Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach

In the new CAMP working paper 11/2019, Ferrari, Ravazzolo and Vespignani forecast quarterly energy prices of commodities, using a large Global VAR dataset proposed by Mohaddes and Raissi (2018). They apply a large dynamic factor model based on a penalized maximum likelihood approach to deal with the information of the large database. They find that, 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. The largest improvement in terms of prediction accuracy is observed when predicting gas prices from 1 to 4 quarters ahead.


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