A Globally Flexible Model for Crop Yields Under Weather Risk-气候风险下作物产量的全球灵活模型

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英文文献：A Globally Flexible Model for Crop Yields Under Weather Risk-气候风险下作物产量的全球灵活模型
英文文献作者：Cooper, Joseph C.,Wallander, Steven
英文文献摘要：
The literature on climate change and crop yields recognizes the need to allow for highly non-linear marginal effects. This study combines these two areas of the literature by using Flexible Fourier Transforms (FFT’s) to ensure flexibility for both the time trend and the weather effects. This study also illustrates how FFT’s can be combined with quantile regression (QR) to provide both robustness to outliers and information on the scale effects of time and weather variables. For U.S. county level data on corn, soybeans, and winter wheat, we estimate the relationship between yield and temperature and precipitation using a traditional parametric expected-yield estimator, our quantile-FFT regression evaluated at the median, and our QR-FFT regression that incorporates information on the tails of the distribution. We find that quadratic terms are not sufficient for capturing nonlinearities in the relationship between yield and the explanatory variables.

关于气候变化和作物产量的文献认识到需要考虑高度非线性的边际效应。本研究使用灵活的傅里叶变换(FFT)结合了这两个领域的文献，以确保时间趋势和天气影响的灵活性。本研究还说明了FFT如何与分位数回归(QR)相结合，以提供对异常值的鲁棒性，以及时间和天气变量的尺度效应方面的信息。对于美国县级玉米、大豆和冬小麦的数据，我们使用传统的参数预期产量估计器估计产量与温度和降水量之间的关系，我们的定量fft回归评估中值，以及包含分布尾部信息的定量fft回归。我们发现二次项是不充分的，以捕获的非线性关系之间的收益率和解释变量。

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