动态面板阈模型的一种序贯两步估计  

A Two-step Sequential Estimation for Dynamic Panel Threshold Model

作　　者：李仲达[1] 余壮雄[2] 王美今[1] 

机构地区：[1]中山大学岭南学院 [2]暨南大学产业经济研究院

出　　处：《统计研究》2014年第7期72-80,共9页Statistical Research

基　　金：国家自然科学基金项目(71301160;71303264);中央高校基本科研业务费专项资金(10000-3161137);广东省自然科学基金项目(S2013040015332)的资助

摘　　要：动态面板阈模型可以刻画经济变量动态调整过程的非对称性,在实证分析中有广泛的运用,但阈值参数的引入同时增加了参数估计的困难,理论上尚有许多问题没有解决。针对此类模型,本文提出了一种简单而实用的序贯两步估计方法,首先利用格点搜索获得阈值参数的一致估计,基于该参数对数据结构进行合理划分并引入不同类型的矩条件,然后利用广义矩方法获得自回归参数的估计。理论研究与模拟结果均表明,序贯两步估计具有良好的大样本性质和有限样本表现;与现有文献的方法相比,序贯两步估计能够有效避免不同类型参数估计偏差的相互影响,减小估计量的偏差与均方根误差。Dynamic panel threshold model can characterize the asymmetry in the dynamic process of adjustment variables of economic, which is widely used in empirical analysis. However, the inclusion of threshold parameter increases the difficulty of parameter estimation, and leads to many unresolved problems theoretically. For this kind of model, we propose a simple but practicable two-step sequential estimation method. Firstly we make use of grid search to obtain the consistent estimator of the threshold parameter. On this basis, we can correctly split the sample and incorporate different types of moment conditions. Then we estimate the autoregressive parameter by using GMM estimation. The theoretical and simulated results demonstrate that the two-step sequential estimator enjoys good asymptotic properties and finite sample performance; compared to the existing estimators, this estimator cannot only effectively avoid the bias induced by the interaction of different types of estimators but also reduce the root mean squared error.

关 键 词：动态面板阈模型 动态调整 阈值效应 结构变化 序贯两步估计 

分 类 号：O212[理学—概率论与数理统计]