Forecasting energy commodity prices: A large global dataset sparse approach 1 1 We thank the associate editor, three anonymous referee, our discussant Shaun Vahey and conference and seminar participants at the CAMA-CAMP-RBA "International Economic Flows: Energy, Finance, Diplomacy and Market Structures" workshop for very useful comments. This paper is part of the research activities at the Centre for Applied Macroeconomics and commodity Prices (CAMP) at BI Norwegian Business School

Highlights: • This paper applies a dynamic sparse factor model for forecasting energy prices. • The estimated latent factors show considerable sparsity and heterogeneity. • Larger predictability for all energy commodities. • Machine learning techniques do not provide similar gains. 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.