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Forecasting energy commodity prices: A large global dataset sparse approach
Citation
Ferrari, D and Ravazzolo, F and Vespignani, J, Forecasting energy commodity prices: A large global dataset sparse approach, Energy Economics, 98 Article 105268. ISSN 0140-9883 (2021) [Refereed Article]
Copyright Statement
© 2021 Elsevier B.V. All rights reserved.
DOI: doi:10.1016/j.eneco.2021.105268
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 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 Details
Item Type: | Refereed Article |
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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: | 4 |
Deposited By: | Economics and Finance |
Deposited On: | 2021-04-16 |
Last Modified: | 2021-12-15 |
Downloads: | 0 |
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