Modelling Primary Energy Consumption under Model Uncertainty

This paper examines the long-term relationship between primary energy consumption and other key macroeconomic variables, including real GDP, labour force, capital stock and technology, using a panel dataset for 64 countries over the period 1965-2009. Deploying panel error correction models, we find that there is a positive relationship running from physical capital, GDP, and population to primary energy consumption. We observe however a negative relationship between total factor productivity and primary energy usage. Significant differences arise in the magnitude of the cointegration coefficients, when we allow for differences in geopolitics and wealth levels. We also argue that inference on the basis of a single model without taking model uncertainty into account can lead to biased conclusions. Consequently, we address this problem by applying simple model averaging techniques to the estimated panel cointegration models. We find that tackling the uncertainty associated with selecting a single model with model averaging techniques leads to a more accurate representation of the link between energy consumption and the other macroeconomic variables, and to a significantly increased out-of-sample forecast performance.