Abstract
Electricity consumption in China has attracted increasing attention by the government in monitoring the economy. The purpose of the study is test whether electricity consumption is an appropriate indicator. To do that, this paper proposes an alternative bootstrap Granger causality test, which can capture the contemporaneous correlation of the term error in the Vector Autoregressive Model, based on a seemingly unrelated regression estimator. Using a quarterly data set containing more dynamic changes, this study reinvestigates the relationship between electricity consumption and economic growth. The results show that there exists a long-run relationship between the two variables. Electricity consumption can be treated as an indicator of the functioning of the economy. A strong unidirectional Granger causality is found running from gross domestic product to electricity consumption. However, the causality relationship from electricity consumption to gross domestic product is relatively weak. Thus, electricity consumption is a useful indicator to check the reliability of GDP data, however, caution is required when using electricity consumption to predict future economic activities in China.
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Akinlo, A. E. (2009). Electricity consumption and economic growth in Nigeria: Evidence from cointegration and co-feature analysis. Journal of Policy Modeling, 31(5), 681–693.
Al-mulali, U., Fereidouni, H. G., & Lee, J. Y. M. (2014). Electricity consumption from renewable and non-renewable sources and economic growth: Evidence from Latin American countries. Renewable and Sustainable Energy Reviews, 30, 290–298.
Aqeel, A., & Butt, M. S. (2001). The relationship between energy consumption and economic growth in Pakistan. Asia-Pacific Development Journal, 8(2), 101–110.
Dolado, J. J., & Lutkepohl, H. (1996). Making wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369–386.
Engle, R. F., & Granger, C. W. J. (1987). Cointegration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.
Fatai, K., Oxley, L., & Scrimgeour, F. G. (2004). Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, The Philippines and Thailand. Mathematics and Computers in Simulation, 64(3–4), 431–445.
Ghosh, S. (2002). Electricity consumption and economic growth in India. Energy Policy, 30(2), 125–129.
Gurgul, H., & Lach, Ł. (2012). The electricity consumption versus economic growth of the Polish economy. Energy Economics, 34(2), 500–510.
Hacker, R. S., & Hatemi-J, A. (2006). Tests for causality between integrated variables using asymptotic and bootstrap distributions: Theory and application. Applied Economics, 38(13), 1489–1500.
Hafner, C. M., & Herwartz, H. (2009). Testing for linear vector autoregressive dynamics under multivariate generalized autoregressive heteroskedasticity. Statistica Neerlandica, 63(3), 294–323.
Hatemi-J, A. (2002). Export performance and economic growth nexus in Japan: A bootstrap approach. Japan and the World Economy, 14(1), 25–33.
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254.
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—With applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210.
Karanfil, F., & Li, Y. (2015). Electricity consumption and economic growth: Exploring panel-specific differences. Energy Policy, 82, 264–277.
Kraft, J., & Kraft, A. (1978). Relationship between energy and GNP. Journal of Energy and Development, 3(2), 401–403.
Kruskal, W. (1968). When are Gauss–Markov and Least Squares Estimators identical? A coordinate-free approach. Annals of Mathematical Statistics, 39(1), 70–75.
Mackinnon, J. G. (2002). Bootstrap inference in econometrics. Canadian Journal of Economics, 35(4), 615–645.
Mantalos, P. (2000). A graphical investigation of the size and power of the Granger-causality tests in integrated-cointegrated VAR systems. Studies in Nonlinear Dynamics and Econometrics, 4(1), 1–18.
Mantalos, P., & Shukur, G. (1998). Size and power of the error correction model cointegration test: A bootstrap approach. Oxford Bulletin of Economics and Statistics, 60(2), 249–255.
Odhiambo, N. M. (2009). Electricity consumption and economic growth in South Africa: A trivariate causality test. Energy Economics, 31(5), 635–640.
Osman, M., Gachino, G., & Hoque, A. (2016). Electricity consumption and economic growth in the GCC countries: Panel data analysis. Energy Policy, 98, 318–327.
Ozturk, I. (2010). A literature survey on energy-growth nexus. Energy Policy, 38(1), 340–349.
Park, J. Y., & Phillips, P. C. B. (1989). Statistical inference in regressions with integrated processes: Part 2. Econometric Theory, 5(1), 95–131.
Payne, J. E. (2010a). A survey of the electricity consumption-growth literature. Applied Energy, 87(3), 723–731.
Payne, J. E. (2010b). Survey of the international evidence on the causal relationship between energy consumption and growth. Journal of Economic Studies, 37(1), 53–95.
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
Shahbaz, M., Sbia, R., Hamdi, H., & Ozturk, I. (2014). Economic growth, electricity consumption, urbanization and environmental degradation relationship in United Arab Emirates. Ecological Indicators, 45, 622–631.
Shiu, A., & Lam, P.-L. (2004). Electricity consumption and economic growth in China. Energy Policy, 32(1), 47–54.
Shukur, G., & Mantalos, P. (2000). A simple investigation of the Granger-causality test in integrated–cointegrated VAR systems. Journal of Applied Statistics, 27(8), 1021–1031.
Song, M., & Wang, J. (2016). Coal price fluctuations in China: Economic effects and policy implications. Journal of Renewable and Sustainable Energy, 8(6), 422–431.
Song, M., & Zhou, Y. (2015). Analysis of carbon emissions and their influence factors based on data from Anhui of China. Computational Economics, 46(3), 359–374.
Song, M., Song, H., Zhao, J., & Wang, J. (2015). Power supply, coal price, and economic growth in China. Energy Systems. doi:10.1007/s12667-015-0167-3.
Thoma, M. (2004). Electrical energy usage over the business cycle. Energy Economics, 26(3), 463–485.
Toda, H. Y., & Phillips, P. C. B. (1993). Vector autoregressions and causality. Econometrica, 61(6), 1367–1393.
Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1), 225–250.
Wang, Y., Yao, X., & Yuan, P. (2015). Strategic adjustment of China’s power generation capacity structure under the constraint of carbon emission. Computational Economics, 46(3), 421–435.
Yang, H.-Y. (2000). A note on the causal relationship between energy and GDP in Taiwan. Energy Economics, 22(3), 309–317.
Yoo, S.-H., & Kim, Y. (2006). Electricity generation and economic growth in Indonesia. Energy, 31(14), 2890–2899.
Yu, L., & Wang, J. (2008). Economic analysis and solution of vertical dual pricing system. Journal of Chinese Industrial Economics, 10, 43–52.
Yuan, J., Zhao, C., Yu, S., & Hu, Z. (2007). Electricity consumption and economic growth in China: Cointegration and co-feature analysis. Energy Economics, 29(6), 1179–1191.
Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348–368.
Acknowledgements
We are thankful for the support of Humanities and Social Science Research of the Ministry of Education Youth Project of China (No. 16YJCZH155), China Natural Science Foundation (Nos. 71203023 and 71663021).
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Wang, J., Zhao, J. & Li, H. The Electricity Consumption and Economic Growth Nexus in China: A Bootstrap Seemingly Unrelated Regression Estimator Approach. Comput Econ 52, 1195–1211 (2018). https://doi.org/10.1007/s10614-017-9709-1
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DOI: https://doi.org/10.1007/s10614-017-9709-1