New Findings Regarding the Out-of-Sample Predictive Impact of the Price of Crude Oil on the United States Industrial Production

Contrary to the extensive literature pioneered by James Hamilton in the early 1980s that focuses on analyzing the relationship between changes in the price of crude oil and the U.S. real gross domestic product growth (GDP) rate, Herrera et al. (2011) is essentially the first study that explores the in-sample predictive impact of the price of crude oil on the U.S. industrial production index. To date, almost nothing is known about the nature and degree of the out-of-sample predictive impact of the price of crude oil on the U.S. industrial production index. This study fills the gap. Using various nonlinear transformations of the price crude oil widely employed in the crude oil price/GDP predictability literature as well as crude oil price volatility measures, we document (rather surprisingly) that the form of nonlinearity that delivers the most consistent pattern of out-of-sample population-level predictability gains relative to the benchmark when forecasting ex-post revised as well as real-time U.S. industrial production has to do with crude oil price decreases below the minimum price in recent memory. In contrast to the GDP predictability literature, crude oil price increases beyond the maximum in recent memory do not afford any predictive power. On the contrary, they deteriorate relative forecast performance. These results go directly against a distinct sense of déjà vu that one would expect given the degree of affinity between industrial production and GDP. The predictive power afforded by crude oil price net decreases also translate into economic gains. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.