 9 1 )

0 . 0 0 3 7

( 0 . 5 1 0 )

C

o n ( R . 3 )

0 . 0 1 7 5

( 0 . 5 7 4 )

0 . 1 9 4 4

( 1 . 5 9 6 3 )

D L Y - 1

– 0 . 6 8 0 2

( – 3 . 9 6 5 )

1 . 4

1 1 5

( 7 . 5

9 2 8 )

D L Y - 1

0 . 6 5 1 6

(

3 . 8 3 2 )

1 . 8 8 4 5

( 2 . 5 4 6 )

D L Y - 1

– 0 . 0 4 4 4

( – 1 . 4 6 4 )

– 0 . 8 2 9 1

( – 6 . 7 9 6 )

D L E C

- 1

– 0 . 1 3 9 2

( – 2 . 9 4 2 1 )

– 0 . 4 5 3 8

( – 0 . 2 4 5 2 )

D L E C - 1

–

0 . 0 3 0 2

( – 7 . 5 4 3 )

– 0 . 5 3 6 5

( – 1 . 7 2 4 )

D L E C - 1

0 . 1 0 8 4

( 2 . 4 5 8 7 )

– 0 . 3 9 4 1

( – 2 . 7 8 5 )

T r a n s i t i o


R e g i m e 1

R e g i m e 2

R e g i m e 3

P r o b a b i l i t i e s

D u r a t i o n ( i n

y e a r s )

R e g i m e 1

0 . 6 4 5 4

0 . 0

0 1 5

0 . 3 5 3 1

R e g i m e 1

0 . 2 1 1 5

2 . 5 5

R e g i m e 2

0 . 0 1 0 0

0 . 9

3 0 2

0 . 0 4 9 8

R e g i m e 2

0 . 6 2 3 2

8 . 3 2

R e g i m e 3

0 . 3 7 5 4

0 . 1

0 8 0

0 . 5 1 6 7

R e g i m e 3

0 . 1 6 5 4

1 . 4 6

L o g - l i k e

l i h o o d = 1 1 3 . 8 1 2 7 , L i n e a r s y s t e

m = 7 5 . 3 0 4 1 ; A I C c r i t e r i o n = – 4 . 3 6 8 3 , L i n e a r s y s t e m = – 3 . 5 8 4 0

L R l i n e a

r i t y t e s t = 7 7 . 0 1 7 3 , C h i ( 1 8 ) = [ 0 . 0 0 0 0 ] , C h i ( 2 4 ) = [ 0 . 0 0 0 0 ] , D A V I E S = [ 0 . 0 0 0 0 ] ; S t d R e s i d s : V


C h i ( 1 6 ) = 1 8 . 4 5 0 8 [ 0 . 2 9 8 2 ]

S t d R e s i d

s : V e c t o r n o r m a l i t y t e s t : C h i ( 4 )

= 5 . 3 9 4 0 [ 0 . 2 4 9 2 ] ; S t d R e s i d s : V

e c t o r h e t e r o t e s t : C h i ( 1 2 ) = 1 1 . 2 6 6 6 [ 0 . 5 0 6 2 ] F ( 1 2 , 7 4 ) =

0 . 8 2 5 4 [ 0 . 6 2 4 0 ]

S t d R e s i d

s : V e c t o r h e t e r o - X t e s t : C h i ( 1 5

) = 1 7 . 2 9 7 3 [ 0 . 3 0 1 4 ] F ( 1 5 , 7 4 ) = 1 . 1 0 1 9 [ 0 . 3 7 0 0 ]

P r e d E r r o

r : V e c t o r p o r t m a n t e a u ( 5 ) : C h i ( 1 6 ) = 2 2 . 0 0 7 9 [ 0 . 1 4 2 9 ] ; P r e d E r r o r : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 8 . 6 7 9 1 [ 0 . 0 6 9 6 ]

P r e d E r r o


1 4 . 3 5 0 1 [ 0 . 2 7 8 9 ] F ( 1 2 , 7 4 ) = 1

. 0 8 6 1 [ 0 . 3 8 4 2 ]

P r e d E r r o

r : V e c t o r h e t e r o - X t e s t : C h i ( 1 5 ) = 1 4 . 5 4 9 0 [ 0 . 4 8 4 4 ] F ( 1 5 , 7 4 ) =

0 . 8 4 9 2 [ 0 . 6 2 1 3 ]

V A R E r r o r : V e c t o r p o r t m a n t e a u ( 5 ) : C h

i ( 1 6 ) = 2 5 . 0 1 7 5 [ 0 . 0 6 9 5 ] ; V A R

E r r o r : V e c t o r n o r m a l i t y t e s t : C

h i ( 4 ) = 7 . 4 0 8 2 [ 0 . 1 1 5 8 ]


= 2 1 . 9 9 3 1 [ 0 . 0 3 7 6 ] F ( 1 2 , 7 4 ) =

1 . 9 6 5 0 [ 0 . 0 3 9 8 ]




T a

b l e 1 0

T O G O : M S I A ( 3 ) – V A

R ( 4 ) M O D E L R E S U L T S


R e g i m e 1

R e g i m e 2

R e g i m e

3

D L Y

D L E

C

D

L Y

D L E C

D L Y

D L E C

C o n ( R . 1 )

– 0 . 0 3 5 9

( 2 . 7 7 9 )

0 . 0 1

0 8

( 0 . 3 7 5 )

C o n ( R . 2 )

– 0

. 0 1 2 2

( 1

. 0 0 1 )

0 . 0 3 8 7

( 1 . 3 5 7 )

C o n ( R . 3 )

– 0 . 0 3 3 0

( – 2 . 5 3 8 )

0 . 1 6 4 5

( 5 . 7 1 4 )

D L Y - 1

0 . 1 5 5 7

( 1 . 2 0 6 )

– 0 . 9 8 6 1

( 3 . 0 0 1 )

D L Y - 1

– 0

. 1 0 7 0

( – 8 . 2 2 4 )

0 . 0 2 3 4

( 0 . 8 1 6 )

D L Y - 1

– 0 . 2 5 5 5

( – 2 . 0 0 1 )

– 2 . 1 9 8 8

( – 7 . 5 8 2 )

D L Y - 2

0 . 5 8 2 5

( 4 . 8 3 )

– 1 . 0 1 8 3

( – 3 . 6

3 6 )

D L Y - 2

0 . 0 3 4 5

( 2 . 6 5 )

0 . 2 4 0 5

( 2 . 6 7 )

D L Y - 2

– 0 . 4 5 5 0

( – 3 . 5 0 7 )

0 . 7 4 1 2

( 2 . 5 5 5 )

D L Y - 3

1 . 3 2 2 7

( 1 0 . 2 8 5 )

5 . 5 9

7 1

( 5 . 9 0 1 )

D L Y - 3

– 0

. 2 7 9 4

( – 2 . 1 4 6 )

– 0 . 9 7 0 5

( – 3 . 3 8 1 )

D L Y - 3

– 0 . 4 9 3 3

( 3 . 8 0 8 )

– 1 . 0 1 8 3

( – 3 . 5 4 6 )

D L Y - 4

– 0 . 2 1 8 4

( – 1 . 8 9 1 )

3 . 4 3

5 1

( 1 1 . 9

8 6 )

D L Y - 4

0 . 2 0 0 4

( 2

. 0 9 9 )

0 . 0 5 6 9

( 1 . 8 9 6 )

D L Y - 4

– 0 . 9 9 9 7

( – 8 . 3 2 5 )

0 . 5 3 5 2

( 1 8 . 8 1 8 )

D L E C - 1

– 0 . 3 1 1 2

( 2 . 6 0 0 )

– 0 . 5 5 3 0

( – 0 . 1

2 6 )

D L E C - 1

0 . 1 3 6 1

( 1

0 . 7 9 )

– 0 . 1 4 6 2

( – 5 . 2 6 9 )

D

L E C - 1

0 . 0 6 3 7

( 4 . 8 8 7 )

0 . 6 9 4 5

( 2 . 4 1 3 )

D L E C - 2

0 . 0 5 4 8 4

( 4 . 5 7 )

0 . 0 4 3 1 3

( 1 . 5 3 5 )

D L E C - 2

– 0

. 1 3 1 8

( – 1 0 . 7 6 )

– 0 . 0 1 5 4

( – 5 . 3 4 )

D

L E C - 2

0 . 2 7 5 2

( 2 . 7 5 6 )

– 0 . 8 4 5 4

( - 2 . 2 4 5 8 )

D L E C - 3

– 0 . 2 4 8 0

( – 1 . 9 2 3 )

– 1 . 2 3 4 7

( – 4 . 0

9 6 )

D L E C - 3

0 . 0 3 1 4

( 0

. 2 4 6 )

0 . 0 8 9 0

( 3 . 0 6 8 )

D

L E C - 3

1 . 3 5 7 1

( 1 0 . 4 6 9 )

0 . 7 0 0 5

( 2 . 1 4 2 )

D L E C - 4

– 0 . 2 0 6 3

( – 1 7 . 1 8 0 )

– 0 . 8 6 8 4

( – 1 . 2

8 3 )

D L E C - 4

0 . 7 1 7 3

( 5

. 5 5 4 )

– 0 . 1 7 5 0

( – 6 . 0 6 9 )

D

L E C - 4

0 . 3 2 1 8

( 1 . 2 4 6 )

– 1 . 0 1 0

( – 0 . 7 9 0 )





T a b l e 1 0


T O G O : M S I A ( 3 ) – V A R

( 4 ) M O D E L R E S U L T S


T r a n s i t i o


R e g i m e 1

R e g i m

e 2

R e g i m e 3

P r o b a b i l i t i e s

D u r a t i o n ( i n

y e a r s )

R e g i m e

1

0 . 6 1 6 0

0 . 2 4

3 8

0 . 1 4 0 2

R e g i m e 1

0 . 1 9 0 9

2 . 6 0

R e g i m e

2

0 . 0 3 7 4

0 . 9 4

2 6

0 . 0 1 0 0

R e g i m e 2

0 . 6 0 9 3

8 . 4 3

R e g i m e

3

0 . 2 3 8 1

0 . 0 0

2 0

0 . 7 5 9 9

R e g i m e 3

0 . 1 9 9 8

2 . 1 7

L o g - l i k e

l i h o o d = 1 6 3 . 0 7 0 1 , L i n e a r s y s t e m = 8 2 . 2 2 9 3

A I C = – 5 . 8 8 6 5 , L i n e a r s y s t e m = – 3 . 6 0 1

7

L R l i n e a

r i t y t e s t = 1 6 1 . 6 8 1 5 , C h i ( 3 6 ) =

[ 0 . 0 0 0 0 ] , C h i ( 4 2 ) = [ 0 . 0 0 0 0 ] , D

A V I E S = [ 0 . 0 0 0 0 ]

S t d R e s i d

s : V e c t o r p o r t m a n t e a u ( 9 ) : C h i ( 2 0 ) = 2 4 . 9 2 5 9 [ 0 . 2 0 4 3 ]

S t d R e s i d

s : V e c t o r n o r m a l i t y t e s t : C h i ( 4 ) = 1 7 . 8 4 7 8 [ 0 . 0 0 1 3 ]

S t d R e s i d

s : V e c t o r h e t e r o t e s t : C h i ( 4 8 ) =

4 3 . 9 1 7 2 [ 0 . 6 4 0 8 ] F ( 4 8 , 2 1 ) = 0

. 3 6 9 0 [ 0 . 9 9 7 8 ]

S t d R e s i d

s : V e c t o r h e t e r o - X t e s t : C h i ( 9 9 ) = 1 0 2 . 0 0 0 0 [ 0 . 3 9 8 1 ] F ( 9 9 , – 2 9 ) = – 0 . 1 6 9 2 [ 0 . 0 0 0 0 ]

P r e d E r r o

r s : V e c t o r p o r t m a n t e a u ( 9 ) : C h

i ( 2 0 ) = 2 9 . 5 3 9 2 [ 0 . 0 7 7 7 ]

P r e d E r r o

r s : V e c t o r n o r m a l i t y t e s t : C h i ( 4

) = 0 . 8 0 4 0 [ 0 . 9 3 7 9 ]

P r e d E r r o

r s : V e c t o r h e t e r o t e s t : C h i ( 4 8 ) = 5 9 . 6 2 1 3 [ 0 . 1 2 1 2 ] F ( 4 8 , 2 1 ) = 0 . 6 5 6 9 [ 0 . 8 7 6 4 ]

P r e d E r r o

r s : V e c t o r h e t e r o X - t e s t : C h i ( 9

6 ) = 9 9 . 0 0 0 0 [ 0 . 3 9 6 5 ] F ( 9 6 , – 2 9 ) = 0 . 3 0 2 5 [ 0 . 0 0 0 0 ]

V A R E r r o r s : V e c t o r p o r t m a n t e a u ( 9 ) : C

h i ( 2 0 ) = 2 6 . 0 6 6 5 [ 0 . 1 6 3 6 ]

V A R E r r o r s : V e c t o r n o r m a l i t y t e s t : C h

i ( 4 ) = 1 2 . 2 1 7 2 [ 0 . 0 1 5 8 ]

V A R E r r o r s : V e c t o r h e t e r o t e s t : C h i ( 4 8 ) = 3 9 . 9 6 4 9 [ 0 . 7 8 8 7 ] F ( 4 8 , 1 8 ) =

0 . 2 7 9 3 [ 0 . 9 9 9 8 ]




Table 11ZIMBABWE: MSIA(2)–VAR(4) MODEL RESULTS


Regime 1 Regime 2

DLY DLEC DLY DLEC

Con (R.1) –0.0259(–0.987)

0.0115(0.405) Con (R.2)

0.0298(1.142)

0.0315(1.523)

DLY-1 –0.2210(–8.25)

0.5190(8.025) DLY-1

0.3813(4.148)

–0.7397(–2.645)

DLY-20.2656(7.245)

–0.1714(–4.652) DLY-2

–0.0341(–1.311)

–0.7416(–3.52)

DLY-3 –0.0452(–6.426)

–0.0108(–0.386) DLY-3

0.4219(6.264)

–0.2393(–8.356)

DLY-40.4428(1.666)

–0.3020(5.145) DLY-4

–0.3659(–5.012)

0.0270(0.936)

DLEC-10.1480(5.552)

–0.1337(3.642) DLEC-1

0.7507(2.620)

–0.0466(–2.238)

DLEC-21.2878(4.067)

–0.1997(–0.135) DLEC-2

–0.8817(–3.085)

0.7145(2.536)

DLEC-3

–0.2907

(–1.536)

–0.0110

(–0.824) DLEC-3

–0.4088

(–2.790)

–0.4427

(–1.557)

DLEC-4 –0.8853(–3.826)

0.1206(4.307) DLEC-4

–0.0516(–1.907)

0.0494(1.689)


Regime 1 Regime 2 Probabilities Duration (in years)Regime 1 0.6074 0.3926 Regime 1 0.5355 2.55Regime 2 0.4525 0.5475 Regime 2 0.4645 2.21

Log-likelihood = 137.9320, Linear system = 114.4792AIC = – 5.8747, Linear system = –5.6654

LR linearity test = 46.9054, Chi(18) = [0.0002], Chi(20) = [0.0006], DAVIES = [0.0072]StdResids: Vector portmanteau (9): Chi(20) = 22.4051 [0.3189]StdResids: Vector normality test: Chi(4) = 14.6649 [0.0054]StdResids: Vector hetero test: Chi(48) = 36.4280 [0.8892] F(48,18) = 0.2444 [1.000]StdResids: Vector hetero-X test: Chi(96)= 99.0000 [0.3965] F(96,–29)= –0.0996 [0.0000]PredError : Vector portmanteau (9): Chi(20) = 22.1476 [0.3326]PredError: Vector normality test: Chi(4) = 12.5988 [0.0134]PredError: Vector hetero test: Chi(48) = 62.2354 [0.0813] F(48,21) = 0.8197 [0.7218]VAR Error: Vector portmanteau (9): Chi(20) = 23.3649 [0.2712]VAR Error: Vector normality test: Chi(4) = 3.0898 [0.5429]VAR Error: Vector hetero test: Chi(48) = 44.9525 [0.5985] F(48,21) = 0.3780 [0.9973]




consumption. In the first equation for the first, second and third regime—that is for the equation of LY—the EC appears to be the Granger cause of economic growth.It is determined that Granger causality exists from EC to LY for equation 1 inregimes 1, 2, and 3. According to the second equation obtained for the first,

second, and third regime, that is, the equation for LEC, Y appears to be theGranger cause of energy consumption and the direction of causality is from LY toEC for equation 2. To summarize our findings of causality for Egypt, we found some evidence of bi-directional Granger causality between energy consump-tion and GDP in a recession, moderate growth, and high growth periods of theeconomy.

The MSIA(2)–VAR(2) model offered important results for Morocco (see table5). The estimated coefficients of electricity consumption innovations (DLEC) aresignificant for the first and second regime. According to the first equation of thefirst and second regime, that is, the equation for LY, the EC appears to be theGranger cause of GDP; regarding the second equation of the first and second regime—i.e., the equation for LEC—the LY appears to be the Granger cause of electricity consumption. Thus, we found some evidence of bi-directional Granger causality between electricity consumption and LY in the recession and growthregimes in the case of Morocco.

For the country of Sudan, the MSIAH(3)–VAR(1) model was determined to provide the best econometric results, which are available in table 6. Regime 1

approximates the recessionary dates. Regimes 2 and 3 show the moderate and highgrowth regimes, respectively. The estimated coefficients of electricity consump-tion innovations (DLEC) in equation 1 are statistically significant at convetionallevels under all three regimes. According to first equation of the first, second, and third regime, which is the equation for LY, the EC appears to be the Granger causeof Y. In the instance of the second equation (that is, the equation for LEC), the Yappears to be the Granger cause of electricity consumption in the first, second, and third regime.

We established that the MSIA(3)–VAR(1) model presented the best econo-

metric performance for the nation of Tunisia (see table 7). The estimated co-efficients of EC innovations (DLEC) were significant in the first, second, and third regimes. According to first equation of the first, second, and third regimes—thatis, the equation for LY—the EC appears to be the Granger cause of GDP; in thecase of the second equation of all three regimes—i.e., the equation for LEC—theGDP appears to be the Granger cause of electricity consumption.

For South Africa, a MSIAH(3)–VAR(4) model was utilized with the resultsshown in table 8. The estimated coefficients of electricty consumption innovations(DLEC) and economic growth innovations (DLY) are statistically significant at

conventional levels in the regimes; however, the estimated coefficients of theDLEC-3 in equation 2 in regime 1 and the DLEC-4 in equation 1 in regime 2 and the DLEC-2 in equation 2 in regime 3 are not. In equations 1 and 2 of the regimes,




the estimated coefficients of electricity consumption and Y are statistically sig-nificant at conventional levels. According to the first equation—the equation for LY—the EC appears to be the Granger cause of GDP in the regimes, and in thesecond equation (which is the equation for LEC) the GDP appears to be the

Granger cause of electricity consumption for the regimes. To sum up the results,there is bi-directional Granger causality between electricity consumption and theGDP in the regimes for South Africa.

For Nigeria, we established that the MSIAH(3)–VAR(1) model offered the besteconometric performance with the results shown in table 9. The estimated co-efficients of GDP innovations (DLY) were found to be statistically significant for all three regimes. Moreover, the estimated coefficients of electricty consumptioninnovations (DLEC) are statistically significant at a conventional level in all re-gimes. However, the DLEC(-1) in equation 2 in both regimes 1 and 2, and theDLY(-1) in equation 1 in regime 3 are statistically insignificant at conventionallevels. We found bi-directional causality in the Nigeria analysis; that is to say,Granger causality appears to exist from DLEC toward GDP for equation 1 inall regimes and Granger causality exists from Y toward DLEC for equation 2 inall regimes. To conclude, some evidence of bi-directional Granger causality wasfound between electricity consumption and GDP in the first, second, and third regimes in our model of Nigeria.

The MSIA(3)–VAR(4) model offers important insights for Togo (table 10).

The estimated coefficients of economic growth innovations (DLY) and electrictyconsumption innovations (DLEC) are statistically significant in equations 1 and 2in the regimes with the exception of DLY-1 for equation 1 and DLEC-1, DLEC-2,and DLEC-4 for equation 2 in regime 1; DLEC-3 for equation 1 and DLY-1 for equation 2 in regime 2; and DLEC-4 for equation 1 and 2 in regime 3. Accordingto the first equation of the first, second, and third regime, which corresponds to theequation for DLY, the EC appears to be the Granger cause of GDP; in the second equation of the first, second, and third regime, the Y appears to be the Granger cause of electricity consumption. Therefore, we found some evidence of bi-

directional Granger causality between electricity consumption and GDP in therecession, moderate growth, and high growth regimes for Togo.In table 11 we provide the results of the MSIA(2)–VAR(4) model for Zim-

babwe. In the case of the first and second regimes, the first equation (denoted asthe equation of LY) implies that the EC appears to be the Granger cause of eco-nomic growth. It is determined that Granger causality exists from EC to Y inequation 1 in regimes 1 and 2. According to the second equation obtained for thefirst and the second regimes, that is, the equation for LEC, Y appears to be theGranger 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 cau-sality between energy consumption and GDP in the recession and growth periodsfor the nation of Zimbabwe.




Traditional Granger Causality Results: For comparison, we modeled thesame data sets using standard Granger causality tests for the nine African states. Theresults are reported in table 12. In the short-run causality analysis, there is evidenceto support the growth hypothesis for Algeria. There is a uni-directional relationship

from electricity consumption to real GDP, which means that electricity consump-tion acts as a stimulus to economic growth. Furthermore, there is evidence tosupport the conservation hypothesis for Morocco, Togo, Zimbabwe, and Tunisia.Bi-directional causality was confirmed for Egypt, Nigeria, South Africa, and Sudan.

Conclusion

In this study, we used MS-Granger causality testing to examine the causalrelationship between electricity consumption and the real GDP. Furthermore, we

were able to detect changes in the behavior of the variables through MS-VAR modeling. MS-VAR analysis was used to examine the MS-Granger causality

between electricity consumption and economic growth. The dependent variable inthe first equation is the innovation of economic growth, i.e., DLY. For all

Table 12RESULTS OF TRADITIONAL GRANGER CAUSALITY TESTS

DEC ! DY

DY ! DEC

Country F-statistic for SR-GC

247.49Algeria 2.9915

85.2648Egypt 34.68

0.27365Morocco 249.426

25.142

Sudan 174.95

0.7924Tunisia 292.5159

72.5018 Nigeria 59.3389

306.8788South Africa 16.6168

0.3526Togo 66.145

2.2590Zimbabwe 184.594




countries, electricity consumption is the Granger cause of the economic growth.According to the second equation, economic growth appears to be the Granger cause of electricity consumption in the regimes. In summation, some evidence wasfound of bi-directional Granger causality between electricity consumption and

economic growth for the nine countries we analyzed.The results highlight the importance of electricity policy on economic growth,

economic development, and welfare. Therefore, an extremely important factor inexplaining low levels of economic growth in some African nations is the lack of investments in energy infrastructure and services. Thus, the current low levels of investment in energy infrastructure may be an obstacle that may prevent someAfrican countries from more rapid economic growth. The energy-related problemsare crucial policy issues for African states. The current energy policy and theelectricity-sector restructuring process should be designed to address this goal.The energy policies aimed at improving the energy infrastructure, within thecontext of our findings regarding the MS-VAR approach, suggest that increasingthe energy supply remains an appropriate option for economic growth.

NOTES

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16The transition probability matrix is ergodic and cannot be reduced because the maximumEigen values of the matrix of transition probabilities related to the MS-VAR models is one and theother two Eigen values are less than one; thus, the transition probability matrix is ergodic and cannot be reduced.


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