Measuring persistent global economic factors with output, commodity price, and commodity currency data

Abstract

In this study, we use monthly G7 industrial production data, commodity price index data, and commodity currency exchange rate data in a dynamic factor model to examine the global economic factors useful for commodity price prediction. We differentiate between the dynamic factors by specifying a persistent factor and a non-persistent factor, both as a single global factor using all data and as factors for each category of data. The in-sample predictive performances of the three persistent factors together are better than the non-persistent factors and the single global factors. Out-of-sample outcomes based on forecast combinations also support the presence of predictive information in the persistent factors for overall commodity prices and for most sub-categories of commodity price indexes relative to their means. The gains in forecast accuracy are heterogeneous, ranging from 5% to 7% in the 1- to 6-month horizon for overall commodity prices to a high of around 20% for fertilizers in the 12-month horizon in the recent sample. We further show that the information in the persistent factors, especially in the commodity currency exchange rate-based persistent factor, can be integrated with other global measures to further improve the predictive performances of the global measures.