Modelling soybean prices in a changing policy environment

Goodwin, Barry K.; Schnepf, Randy; Dohlman, Erik

doi:10.1080/00036840412331315060

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…For example, Chavas and Holt (1991) adopt the "deterministic chaos" method to investigate nonlinear dynamics of the corn-hog cycle, Goodwin et al (2005) examine structural change in a system of soybean prices and stocks-to-use ratio, and Balagtas and Holt (2009) investigate nonlinearities in the commodity terms of trade. An approach used by Holt and Craig (2006) is the closest to the motivation and the econometric implementation of the current study, as it investigates nonlinear dynamics of the corn-hog cycle, using time-varying smooth transition autoregressive (TV-STAR) modeling approach.…”

Section: Introductionmentioning

confidence: 99%

Modeling Nonlinearities in the U.S. Soybean‐to‐Corn Price Ratio: A Smooth Transition Autoregression Approach

Ubilava¹

2012

Agribusiness

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The soybean-to-corn price ratio has long been considered one of the triggers that farmers use in the decision to produce corn or soybeans, but little has been done to analyze its dynamics. Recently, the soybean-to-corn price ratio options were introduced on the Chicago Board of Trade, providing the market participants with an opportunity to trade the relative prices of the crops. In this study, the author investigates potentially nonlinear dynamics of the soybean-to-corn price ratio by applying the smooth transition autoregression (STAR) modeling framework to the soybean-to-corn price ratio, derived from monthly futures prices of corn and soybeans. The estimated model demonstrates nonlinear dynamics, resulting in asymmetries in the adjustment to the long-run equilibrium of the soybean-to-corn price ratio. The findings of this research provide insightful implications to crop producers, input suppliers, and other market participants. [EconLit Citations: C220; E370; Q110]. C 2011 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

Modeling Nonlinearities in the U.S. Soybean‐to‐Corn Price Ratio: A Smooth Transition Autoregression Approach

Ubilava¹

2012

Agribusiness

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“…They seek effective management tools to deal with, control, and transfer risks [6]. An accurate soybean price prediction can offer a reliable and essential foundation for market operation and policy planning [7]. It is crucial for steady marketing and the sustainable supply of soybeans worldwide [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention

2022

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China’s soybean spot price has historically been highly volatile due to the combined effects of long-term massive import dependence and intricate policies, as well as inherent environmental elements. The accurate prediction of the price is crucial for reducing the amount of soybean-linked risks worldwide and valuable for the long-term sustainability of global agriculture. Therefore, a hybrid prediction model that combines component clustering and a neural network with an attention mechanism has been developed. After fully integrated complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) processing of the price series, the fuzzy entropy of each component is measured as the complexity characteristic. K-means clustering and reconstruction are applied to the components before being input to the CNN-GRU-Attention network for prediction to improve the model ability and adaptability of the sequences. In the empirical analysis, the proposed model outperforms other decomposition techniques and machine learning algorithms regarding prediction accuracy. After applying the decomposition part, the results have RMSE, MAPE, and MAE values of 49.59%, 22.58%, and 21.99% lower than those of the individual prediction part, respectively. This research presents a novel approach for market participants in the soybean industry for risk response. It gives a new perspective on agricultural product prices in sustainable agricultural marketing, while also providing practical tools for developing public policies and decision-making.

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“…[11]). This section calculates influences on prices of allocation and entitlement water for the period of 1993 to 2007 utilizing average monthly prices paid for water allocations and entitlements.…”

Section: Influences On Average Monthly Allocation and Entitlement Pricesmentioning

confidence: 99%

Factors influencing water allocation and entitlement prices in the Greater Goulburn area of Australia

Wheeler

Bjørnlund

Shanahan

et al. 2008

WIT Transactions on Ecology and the Environment

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This paper examines the factors that influence the price of water allocations and entitlements by using actual monthly prices paid for water allocations and entitlements from 1993 to 2007. Water allocation prices are influenced by seasonal factors, current water allocations received, some rural commodity prices and policy impacts. The price of water entitlements are most significantly influenced by current prices of water allocations, allocations currently received, seasonal factors and government water policy. Such an analysis highlights the importance of government policy in influencing water markets.

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Modelling soybean prices in a changing policy environment

Cited by 11 publications

References 8 publications

Modeling Nonlinearities in the U.S. Soybean‐to‐Corn Price Ratio: A Smooth Transition Autoregression Approach

Modeling Nonlinearities in the U.S. Soybean‐to‐Corn Price Ratio: A Smooth Transition Autoregression Approach

A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention

Factors influencing water allocation and entitlement prices in the Greater Goulburn area of Australia

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