Effect of Nonnormality on the Estimation of a Single Structural Equation with Structural Change
研究了非正态性下三种估计量（有限信息最大似然估计、两阶段最小二乘型估计和最小距离估计）在存在结构变化的单一结构方程中的渐近性质，发现最小距离估计优于其他估计量。
Effect of nonnormality on the asymptotic property of three estimators of a single structural equation with structural change is examined. The three estimators are the limited information maximum likelihood estimator, derived under normality and equality of structural variances in different samples, given by Hodoshima, a two-stage least squares type estimator due to Barten and Bronsard, and a minimum distance estimator presented here. Normality is relaxed but the equality assumption of structural variances is retained. Under nonnormality the limited information maximum likelihood estimator is consistent but may not be efficient relative to the Barten and Bronsard's estimator. A sufficient condition is given under which the limited information maximum likelihood estimator dominates the Barten and Bronsard's estimator in terms of the asymptotic covariance matrix. The minimum distance estimator dominates other estimators.