AN INVESTIGATION OF THE ENERGY CONSUMPTION-GROWTH NEXUS IN

AN INVESTIGATION OF THE ENERGY CONSUMPTION-GROWTH NEXUS IN

AN INVESTIGATION OF THE ENERGY CONSUMPTION-GROWTH NEXUS IN THE ARAB COUNTRIES Leila Dagher American University of Beirut National Renewable Energy Lab, Research Fellow MOTIVATION & POLICY IMPLICATIONS (1) 1. E Y growth hypothesis 2. Y E conservation hypothesis 3. E Y feedback hypothesis 4. E Y neutrality hypothesis MOTIVATION & POLICY IMPLICATIONS (2) Conflicting results GHG mitigation activities: low ghg emissions but high on emissions per capita New energy conservation targets: NEEAP (e.g.

Egypt 5% reduction in electricity consumption 2012-2015) External oil shocks Electricity rationing LITERATURE REVIEW (1) Kraft and Kraft (1978) 100s of studies Renewed interest (climate change and mitigation) Variables used Bivariate versus multivariate models Kuznets curve framework Conflicting results LITERATURE REVIEW (2) Study Algeria Egypt

Morocco Akinlo 2008 Al-Iriani 2006 Oman SA YE YE Sudan Syria E---Y (SR) EY (LR) Apergis and NGY (SR NGY (SR NGY (SR

Payne 2010a & LR) & LR) & LR) Apergis and Payne 2010b Belloumi 2009 Chontanawat et al. 2006 Y E Dagher and Yacoubian 2012 NGY (SR NGY (SR & LR) & LR) CY (SR & CY (SR & LR) LR) E Y (SR)

EY (LR) EY EY EY YE EY (SR) p:Y E (LR) Mahadevan and AsafuAdjaye 2007 Tunisia i:EY (LR) EY EY

LITERATURE REVIEW (3) Study Algeria Egypt Morocco Oman Mehrara 2007a Mehrara 2007b SA E Y (SR) Sudan

Syria Tunisia E Y (LR) Y E (SR & LR) Y E (SR & Y E (SR & LR) LR) Narayan and Smyth 2009 EL Y (SR) EL Y (SR) ELY (LR) ELY (LR) Narayan et al.

2010 ELY ELY ELY Y EL Ozturk et al. EY (SR & 2010 LR) Sharma 2010 E Y EY (SR & LR) EY EY (SR & LR) EY

EY (SR & LR) Wolde-Rufael E---Y 2006 ELY ELY Wlde-Rufael 2009 EY YE YE Y EL

EL Y (SR) ELY (LR) ELY Y E (SR) EY (LR) EY E---Y YE Y EL ELY EY (SR & LR) EY EY (SR &

LR) EY EL Y YE METHODOLOGY (1) Unit root testing (ADF or IPS) Testing for cointegrating relationship (Johansen or Pedroni) Granger causality METHODOLOGY (2) if y t causes x t , then x t + 1 is better forecast if the information in y t - j is used than if it is not used. (Granger, 1988) Major drawbacks: Series should be stationary Results are sensitive to lag length METHODOLOGY (3)

Hsiao test uses Akaikes Final Prediction Error Compare FPE(n,0) to FPE(n,m) Compare FPE(s,0) to FPE(s,r) Strengths: valid whether series are stationary or not METHODOLOGY (4) Toda and Yamamoto: modified Wald test on an augmented VAR METHODOLOGY (5) Estimator: seemingly unrelated regression (SUR) procedure Wald test ignoring m Strengths: valid whether series are stationary or not Avoids biases relating to pretesting Size is acceptable Drawbacks: inefficient due to overfitting METHODOLOGY (6)

Granger on VECM METHODOLOGY (7) Panel causality tests Holtz-Eakin et al., 1988 Dumitrescu and Hurlin, 2012 Very good small sample properties Can be used with unbalanced panels and different lag orders METHODOLOGY (8) DATA (1) Panel of 9 countries: Algeria, Egypt, Mauritania, Morocco, Oman, Saudi Arabia, Sudan, Syria, and Tunisia. N=9, T=41 (1970-2010) E from OAPEC and Y from WDI

DATA (2) ENERGY CONSUMPTION 9 8 7 6 5 4 3 2 1 0 1970 1975 1980 1985 ALGERIA

MOROCCO SUDAN 1990 EGYPT OMAN SYRIA 1995 2000 2005 MAURITANIA SAUDI TUNISIA 2010 DATA (3)

REAL GDP 29 28 27 26 25 24 23 22 21 20 1970 1975 1980 1985 ALGERIA

MOROCCO SUDAN 1990 EGYPT OMAN SYRIA 1995 2000 2005 MAURITANIA SAUDI TUNISIA 2010 RESULTS (TIME SERIES)

Algeria Egypt Mauritania Morocco Oman Saudi Arabia Sudan Syria Tunisia E---Y YE E---Y E---Y YE EY EY E---Y EY

RESULTS (PANEL) E is I(0) and Y is I(1) Dumitrescu-Hurlin: no causality in either direction

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