Forecasting oil Prices: can large BVARs help?

Bh Nguyen () and Bo Zhang ()
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Bh Nguyen: Tasmanian School of Business & Economics, University of Tasmania, https://www.utas.edu.au/profiles/staff/economics/bao-nguyen
Bo Zhang: Business School, Wenzhou University, Wenzhou, Zhejiang Province and Centre for Applied Macroeconomic Analysis (CAMA), Australian National University, Australia

No 2022-04, Working Papers from University of Tasmania, Tasmanian School of Business and Economics

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: 25 pages
Date: 2022
New Economics Papers: this item is included in nep-ene and nep-for
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Published by the University of Tasmania. Discussion paper 2022-04

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https://eprints.utas.edu.au/47522/1/2022-04_Nguyen_Zhang.pdf

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Persistent link: https://EconPapers.repec.org/RePEc:tas:wpaper:47522

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