ESTIMATORS FOR PERSISTENT AND POSSIBLY NONSTATIONARY DATA WITH CLASSICAL PROPERTIES
提出一种基于矩的非线性估计量，无论驱动过程的持久性如何，该估计量都是根T一致且一致渐近正态的，适用于线性自回归模型、线性预测回归及某些非线性动态模型。
This paper considers a moments based non-linear estimator that is root-T consistent and uniformly asymptotically normal irrespective of the degree of persistence of the forcing process. These properties hold for linear autoregressive models, linear predictive regressions, as well as certain non-linear dynamic models. Asymptotic normality is obtained because the moments are chosen so that the objective function is uniformly bounded in probability and that a central limit theorem can be applied.