Estimating Yield Distributions with a Stochastic Trend and Nonnormal Errors
提出用随机趋势模型估计产量分布的中心趋势，并通过反双曲正弦分布处理非正态性，纠正了玉米产量的偏度和峰度问题。
Abstract Randomness in crop yields can be decomposed into two broad modeling focuses: the estimation of the mean or central tendency of the distribution and the dispersion around that central tendency. We propose modeling the central tendency of the distribution with a stochastic trend model and allowing for nonnonnality within the stochastic trend through an inverse hyperbolic sine distribution. Results are consistent with this construction. First, residuals around the stochastic trend model are found to be non normal. Second, the inverse hyperbolic sine modification of the stochastic trend model corrects both skewness and kurtosis of corn yields.