Autoregressive Distributed Lag (ARDL) analysis of U.S.-China commodity trade dynamics

Abstract

This study examines the U.S.-China trade dynamics focusing on the leading commodity imports and exports in both manufacturing (SITC 5–8) and crude materials (SITC2) sectors at SITC 3-digit level. The effect of real income of China and the U.S., bilateral real exchange rates and its volatility, U.S. investment, the “third country” exchange risk effect, and the role of financial deepening and human capital investment are evaluated using 2000–2016 quarterly data. The Autoregressive Distributed Lag (ARDL) model results for the U.S. inpayments and outpayments indicate that real exchange rates play the most significant role, affecting most commodities in both the short-and long-run, with the expected signs. While the relationship of most U.S. commodity exports and imports with the real income of both China and the U.S. have the expected long-run positive effect, the average responsiveness of the U.S. imports to U.S. real income is larger than that of the U.S. exports’ response to Chinese real income. This, combined with the relatively larger exchange rate elasticity value for the U.S. exports, suggests a greater U.S.-China trade deficit following the real appreciation of the yuan, which is consistent with the actual post-2005 trade experience. Both the real exchange rate volatility measures and the U.S. foreign direct investment in China are found to have a limited significant relationship with both U.S. commodity imports and exports. Although financial deepening and education affect trade in both directions, the positive influence of human capital is consistent and evident across most affected commodity cases.