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Modeling Yield Risk Under Technological Change: Dynamic Yield Distribution and the U.S Crop Insurance Program

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  • Zhu, Ying
  • Goodwin, Barry K.
  • Ghosh, Sujit K.

Abstract

The objective of this study is to evaluate and model the yield risk associated with major agricultural commodities in the U.S. We are particularly concerned with the nonstationary nature of the yield distribution, which primarily arises because of technological progress and changing environmental conditions. Precise risk assessment depends on the accuracy of modeling this distribution. This problem becomes more challenging as the yield distribution changes over time, a condition that holds for nearly all major crops. A common approach to this problem is based on a two-stage method in which the yield is first detrended and then the estimated residuals are treated as observed data and modeled using various parametric or nonparametric methods. We propose an alternative parametric model that allows the moments of the yield distributions to change with time. Several model selection techniques suggest that the proposed time-varying model outperforms more conventional models in terms of in-sample goodness-of-fit, out-of-sample predictive power and the prediction accuracy of insurance premium rates.

Suggested Citation

  • Zhu, Ying & Goodwin, Barry K. & Ghosh, Sujit K., 2011. "Modeling Yield Risk Under Technological Change: Dynamic Yield Distribution and the U.S Crop Insurance Program," Working Papers 102048, Structure and Performance of Agriculture and Agri-products Industry (SPAA).
  • Handle: RePEc:ags:spaawp:102048
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    References listed on IDEAS

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    1. Jean-Philippe Gervais & Maurice Doyon, 2004. "Developing Hedging Strategies for Qu├ębec Hog Producers under Revenue Insurance," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 52(1), pages 35-53, March.
    2. Lota D. Tamini & Jean-Philippe Gervais, 2005. "Developing Economic Indexes for the Quebec Hog/Pork Industry," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 53(1), pages 1-23, March.
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    Cited by:

    1. Wyatt Thompson & Joe Dewbre & Patrick Westfhoff & Kateryna Schroeder & Simone Pieralli & Ignacio Perez Dominguez, 2017. "Introducing medium-and long-term productivity responses in Aglink-Cosimo," JRC Working Papers JRC105738, Joint Research Centre (Seville site).
    2. Shen, Zhiwei, 2016. "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wagenigen, The Netherlands 249984, European Association of Agricultural Economists.
    3. Agarwal, Sandip Kumar, 2017. "Subjective beliefs and decision making under uncertainty in the field," ISU General Staff Papers 201701010800006248, Iowa State University, Department of Economics.
    4. Poudel, Mahadeb Prasad & Chen, Shwu-En & Ghimire, Raju, 2013. "Rice Yield Distribution and Risk Assessment in South Asian Countries: A Statistical Investigation," International Journal of Agricultural Management and Development (IJAMAD), Iranian Association of Agricultural Economics, vol. 3(1), March.

    More about this item

    Keywords

    Crop Insurance; Model Comparison; Time-Varying Distribution; Financial Economics;

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