Commodity markets and the global macroeconomy: evidence from machine learning and GVAR

Abstract

Based on a strongly data-intensive machine learning approach, this study first identifies the most essential globally traded commodities in view of their role for the global macroeconomic performance. At the second stage we estimate a global vector autoregressive model to assess in more detail these global reactions. Our results from the first stage indicate that of the 55 analyzed commodity markets, only four are revealed as the most important. At the second step, our GVAR analysis indicates that the commodity market effects on macroeconomic activity are neither unanimous across the commodities nor across macrovariables. As an overall result, the commodity market exposure is clearly stronger among the advanced countries such as the euro area, other developed economies, and China, compared to the emerging economies of Africa, Asia, and Latin America, at both the country and regional levels. This puts a lot of pressure on economic policies aimed at reducing, e.g., the depriving effects of commodity market price development on aggregate economic performance of these countries.