This paper focuses on forecasting quarterly nominal global energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information on this large database, we apply dynamic factor models based on a penalized maximum likelihood approach that allows to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selected loadings across variables. When the model is extended to predict energy commodity prices up to four periods ahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter ahead for all energy commodities and up to 4 quarters ahead for gas prices. Our model also provides superior forecasts than machine learning techniques, such as elastic net, LASSO and random forest, applied to the same database.

Item Type:	Refereed Article
Keywords:	energy prices, forecasting dynamic factor model, sparse estimation, penalized maximum likelihood
Research Division:	Economics
Research Group:	Applied economics
Research Field:	Agricultural economics
Objective Division:	Economic Framework
Objective Group:	Macroeconomics
Objective Field:	Taxation
UTAS Author:	Vespignani, J (Associate Professor Joaquin Vespignani)
ID Code:	144057
Year Published:	2021
Web of Science® Times Cited:	1
Deposited By:	Economics and Finance
Deposited On:	2021-04-16
Last Modified:	2021-09-21
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