『Abstract
In conventional causality testing based on asymptotic distribution
theory, there is a high risk of wrongly rejecting the true null
of no causality especially when the sample size is as small as
typically seen in the literature. In this study, we offer a formal
diagnosis of the existing contradictory results on the causal
relationship between energy consumption and real GDP. We also
employ a time series oriented advanced data generation process
to perform simulation based inference for the People's Republic
of China. Our study covers the 1971-2007 period and considers
five different aggregated and disaggregated energy consumption
measures as well as three different lag orders in both a bivariate
as well as a multivariate frameworks. Our maximum entropy bootstrap
based analysis, which avoids pretest biases and is also robust
to Type I errors, supports the neutrality hypothesis in 53 out
of the total of 60 model estimations. The strong results show
that coarse aggregate data has a limited potential to observe
the complex causal linkages between energy consumption and economic
growth. Future policy oriented research on this nexus requires
more focused analyses based on sectoral and provincial data.
Keywords: Causality; Bootstrap; China』
1. Introduction
2. An overview of energy issues in china
3. Empirical analysis
3.1. Bivariate analysis
3.2. Multivariate analysis
4. Policy implications of empirical analysis
5. Conclusion
Acknowledgments
References