Identifying Lead-Lag Relationships in Illinois Soybean Basis

The purpose of this paper is to identify soybean basis relationships between differing regions of the state of Illinois. Time-series analysis using a Granger Causality framework is conducted to identify lead-lag relationships between seven geographical regions of Illinois. The regions are identified as Northern, Western, North Central, South Central, Wabash, West-Southwest, and Little Egypt. There has been considerable research describing the factors that influence grain basis; the most consistently identified being local production and consumption, stocks, storage capacity and cost, and transportation costs. However, there has been minimal inquiry into tracking grain basis relationships through time in different marketplaces. This area of research has a high level of importance because if a lead-lag relationship is found between any two regions, the leading region soybean basis can be used as a tool to assist in predicting future soybean basis in the lagging region. The time-series analysis results indicate that lead-lag relationships do play a role in determining Illinois soybean basis. The Western and West-Southwest regions are the most dominant while the Southern Illinois regions of Wabash and Little Egypt are the least. These findings can help soybean basis users in making important decisions regarding expected basis levels during the marketing year

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A Curriculum Revision Evaluation Model: Its Tryout and Revision

175 p.Thesis (Educat.D.)--University of Illinois at Urbana-Champaign, 1968.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Calculating Reliable Gibbs Free Energies for Formation of Gas-Phase Clusters that Are Critical for Atmospheric Chemistry: (H\u3csub\u3e2\u3c/sub\u3eSO\u3csub\u3e4\u3c/sub\u3e)\u3csub\u3e3\u3c/sub\u3e

The effects of atmospheric aerosols on our climate are one of the biggest uncertainties in global climate models. Calculating the pathway for the formation of pre-nucleation clusters that become aerosols is challenging, requiring a comprehensive analysis of configurational space and highly accurate Gibbs free energy calculations. We identified a large set of minimum energy configurations of (H2SO4)3 using a sampling technique based on a genetic algorithm and a stepwise density functional theory (DFT) approach and computed the thermodynamics of formation of these configurations with more accurate wavefunction-based electronic energies computed on the DFT geometries. The DLPNO-CCSD(T) methods always return more positive energies compared to the DFT energies. Within the DLPNO-CCSD(T) methods, extrapolating to the complete basis set limit gives more positive free energies compared to explicitly correlated single-point energies. The CBS extrapolation was shown to be robust as both the 4-5 inverse polynomial and Riemann zeta function schemes were within chemical accuracy of one another