Modeling The Causality Relationshıps Between Gdp and Electricity Consumption According to Income Levels of Countrıes by Generalized Estimation Equations

Harun Yonar, Neslihan İyit


Gross domestic product (GDP) and energy consumption in the economic evaluations of countries are seen as two basic concepts of development. The need for energy resources in recent years has brought countries closer to technology, but in some cases, it causes problems such as wars. It is also important to determine the economic feasibility of energy consumption as well as the feasibility of many aspects such as the origin, usage, and necessity of energy. When we look at the crises that have taken place in the last 20 years, it is once again seen that energy is the dynamism and indispensable necessity of the countries. If we look at the effect of the consumed energy on the country's economy, the first economic variable will be GDP. Interpretation and evaluation of GDP, which reveals steady growth, will give effective results on economic indicators of the country. A lot of research has been done in the literature between the amount of energy consumption (according to the sectors, type of energy used, supply, and etc.) and the GDP which is the most important indicator of the country's economy. The final relationship between these two variables has been examined in details for different countries and energy concepts. In previous studies, it is sometimes observed that energy consumption is a cause of GDP or vice versa, and sometimes a two-way causality between them is determined. On the other hand, a causality relationship can not be always determined between the variables. In this case, a suitable regression model can be established without looking for causality.

In this study, the causality relationship between the GDP values, categorized by five income levels, and the energy consumptions of the countries between 1980 and 2014 is determined by using the Granger causality test. When we look at the results of the causality test, we find that only one causality relationship exists between high income level countries by GDP and the energy consumption of them. According to the causality test result, dependent and independent variable are determined before generalized estimating equations (GEE) method is used for modelling the data. In GEE method, the smallest values of QIC and QICC information criteria are found in the direction of causality relationships. The same causality assessment is done between gross national incomes (GNI) of countries categorized by income levels and energy consumptions, and it is concluded that the GEE models established according to the causality relationship direction are much better fit to the data.  This finding obtained from this study suggests that causality test is a guide for us when we have insufficient knowledge in determining dependent and independent variables before fitting regression models to the data.

Anahtar Kelimeler

Gross Domestic Product; Energy Consumption; Granger Causality Test; Generalized Estimating Equations

Tam Metin:



Agresti, A. (2015). Foundations of linear and generalized linear models: John Wiley & Sons.

Aydın, F. F. (2010). Enerji tüketimi ve ekonomik büyüme. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi(35), 317-340.

Chiou-Wei, S. Z., Chen, C.-F., & Zhu, Z. (2008). Economic growth and energy consumption revisited—evidence from linear and nonlinear Granger causality. Energy economics, 30(6), 3063-3076.

Davis, C. S. (2002). Statistical methods for the analysis of repeated measurements: Springer Science & Business Media.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.

Dobson, A. J., & Barnett, A. (2008). An introduction to generalized linear models: CRC press.

Florens, J.-P., & Mouchart, M. (1982). A note on noncausality. Econometrica: Journal of the Econometric Society, 583-591.

Fox, J. (2015). Applied regression analysis and generalized linear models: Sage Publications.

Glasure, Y. U. (2002). Energy and national income in Korea: further evidence on the role of omitted variables. Energy economics, 24(4), 355-365.

Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438.

Gujarati, D. N. (2003). Basic Economics”. 4th. In: McGraw Hill, New York.

Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. Journal of political economy, 91(2), 228-248.

Hardin, J. W. (2005). Generalized estimating equations (GEE): Wiley Online Library.

Hossain, S. (2014). Multivariate granger causality between economic growth, electricity consumption, exports and remittance for the panel of three SAARC countries. European Scientific Journal, ESJ, 8(1).

Hwang, D. B., & Gum, B. (1991). The causal relationship between energy and GNP: the case of Taiwan. The Journal of Energy and Development, 219-226.

Işığıçok, E. (1994). Zaman serilerinde nedensellik çözümlemesi: Türkiye'de para arzı ve enflasyon üzerine amprik bir araştırma: Uludağ Üniversitesi Basımevi.

İyit, N., Yonar, H., & Genç, A. (2016). Generalized Linear Models for European Union Countries Energy Data. Acta Physica Polonica, A., 130(1).

Jang, M. J. (2011). Working correlation selection in generalized estimating equations: The University of Iowa.

McCullagh, P. (1984). Generalized linear models. European Journal of Operational Research, 16(3), 285-292.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (Vol. 821): John Wiley & Sons.

Nelder, J. A., & Baker, R. J. (1972). Generalized linear models: Wiley Online Library.

Ozturk, I., Aslan, A., & Kalyoncu, H. (2010). Energy consumption and economic growth relationship: Evidence from panel data for low and middle income countries. Energy Policy, 38(8), 4422-4428.

Pan, W. (2001). Akaike's information criterion in generalized estimating equations. Biometrics, 57(1), 120-125.

Saidi, K., & Mbarek, M. B. (2016). Nuclear energy, renewable energy, CO 2 emissions, and economic growth for nine developed countries: Evidence from panel Granger causality tests. Progress in Nuclear Energy, 88, 364-374.

Shahbaz, M., Lean, H. H., & Shabbir, M. S. (2012). Environmental Kuznets curve hypothesis in Pakistan: cointegration and Granger causality. Renewable and Sustainable Energy Reviews, 16(5), 2947-2953.

Wang, Y. G., & Carey, V. (2003). Working correlation structure misspecification, estimation and covariate design: implications for generalised estimating equations performance. Biometrika, 90(1), 29-41.

Yang, H.-Y. (2000). A note on the causal relationship between energy and GDP in Taiwan. Energy economics, 22(3), 309-317.

Yu, E. S., & Choi, J.-Y. (1985). The causal relationship between energy and GNP: an international comparison. The Journal of Energy and Development, 10(2), 249-272.


  • Şu halde refbacks yoktur.

Creative Commons Lisansı
Bu eser Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır.