Full text not available from this repository.

Abstract

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 morethan 80% of the global GDP. To deal with the information on this large database, we apply dynamic factor modelsbased on a penalized maximum likelihood approach that allows to shrink parameters to zero and to estimatesparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selectedloadings across variables. When the model is extended to predict energy commodity prices up to four periodsahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter aheadfor all energy commodities and up to 4 quarters ahead for gas prices. Our model also provides superior forecaststhan machine learning techniques, such as elastic net, LASSO and random forest, applied to the same database.

Item Type:	Article
Authors/Creators:	Ferrari, D and Ravazzolo, F and Vespignani, J
Keywords:	energy prices, forecasting dynamic factor model, sparse estimation, penalized maximum likelihood
Journal or Publication Title:	Energy Economics
Publisher:	Elsevier Science Bv
ISSN:	0140-9883
DOI / ID Number:	10.1016/j.eneco.2021.105268
Copyright Information:	© 2021 Elsevier B.V. All rights reserved.
Related URLs:	Google Scholar: Vespignani, J UTAS Profile: Vespignani, J
Item Statistics:	View statistics for this item

Actions (login required)

Item Control Page