LAUSR

The study empirically examines the association between electricity demand and economic growth in China in a time–frequency framework. Wavelet coherence analysis and phase difference methods are applied to find the co-movement and causality between variables using monthly data for 1999 to 2017 time period. The results of the wavelet power spectrum show that both series have high fluctuations at high frequencies. The findings of wavelet coherence reveal co-movements between electricity demand and economic growth at different frequency levels. However, this association is stronger at low-frequency levels. Evidence from the phase difference indicates that electricity is causing economic growth with a positive sign. The results of wavelet-based correlation also show a high correlation between these two variables. For robustness analysis, linear and nonlinear causality tests are applied to find causality between variables over time. Both linear and nonlinear causality tests reveal bidirectional causality between variables. It corroborates the result of wavelet causality that both variables cause each other at different frequency levels.