usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of …

and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature …

systems. However, forecasting energy price volatility can be less satisfactory, especially in the era of big data because of … techniques that seamlessly cut across artificial intelligence and computational intelligence for forecasting energy price … machine model (MS-GARCH-type-ELM) alongside the selection of an appropriate model for price volatility forecasting in the …

amount of support within sample, it appears to be of more limited importance from a forecasting perspective. …

Recent research has shown that a reliable vector autoregressive model (VAR) for forecasting and structural analysis of … priors. This is important both for reduced form applications, such as forecasting, and for more structural applications, such …