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

Published online by Cambridge University Press:  26 January 2015

Shu-Ling Chen  and
Mario J. Miranda
Shu-Ling Chen
Affiliation:
Department of Quantitative Finance, National Tsing Hua University, Taiwan
Mario J. Miranda
Affiliation:
Department of Agricultural, Environmental and Development Economics, The Ohio State University, Columbus, OH
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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.

Keywords

actuarial ratingadverse selectioncottoncrop insurancegroup risk planregime-switchingyield distributionQ10Q14Q18
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 40 , Issue 1 , April 2007 , pp. 239 - 252
Copyright
Copyright © Southern Agricultural Economics Association 2008

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References

Atwood, J., Shaik, S., and Watts, M. “Are Crop Yields Normally Distributed? A Reexamination.” American Journal of Agricultural Economics 85(November 2003):888–901.Google Scholar
Day, R.H. “Probability Distributions of Field Crop Yields.” Journal of Farm Economics 47(August 1965):713–41.Google Scholar
Gallagher, P. “U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances.” American Journal of Agricultural Economics 69(November 1987):796–803.Google Scholar
Glauber, J.W. “Crop Insurance Reconsidered.” American Journal of Agricultural Economics 86(December 2004):1179–95.CrossRefGoogle Scholar
Goldfeld, S.M., and Quandi, R.E. Nonlinear Methods in Econometrics. Amsterdam: North-Holland, 1972.Google Scholar
Goldfeld, S.M., and Quandi, R.E. “A Markov Model for Switching Regressions.” Journal of Econometrics 1(1973):3–16.Google Scholar
Goodwin, B.K. “An Empirical Analysis of the Demand for Multiple Peril Crop Insurance.” American Journal of Agricultural Economics 75(May 1993):425–34.CrossRefGoogle Scholar
Goodwin, B.K., and Ker, A.P. “Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts.” American Journal of Agricultural Economics 80(February 1998):139–53.Google Scholar
Goodwin, B.K., and Mahul, O. “Risk Modeling Concepts Relating to the Design and Rating of Agricultural Insurance Contract.” Working paper 3392, World Bank Policy Research, September 2004.Google Scholar
Goodwin, B.K., Vandeveer, M.L., and Deal, J.L. “An Empirical Analysis of Acreage Effects of Participation in the Federal Crop Insurance Program.” American Journal of Agricultural Economics 86(November 2004):1058–77.Google Scholar
Just, R.E., and Weninger, Q. “Are Crop Yields Normally Distributed?” American Journal of Agricultural Economics 81(May 1999):287–304.CrossRefGoogle Scholar
Ker, A.P., and Coble, K.H. “Modeling Conditional Yield Densities.” American Journal of Agricultural Economics 85(May 2003):291–304.Google Scholar
Ker, A.P., and Goodwin, B.K. “Nonparametric Estimation of Crop Insurance Rates Revisited.” American Journal of Agricultural Economics 82(May 2000):463–78.Google Scholar
Miranda, M.J. “Area-Yield Crop Insurance Reconsidered.” American Journal of Agricultural Economics 73(May 1991):233–42.CrossRefGoogle Scholar
National Oceanic & Atmospheric Administration National Climatic Data Center, Internet site: www.ncdc.noaa.gov/oa/climate/onlineprod/drought/xmgr.html (Accessed August 30, 2007).Google Scholar
Ozaki, V.A., Ghosh, S.K., Goodwin, B.K., and Shirota, R. “Spatio-Temporal Modeling of Agricultural Yield Data with an Application to Pricing Crop Insurance Contracts.” Paper presented at the conference of American Agricultural Economics Association Annual Meeting, Providence, Rhode Island, July 24–27 2005.Google Scholar
Quandt, R.E. “A New Approach to Estimating Switching Regressions.” Journal of the American Statistical Association 67(June 1972):306–10.CrossRefGoogle Scholar
Ramirez, O.A., Misra, S., and Field, J. “Crop-Yield Distributions Revisited.” American Journal of Agricultural Economics 85(February 2003):108–20.Google Scholar
Sherrick, B.J., Zanini, F.C., Schnitkey, G.D., and Irwin, S.H. “Crop Insurance Valuation Under Alternative Yield Distributions.” American Journal of Agricultural Economics 86(May 2004):406–19.CrossRefGoogle Scholar
Skees, J.R., Black, J.R., and Barnett, B.J. “Designing and Rating an Area Yield Crop Insurance Contract.” American Journal of Agricultural Economics 79(May 1997):430–38.CrossRefGoogle Scholar
Skees, J.R., and Reed, M.R. “Rate Making for Farm-Level Crop Insurance: Implication for Adverse Selection.” American Journal of Agricultural Economics 68(August 1986):653–59.Google Scholar
Taylor, C.R. “Two Practical Procedures for Estimating Multivariate Nonnormal Probability Density Functions.” American Journal of Agricultural Economics 72(February 1990):210–17.Google Scholar
U.S. Department of Agriculture National Agricultural Statistics Service, Internet site: www.nass.usda.gov/index.asp (Accessed August 30, 2007).Google Scholar
U.S. Department of Agriculture Risk Management Agency, Internet site: www.rma.usda.gov/ (Accessed August 30, 2007).Google Scholar