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Abstract

This paper focuses on forecasting quarterly 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 a dynamic factor model 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. In our application, the largest improvement in terms of prediction
accuracy is observed when predicting gas prices from 1 to 4 quarters ahead.

Item Type:	Report (Discussion Paper)
Authors/Creators:	Ferrari, D and Ravazzolo, F and Vespignani, JL
Keywords:	energy prices, forecasting, Dynamic Factor model, sparse esti- mation, penalized maximum likelihood
Publisher:	University of Tasmania
Copyright Information:	Copyright 2019 University of Tasmania
Additional Information:	JEL Classification numbers: C1, C5, C8, E3, Q4
Item Statistics:	View statistics for this item

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