Comparing Single-Equation Estimators in a Simultaneous Equation System
基于均方误差和概率集中度，比较了k类估计量（如有限信息最大似然、两阶段最小二乘、普通最小二乘）及其线性组合、贝叶斯估计量等，给出了渐近均方误差和概率集中度的许多不等式。
Comparisons of estimators are made on the basis of their mean squared errors and their concentrations of probability computed by means of asymptotic expansions of their distributions when the disturbance variance tends to zero and alternatively when the sample size increases indefinitely. The estimators include k -class estimators (limited information maximum likelihood, two-stage least squares, and ordinary least squares) and linear combinations of them as well as modifications of the limited information maximum likelihood estimator and several Bayes' estimators. Many inequalities between the asymptotic mean squared errors and concentrations of probability are given. Among medianunbiasedestimators, the limited information maximum likelihood estimator dominates the median-unbiased fixed k -class estimator.