Two-Stage Bounded-lnfluence Estimators for Simultaneous-Equations Models
提出一类针对线性结构模型的估计量，能抵抗重尾扰动、内生或外生变量中的粗差等模型失效问题，通过用有界影响回归替代普通最小二乘回归来修正两阶段最小二乘法，并证明了其稳健性、一致性和渐近正态性。
This article presents a class of estimators for linear structural models that are robust to heavytailed disturbance distributions, gross errors in either the endogenous or exogenous variables, and certain other model failures. The class of estimators modifies ordinary two-stage least squares by replacing each least squares regression by a bounded-influence regression. Conditions under which the estimators are qualitatively robust, consistent, and asymptotically normal are established, and an empirical example is presented.