A SIMPLE ITERATIVE Z-ESTIMATOR FOR SEMIPARAMETRIC MODELS
提出一种新的迭代估计算法，用于传统方法难以计算Z估计量的半参数模型，仅利用Hessian矩阵中易计算部分的曲率信息，并在正则条件下证明估计量的相合性和渐近正态性，通过缺失协变量分位数回归和未知条件均值GARCH-in-mean两个例子展示其适用性。
We propose a new iterative estimation algorithm for use in semiparametric models where calculation of Z-estimators by conventional means is difficult or impossible. Unlike a Newton–Raphson approach, which makes use of the entire Hessian, this approach only uses curvature information associated with portions of the Hessian that are relatively easy to calculate. Consistency and asymptotic normality of estimators obtained from this algorithm are established under regularity conditions and an information dominance condition. Two specific examples, a quantile regression model with missing covariates and a GARCH-in-mean model with conditional mean of unknown functional form, demonstrate the applicability of the algorithm. This new approach can be interpreted as an extension of the maximization by parts estimation approach to semiparametric models.