Asymptotic Expansions of the Distributions of Estimators in a Linear Functional Relationship and Simultaneous Equations
Asymptotic Expansions of the Distributions of Estimators in a Linear Functional Relationship and Simultaneous Equations
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线性函数关系和联立方程中估计量分布的渐近展开
DOI：
10.1080/01621459.1980.10477535
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发表时间：
1980
影响因子：
3.7
通讯作者：
N. Kunitomo
中科院分区：
文献类型：
--
作者：
N. Kunitomo
摘要在线性函数关系模型中，当样本容量无限增加时，我们得到了极大似然（ML）估计和普通最小二乘（OLS）估计的分布的渐近展开式。这些展开式等价于有限信息最大似然（LIML）估计的分布的渐近展开式（当协方差已知在比例常数内时）和两阶段最小二乘（TSLS）估计的分布的渐近展开式（当被排除的外生变量的数目增加时）。
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.