Modelling long-run energy consumption under model uncertainty

The causality between energy consumption and real income in developed countries has been a very vital research topic in recent years. Raising concerns about climate change and global warming increase the pressure on policy makers to take action against energy depletion. Unfortunately these efforts coincide with one of the largest global economic recessions in the past century. Establishing the link between energy consumption and other key macroeconomic variables like real GDP, labour force, capital stock and technology thus currently appears as a particularly relevant research effort.In this area of the literature, uncertainty about the choice of a statistical model is rarely considered. Whenever one model is opted over reasonable alternative models to represent knowledge about a specific process, model uncertainty occurs. We argue that inference on the basis of a single model without taking model uncertainty into account can lead to strongly biased conclusions. Consequently we tackle this problem by applying simple model-averaging techniques to commonly used panel cointegration models.We expect to find that taking into account 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 other macroeconomic variables and a significantly increased out-of-sample forecast performance.