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Modeling Texas Dryland Cotton Yields, With Application to Crop Insurance Actuarial Rating

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  • Chen, Shu-Ling
  • Miranda, Mario J.

Abstract

Texas dryland upland cotton yields have historically exhibited greater variation and more distributional irregularities than the yields of other crops, raising concerns that conventional parametric distribution models may generate biased or otherwise inaccurate crop insurance premium rate estimates. Here, we formulate and estimate regime-switching models for Texas dryland cotton yields in which the distribution of yield is conditioned on local drought conditions. Our results indicate that drought-conditioned regime-switching models provide a better fit to Texas county-level dryland cotton yields than conventional parametric distribution models. They do not, however, generate significantly different Group Risk Plan crop insurance premium rate estimates.

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File URL: http://purl.umn.edu/45522
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Bibliographic Info

Article provided by Southern Agricultural Economics Association in its journal Journal of Agricultural and Applied Economics.

Volume (Year): 40 (2008)
Issue (Month): 01 (April)
Pages:

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Handle: RePEc:ags:joaaec:45522

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Web page: http://www.saea.org/jaae/jaae.htm
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Related research

Keywords: actuarial rating; adverse selection; cotton; crop insurance; group risk plan; regime-switching; yield distribution; Agribusiness; Crop Production/Industries; Farm Management; Q10; Q14; Q18;

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References

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  1. Bruce J. Sherrick & Fabio C. Zanini & Gary D. Schnitkey & Scott H. Irwin, 2004. "Crop Insurance Valuation under Alternative Yield Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 406-419.
  2. Ozaki, Vitor Augusto & Ghosh, Sujit K. & Goodwin, Barry K. & Shirota, Ricardo, 2005. "Spatio-Temporal Modeling Of Agricultural Yield Data With An Application To Pricing Crop Insurance Contracts," 2005 Annual meeting, July 24-27, Providence, RI 19142, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  3. Alan P. Ker & Keith Coble, 2003. "Modeling Conditional Yield Densities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 291-304.
  4. Alan P. Ker & Barry K. Goodwin, 2000. "Nonparametric Estimation of Crop Insurance Rates Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(2), pages 463-478.
  5. Jerry R. Skees & J. Roy Black & Barry J. Barnett, 1997. "Designing and Rating an Area Yield Crop Insurance Contract," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 430-438.
  6. Octavio A. Ramirez & Sukant Misra & James Field, 2003. "Crop-Yield Distributions Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 108-120.
  7. Joseph W. Glauber, 2004. "Crop Insurance Reconsidered," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(5), pages 1179-1195.
  8. Barry K. Goodwin & Alan P. Ker, 1998. "Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 139-153.
  9. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
  10. Barry K. Goodwin & Monte L. Vandeveer & John L. Deal, 2004. "An Empirical Analysis of Acreage Effects of Participation in the Federal Crop Insurance Program," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1058-1077.
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