The transmission of oil price shocks has been a question of central interest in macroeconomics since the 1970s. There has been renewed interest in this question after the large and persistent fall in the real price of oil in 2014–16. In the context of this debate, Ramey (2017) makes the striking claim that the existing literature on the transmission of oil price shocks is fundamentally confused about the question of how to quantify the effect of oil price shocks.
It is commonly believed that the response of the price of corn ethanol (and hence of the price of corn) to shifts in biofuel policies operates in part through market expectations and shifts in storage demand, yet to date it has proved difficult to measure these expectations and to empirically evaluate this view.
Futures markets are a potentially valuable source of information about price expectations. Exploiting this information has proved difficult in practice, because time-varying risk premia often render the futures price a poor measure of the market expectation of the price of the underlying asset.
The answer as to whether there are gains from pooling real-time oil price forecasts depends on the objective. The approach of combining five of the leading forecasting models with equal weights dominates the strategy of selecting one model and using it for all horizons up to two years.
Forecasts of the price of crude oil play a significant role in the conduct of monetary policy, especially for commodity producers such as Canada. This article presents a range of recently developed forecasting models that, when pooled together, can generate, on average, more accurate forecasts of the price of oil than the oil futures curve. It also illustrates how policy-makers can evaluate the risks associated with the baseline oil price forecast and how they can determine the causes of past oil price fluctuations.