The Electricity Consumption and Economic Growth Nexus in China: A Bootstrap Seemingly Unrelated Regression Estimator Approach

Jianlin Wang, Jiajia Zhao () and Hongzhou Li
Additional contact information
Jianlin Wang: Dongbei University of Finance and Economics
Jiajia Zhao: Dongbei University of Finance and Economics
Hongzhou Li: Dongbei University of Finance and Economics

Computational Economics, 2018, vol. 52, issue 4, No 9, 1195-1211

Abstract: 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.

Keywords: Electricity consumption; Economic growth; Bootstrap method; Seemingly unrelated regression estimator (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9709-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:52:y:2018:i:4:d:10.1007_s10614-017-9709-1

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9709-1

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().