纵向数据广义部分线性模型的惩罚GMM估计

摘要

对纵向数据的部分线性模型,通常的做法是用样条方法或者核方法逼近非参数部分,然后再用广义估计方程的估计方法去估计参数部分.本文使用P-样条拟合非参数函数,对不同的矩条件用不同的广义矩方法对模型的参数和非参数进行估计,并且给出了估计量的大样本性质；并用计算机模拟和实例证明了当模型中存在不同的矩条件时,采用不同的惩罚广义矩方法可以显著地提高估计精度.%For the analysis of partial linear model with longitudinal data, the general procedure is to fit the non-parametric part with kernel or spline estimation, followed by generalized linear model estimating frame. In this paper, we fit the nonparametric part with P-spline, and estimate the parametrical and nonpara-metric part with different Generalized method of moments estimation for different moment conditions, implemented by the proof of the asymptotical properties for the estimator, which is also .been proved by simulation and illustrative example, from which we can also find out that different penalized general method of moments estimations for different moment conditions perform more efficiently.