Measuring Dependence between Electricity Consumption and Economic Indicators via Copulas: Turkish Case

Measuring Dependence between Electricity Consumption and Economic Indicators via Copulas: Turkish Case

This paper implements copulas to identify the dependence structure between electricityconsumption and its cofounding indicators. To achieve this, Turkish electricity demand, itseconomic and sectoral indicators are taken into account. As a first step, bivariate copulas areused to identify the best fitting copula and the degree of the dependence. Thereafter,multivariate model is established using vine copulas using highly correlated variables. Theempirical results confirm the added value of the proposed approach in determining numeroustail properties. We indicate that the copulas are useful to underline, especially, the tail propertiesof indicators in the market for decision makers.

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  • Payne, J. E., “A survey of the electricity consumption-growth literature”, Applied Energy, 87: 723- 731, (2010).
  • Georgantopoulos, A., “Electricity consumption and economic growth: analysis and forecasts using VAR/VEC approach for Greece with capital formation”, International Journal of Energy Economics and Policy, 2(4): 263-278, (2012).
  • Bouoiyuor, J., Selmi, R., Ozturk, I., “The nexus between electricity consumption and economic growth: new insights from meta-analysis”, International Journal of Energy Economics and Policy, 4(4): 621-635, (2014).
  • Joyeux, R., Ripple, R. D., “Energy consumption and real income: a panel cointegration multicountry study”, The Energy Journal, 32(2): 107-141, (2011).
  • Tang, C. F., Tan, E. C., “Energy consumption and economic growth in Portugal: evidence from a multivariate framework analysis”, The Energy Journal, 33(4): 23-48, (2012).
  • Lean, H. H., Smyth, R., “Multivariate Granger causality between electricity generation, exports, prices and GDP in Malaysia”, Energy, 35: 3640-3648, (2010).
  • Zeshan, M., “Finding the cointegration and causal linkages between the electricity production and economic growth in Pakistan”, Economic Modeling, 31: 344-350, (2013).
  • Sklar, A., “Fonctions de repartititon a n dimensions et leurs merges”, Publications de l’Institut de Statistique de l’Université de Paris, 8: 229-231, (1959).
  • Patton, A., “A review of copula models for economic time series”, Journal of Multivariate Analysis, 110: 4-18, (2012).
  • Bedford, T., Cooke, R., “Probability density decompositions for conditionally dependent random variables modeled by vines”, Annals of Mathematics and Artificial Intelligence, 32: 245-268, (2001).
  • Zadkarami, M. R., Chatrabgoun, O., “Bayesian inference of pair-copula construction for multivariate dependency modeling of Iran’s macroeconomic variables”, Journal of Modern Applied Statistical Methods, 12(1): 227-234, (2013).
  • Maden, S., Baykul, A., “Co-integration analysis of price and income elasticities of electricity power consumption in Turkey”, European Journal of Social Sciences, 30(4): 523-534, (2012).
  • Kucukbahar, D., “Modeling monthly electricity demand in Turkey for 1990-2006”, MSc Thesis, Middle East Technical University Graduate School of Natural and Applied Sciences, Ankara, (2008).
  • Soytas, U., Sari, R., “The relationship between energy and production: evidence from Turkish manufacturing industry”, Energy Economics, 29(6): 1151-1165, (2007).
  • Ozdemir, A., “Demand, supply and partial equilibrium analysis of Turkish electricity energy pricing”, MSc. Thesis, Middle East Technical Graduate School of Applied Mathematics, Ankara, (2013).
  • Nelsen, R. B., An Introduction to Copulas, Lecture Notes in Statistics 139, Springer, New York, (1999).
  • Kurowicka, D., Cooke, R., Uncertainty Analysis with High Dimensional Dependence Modelling, John Wiley & Sons, New York, (2006).
  • Aas, K., Czado, C., Frigessi, A., Bakken, H., “Pair copula constructions of multiple dependence”, Insurance Mathematics and Economics, 44: 182-198, (2009).
  • Bedford, T., Cooke, R., “Vines: a new graphical model for dependent random variables”, The Annals of Statistics, 30(4): 1031-1068, (2002).
  • “Statistics Service”, World Bank database, (2010), http://data.worldbank.org/.
  • “Electricity Statistics”, TEIAS database, (2010), http://teias.gov.tr/istatistikler/aspx.
  • Vuong, Q. H., “Ratio tests for model selection and non-nested hypotheses”, Econometrica, 57(2): 307-333, (1989).
  • Clarke, K. A., “A Simple distribution-free test for non-nested model selection”, Political Analysis, 15: 347-363, (2007).