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Spatially Smoothed Kernel Densities with Application to Crop Yield Distributions

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Abstract

This study is motivated by the estimation of many crop yield densities, each with a small number of observations. These densities tend to resemble one another if they are spatially proximate. To gain flexibility and improve efficiency, we propose kernel-based estimators refined by empirical likelihood probability weights derived under spatially smoothed moment conditions. We construct spatially smoothed moments based on spline functions, which are robust to outliers and readily customizable. We use these methods to estimate the corn yield distributions of Iowa counties and to predict the premiums of crop insurance programs. Monte Carlo simulations and an empirical application demonstrate the good performance and usefulness of the proposed methods.

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Acknowledgements

Kuangyu Wen’s research is supported by the Fundamental Research Funds for the Central Universities, HUST: 2019WKYXQN058.

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Authors and Affiliations

  1. School of Economics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Kuangyu Wen

  2. Department of Agricultural Economics, Texas A&M University, College Station, TX, 77843, USA

    Ximing Wu & David J. Leatham

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  1. Kuangyu Wen
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  2. Ximing Wu
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  3. David J. Leatham
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Correspondence to Kuangyu Wen.

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Wen, K., Wu, X. & Leatham, D.J. Spatially Smoothed Kernel Densities with Application to Crop Yield Distributions. JABES 26, 349–366 (2021). https://doi.org/10.1007/s13253-021-00442-6

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  • DOI: https://doi.org/10.1007/s13253-021-00442-6

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