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Heteroskedasticity In Crop Yield Models

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  • Yang, Seung-Ryong
  • Koo, Won W.
  • Wilson, William W.

Abstract

This study examines three alternative models of correcting for heteroskedasticity in wheat yield: the time trend variance, the GARCH, and an econometric model that includes the potential sources of heteroskedasticity. Nonnested test results suggest that modeling the sources of heteroskedasticity is the preferred procedure. Including potential sources of heteroskedasticity as explanatory variables removed the heteroskedasticity in the sample wheat yields. The results also suggest that the GARCH specification is a promising model of correcting for heteroskedasticity when the sources cannot be identified. The time trend variance model alone may misspecify the true variance structure.

Suggested Citation

  • Yang, Seung-Ryong & Koo, Won W. & Wilson, William W., 1992. "Heteroskedasticity In Crop Yield Models," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 17(1), pages 1-7, July.
  • Handle: RePEc:ags:jlaare:30738
    DOI: 10.22004/ag.econ.30738
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    1. Aradhyula, Satheesh V. & Holt, Matthew T., 1988. "Garch Time-Series Models: An Application To Retail Livestock Prices," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 13(2), pages 1-10, December.
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    3. A. J. Singh & D. Byerlee, 1990. "Relative Variability In Wheat Yields Across Countries And Over Time," Journal of Agricultural Economics, Wiley Blackwell, vol. 41(1), pages 21-32, January.
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    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Chavas, Jean-Paul & Kim, Kwansoo & Lauer, Joseph G. & Klemme, Richard M. & Bland, William L., 2001. "An Economic Analysis Of Corn Yield, Corn Profitability, And Risk At The Edge Of The Corn Belt," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 26(1), pages 1-18, July.
    2. Aizhen Li & Boris E. Bravo-Ureta & David K. Okello & Carl M. Deom & Naveen Puppala, 2013. "Groundnut Production and Climatic Variability: Evidence from Uganda," Working Papers 17, University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy.
    3. David le Bris & William N. Goetzmann & Sébastien Pouget, 2014. "Testing Asset Pricing Theory on Six Hundred Years of Stock Returns: Prices and Dividends for the Bazacle Company from 1372 to 1946," NBER Working Papers 20199, National Bureau of Economic Research, Inc.
    4. Ozaki, Vitor & Campos, Rogério, 2017. "Reduzindo a Incerteza no Mercado de Seguros: Uma Abordagem via Informações de Sensoriamento Remoto e Atuária," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 71(4), December.
    5. Revoredo-Giha, Cesar & Gaupp, Franziska, 2014. "Weather and their effect on crop yields in Scotland 1935-2012," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 183034, European Association of Agricultural Economists.
    6. le Bris, David & Goetzmann, William N. & Pouget, Sébastien, 2019. "The present value relation over six centuries: The case of the Bazacle company," Journal of Financial Economics, Elsevier, vol. 132(1), pages 248-265.
    7. Kapiamba, Luabeya F., 2005. "Modeling Heteroskedasticity of Crop Yield Distributions: Implications for Normality," 2005 Annual meeting, July 24-27, Providence, RI 19475, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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