Many oil industry analysts believe that there is predictive power in the product spread, defined as the difference between suitably weighted refined product market prices and the price of crude oil. We derive a number of alternative forecasting model specifications based on product spreads and compare the implied forecasts to the no-change forecast of the real price of oil. We show that not all product spread models are useful for out-of-sample forecasting, but some models are, even at horizons between one and two years. The most accurate model is a time-varying parameter model of gasoline and heating oil spot price spreads that allows for structural change in product markets. We document mean-squared prediction error reductions as high as 20% and directional accuracy as high as 63% at the two-year horizon, making product spread models a good complement to forecasting models based on economic fundamentals, which work best at short horizons.

Little is known about the accuracy of expert outlooks, so heavily relied upon by industry participants and policy makers, regarding the future path of oil prices. Using the regular publications by the Energy Information Administration (EIA), we examine the accuracy of annual recursive oil price forecasts generated by the National Energy Modeling System model of the Agency for forecast horizons of up to 15 years. Our results reveal that the EIA model outperforms the benchmark random walk model around the two ends of the forecast horizon spectrum. Additionally, at the longer horizons, simple econometric forecasting models often produce similar, if not better accuracy than the EIA model. Time varying such specifications generally also exhibit stability in their forecast performance. Finally, although combining forecasts does not change the overall patterns, some additional accuracy gains are obtained at intermediate horizons, and in some cases, forecast performance stability is also achieved.

We study the impact of oil price shocks on the U.S. stock market volatility. We jointly analyze three different structural oil market shocks (i.e., aggregate demand, oil supply, and oil-specific demand shocks) and stock market volatility using a structural vector autoregressive model. Identification is achieved by assuming that the price of crude oil reacts to stock market volatility only with delay. This implies that innovations to the price of crude oil are not strictly exogenous, but predetermined with respect to the stock market. We show that volatility responds significantly to oil price shocks caused by unexpected changes in aggregate and oil-specific demand, whereas the impact of supply-side shocks is negligible.

This paper tests the three leading specifications of asymmetric and possibly nonlinear feedback from the real price of oil to U.S. industrial production and its sectoral components. We show that the evidence for such feedback is sensitive to the estimation period. Support for a nonlinear model is strongest for samples starting before 1973. Instead, using post-1973 data only, the evidence against symmetry becomes considerably weaker. For example, at the aggregate level, there is no evidence against the hypothesis of symmetric responses to oil price innovations of typical magnitude, consistent with results of Kilian and Vigfusson [Quantitative Economics, 2(3), 419–453 (2011)] for U.S. real GDP. There is strong evidence of asymmetries at the disaggregate level, however, especially for industries that are energy-intensive in production (such as chemicals) or that produce goods that are energy-intensive in use (such as transportation equipment). Our analysis suggests that these asymmetries may be obscured in the aggregate data and highlights the importance of developing multisector models of the transmission of oil price shocks.

It is customary to suggest that the asymmetry in the transmission of oil price shocks to real output is well established. Much of the empirical work cited as being in support of asymmetry, however, has not directly tested the hypothesis of an asymmetric transmission of oil price innovations. Moreover, many of the papers quantifying these asymmetric responses are based on censored oil price VAR models that have recently been shown to be invalid. Other studies are based on dynamic correlations in the data and do not distinguish between cause and effect. Recently, several new methods of testing and quantifying asymmetric responses of U.S. real economic activity to positive and negative oil price innovations have been developed. We put this literature into perspective, contrast it with more traditional approaches, highlight directions for further research, and reconcile some seemingly conflicting results reported in the literature.