POOLING ESTIMATES WITH DIFFERENT RATES OF CONVERGENCE: A MINIMUMχ2APPROACH WITH EMPHASIS ON A SOCIAL INTERACTIONS MODEL
扩展了经典的最小距离方法，用于合并具有不同收敛速度的估计值，证明了估计量的一致性和渐近正态性，并提供了约束条件的拟合优度检验，特别适用于社会互动模型的估计与检验。
This paper considers the extension of the classical minimum distance approach for the pooling of estimates with various rates of convergence. Under a setting where relatively high rates of convergence can be attained, the minimum distance estimators are shown to be consistent and asymptotically normally distributed. The constrained estimates can be efficient relative to the unconstrained ones. The minimized distance function is shown to be asymptotically χ 2 -distributed, and can be used as a goodness-of-fit test for the constraints. As the extension is motivated by some social interactions models, which are of interest in their own right, we discuss this approach for the estimation and testing of a social interactions model.