An integrated Bayesian vector autoregression and error correction model for forecasting electricity consumption and prices

The analysis and forecasting of electricity consumption and prices has received considerable attention over the past forty years. In the 1950s and 1960s most of these forecasts and analyses were generated by simultaneous equation econometric models. Beginning in the 1970s, there was a shift in the modelling of economic variables from the structural equations approach with strong identifying restrictions towards a joint time-series model with very few restrictions. One such model is the vector autoregression (VAR) model. It was soon discovered that the unrestricted VAR models do not forecast well. The Bayesian vector autoregression (BVAR) approach as well as the error correction model (ECM) and models based on the theory of cointegration have been offered as alternatives to the simple VAR model. This paper argues that the BVAR, ECM, and cointegration models are simply VAR models with various restrictions placed on the coefficients. Based on this notion of a restricted VAR model, a four-step procedure for specifying VAR forecasting models is presented and then applied to monthly data on US electricity consumption and prices.