Efficient Estimation of Agricultural Time Series Models with Nonnormal Dependent Variables
提出用扩展的Johnson SU分布近似回归模型中的非正态分布，可处理异方差和自相关，通过蒙特卡洛模拟评估，并以西德克萨斯棉花基差实证说明，相比最小二乘法能显著降低斜率参数估计的方差。
This article proposes using an expanded form of the Johnson S U family as a way to approximate nonnormal distributions in regression models. The distribution is one of the few that allows modeling heteroskedasticity and autocorrelation. The technique is evaluated with Monte Carlo simulation and illustrated through an empirical model of the West Texas cotton basis. Given nonnormality, this technique can substantially reduce the variance of slope parameter estimates relative to least squares procedures.