Contemporary structural models of the global market for crude oil treat storage demand as a composite of precautionary responses to uncertainty and speculative behavior, due to difficulties in jointly identifying these distinct demand components. This difficulty arises because the underlying expectation shifts are latent and operate through similar transmission mechanisms. In this paper, we extend the workhorse oil market model by jointly identifying these distinct demand components. Our main insight is that precautionary demand is the primary driver of the real price of crude oil, previously associate with storage demand shocks. Historically, precautionary demand shifts associated with adverse sociopolitical conditions in the Middle-East, can explain the oil price spikes during the 1979 oil crisis and the Wars of 1980 and 1990, while speculative demand was a more important driver during the disbandment of OPEC. Finally, we find that these newly identified shocks have distinct consequences for the U.S. economy: precautionary demand shocks reduce real GDP, while speculative demand shocks cause inflation.
This paper evaluates the real-time forecast performance of alternative Bayesian Vector Autoregressive (VAR) models for the Australian macroeconomy. To this end, we construct an updated vintage database and estimate a set of model specifications with different covariance structures. The results suggest that a large VAR model with 20 variables tends to outperform a small VAR model when forecasting GDP growth, CPI inflation and unemployment rate. We find consistent evidence that the models with more flexible error covariance structures forecast GDP growth and inflation better than the standard VAR, while the standard VAR does better than its counterparts for unemployment rate. The results are robust under alternative priors and when the data includes the early stage of the COVID-19 crisis.
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