Forecasting Oil Prices: Can Large BVARs Help?

Bao H. Nguyen and Bo Zhang

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: Large Bayesian Vector Autoregressions (BVARs) have been a successful tool in the forecasting literature and most of this work has focused on macroeconomic variables. In this paper, we examine the ability of large BVARs to forecast the real price of crude oil using a large dataset with over 100 variables. We find consistent results that the large BVARs do not beat the BVARs with small and medium sizes for short forecast horizons but offer better forecasts at long horizons. In line with the forecasting macroeconomic literature, we also find that the forecast ability of the large models further improves upon the competing standard BVARs once endowed with flexible error structures.

Keywords: forecasting; non-Gaussian; stochastic volatility; oil prices; big data (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 Q41 Q47 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2022-10
New Economics Papers: this item is included in nep-ene, nep-ets, nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2022-65

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