Decomposing supply shocks in the US electricity industry: evidence from a time-varying Bayesian panel vector autoregression model

Nicholas Apergis and Michael Polemis

Abstract

This paper investigates spillovers between electricity supply shocks and US growth, using monthly data from forty-eight US states from January 2001 to September 2016, and employs a novel strategy for electricity supply shocks based on a time-varying Bayesian panel vector autoregression model. It accounts for the decomposition of the electricity supply by fuel type and links its possible interactions with the US macroeconomic conditions. In that sense, the methodology models the coefficients as a stochastic function of multiple structural characteristics. The findings document that gross domestic product growth increases after a positive electricity supply shock, regardless of the source of energy that generates it. The absence of a sluggish adjustment mechanism may reflect weak competition and significant market power by the incumbents in the electricity industry. Lastly, we argue that the rate of response of gross domestic product growth per capita to electricity supply