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Modeling Spatial Dependence And Spatial Heterogeneity In County Yield Forecasting Models

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  • DiRienzo, Cassandra
  • Fackler, Paul L.
  • Goodwin, Barry K.

Abstract

The implications of ignoring potential spatial dependence in county-level yield data are discussed. Spatial dependence in a county-level yield data set is identified and methods for correcting the dependence via spatial weighting matrices and generalized least squares regression are performed. The paper also examines how the spatial dependence declines as the distance between observations increases.

Suggested Citation

  • DiRienzo, Cassandra & Fackler, Paul L. & Goodwin, Barry K., 2000. "Modeling Spatial Dependence And Spatial Heterogeneity In County Yield Forecasting Models," 2000 Annual meeting, July 30-August 2, Tampa, FL 21763, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea00:21763
    DOI: 10.22004/ag.econ.21763
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    File URL: https://ageconsearch.umn.edu/record/21763/files/sp00di01.pdf
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    References listed on IDEAS

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    1. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
    2. 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.
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    Cited by:

    1. Spencer, Nekeisha & Polachek, Solomon, 2015. "Hurricane watch: Battening down the effects of the storm on local crop production," Ecological Economics, Elsevier, vol. 120(C), pages 234-240.

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