Asymptotic Expansions of the Distributions of Estimators in a Linear Functional Relationship and Simultaneous Equations
推导了线性函数关系模型中最大似然估计和普通最小二乘估计的分布渐近展开，结果等价于联立方程系统中已知协方差时的有限信息最大似然估计和两阶段最小二乘估计的渐近展开。
Abstract We derive asymptotic expansions of the distributions of the maximum likelihood (ML) estimator and the ordinary least squares (OLS) estimator in a linear functional relationship model as the sample size increases infinitely. These expansions are equivalent to the asymptotic expansions of the distributions of the limited information maximum likelihood (LIML) estimator when the covariance is known to within a proportionality constant and the two-stage least squares (TSLS) estimator as the number of excluded exogenous variables increases in a simultaneous equations system.