Comparing Single-Equation Estimators in a Simultaneous Equation System
Comparing Single-Equation Estimators in a Simultaneous Equation System
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比较联立方程组中的单方程估计器
DOI：
10.1017/s026646660001135x
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发表时间：
1986
影响因子：
0.8
通讯作者：
K. Morimune
中科院分区：
文献类型：
--
作者：
T. W. Anderson;N. Kunitomo;K. Morimune
当干扰方差趋于零时，或者当样本量无限增加时，估计器的比较是基于它们的均方误差和它们的概率集中，通过它们的分布的渐近展开来计算的。这些估计包括k类估计(有限信息最大似然、两阶段最小二乘和普通最小二乘)及其线性组合，以及对有限信息最大似然估计和几种贝叶斯估计的修正。给出了渐近均方误差与概率集中之间的许多不等式。在中值无偏估计中，有限信息极大似然估计优于中值无偏固定k类估计。
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.