Improving the Maximum Likelihood Estimate in Linear Functional Relationships for Alternative Parameter Sequences
提出一种改进线性函数关系中最大似然估计的方法，通过结合最小二乘估计消除偏差，并推导两种参数序列下的估计分布，证明改进估计的均方误差更小，对大规模联立计量模型有应用价值。
Abstract We propose an improvement of the maximum likelihood (ML) estimate in linear functional relationships. The improved estimate is a linear combination of the ML and the least squares estimate so as to remove the bias of the former. Approximations to the distribution of the estimate are derived for two alternative parameter sequences: a sequence in which the noncentrality parameter (the spread of the true values) increases while the number of observations stays fixed, and that in which the number of observations increases. The mean squared errors of the improved estimate, in terms of its asymptotic distributions, are obtained and shown to be smaller than those of the ML. Implications to large-scale simultaneous econometric models are also given.