Another look at the energy-growth nexus: New insights from MIDAS regressions

Abstract

Abstract In this paper, we offer the following contributions to the extant literature on the energy-growth nexus. First, we test the predictability of the energy components in the predictive growth model using the autoregressive distributed lag mixed data sample (ADL-MIDAS) approach. Second, we compare the in-sample and out-of-sample forecast performances of the MIDAS approach with the uniform frequency approach involving the autoregressive (AR) model as well as the autoregressive distributed lag (ARDL) model. Third, we consider an array of energy proxies ranging from aggregate data to sectoral data of energy consumption (residential, commercial, industrial and transportation) and those defined by energy sources (petroleum, natural gas, coal, electricity, nuclear electricity and renewable energy). Fourth, we test whether accounting for asymmetries matters in the ADL-MIDAS regression model for the energy-growth nexus. The results support the significant predictability of energy for growth regardless of the measures of energy. In addition, the in-sample and out-of-sample forecast results overwhelmingly favour the ADL-MIDAS over other competing models. Thus, allowing for high frequency data for energy in the low frequency growth model will enhance the forecast accuracy of the model. However, we find that accounting for asymmetries does not matter in the energy-growth nexus.