Business cycle co-movements between renewables consumption and industrial production: A continuous wavelet coherence approach

One may observe that there exist many seminal papers in energy literature to investigate intensively the energy consumption-growth nexus. The majority of these papers employ time series and/or panel data methodologies to explore, if exists, the impact of energy and/or renewable energy usage on economic growth. While time series or panel data methodologies, on the other hand, can capture time and/or cross sectional dimensions of variables, they are not able to observe frequency dimension of the related variables. This paper, thus, aims at considering both time series and frequency properties of relevant data through wavelet coherence methodology to follow the possible influences of US renewable energy consumption on the US economy in both the short run and the long run cycles. This paper, hence, considers short run and long run co-movements between renewables consumption and industrial production through wavelet coherence and wavelet partial coherence analyses. Wavelet analyses are able to capture the co-movements in time and frequency domains. Hence, wavelet analyses are able to observe structural breaks within transitory and permanent cycles through time in analyzing the dependency between two variables. This paper specifically launches continuous wavelet transform methodology since it is more selective with time parameters in comparison with discrete wavelet transform function. Employing the US monthly data of renewables and industrial production with the controlled variables of coal consumption, natural gas consumption and petroleum consumption, this paper observes, first, continuous wavelet coherency analyses and, later, considers continuous wavelet partial coherence analyses with phase differences. In conclusion, paper yields that renewables consumption has positive significant impacts on industrial production, hence, on economic growth, in both lower and higher frequencies in the US. (C) 2015 Elsevier Ltd. All rights reserved.