Small‐Sample Evaluation of Mean‐Variance Production Function Estimators
通过蒙特卡洛实验研究均值-方差生产函数估计量的小样本性质，发现均值估计效率随均值与方差成分的迭代而提高，方差估计偏差在第二阶段后减小，且受异方差程度、符号和样本量影响。
Abstract Production functions have been shown useful for characterizing input effects on both the mean and variability of yield. Monte carlo experiments are used here to investigate small‐sample properties of selected mean‐variance production function estimators. Estimation efficiency in the mean is found to improve with iteration on the mean and variance components. Although efficiency in the variance is greatest at the first stage, bias in the variance diminishes through at least the second stage. These effects are influenced by the degree and sign of heteroscedasticity and by sample size.