Forecasting Natural Gas Prices in Real Time

ABSTRACT This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in complexity and economic content. We find that considerable reductions in mean‐squared prediction error relative to a no‐change benchmark can be achieved in real time for horizons of up to 2 years. A particularly promising model is a vector autoregressive (VAR) model that includes the fundamental determinants of supply and demand for natural gas. To capture real‐time data constraints of these and other predictors, we assemble a rich database of historical vintages from multiple sources. We also compare our model‐based forecasts to model‐free forecasts provided by experts and futures markets. Given that no single forecasting method dominates, we show that combining forecasts from individual models selected in real time using the model confidence set as a novel criterion for dynamic model selection delivers the most accurate forecasts.