This study investigates dynamic relationships among U.S. corn cash prices for the years 2006-2011. With daily data from 182 spatially separated markets spreading across 7 states, Iowa (IA), Illinois (IL), Indiana (IN), Ohio (OH), Minnesota (MN), Nebraska (NE), and Kansas (KS), we apply an error...

We model the energy–agriculture linkage through structural vector autoregression (VAR) model. This model quantifies the relative importance of various contributing factors in driving prices in both markets. The LiNGAM algorithm from the machine learning literature is used to help identify...

Corn prices experienced enormous volatility over the last decade. In this paper, we apply a structural vector autoregression model to quantify the relative importance of various contributing factors in driving corn price movements. The identification of the structural parameters is achieved...

Commodity and energy prices have exhibited an unprecedented increase between October 2006 and July 2008, only to fall sharply during the last months of 2008. Many explanations have been offered to this phenomenon, including steadily increasing demand from China and India, large mandated...