具有组群异方差结构的面板数据模型及其应用研究

The Research of Panel Data Model with Grouped Structural Heteroscedasticity and Its Application

面板数据由不同个体的时间序列数据汇聚而成。已有大量研究表明面板数据个体之间存在组群结构,并且普遍存在模型的异方差现象。本文借鉴组群异质性的研究成果,构建模型误差项组群结构的面板数据模型,基于模型假定条件,提出惩罚伪最大似然函数估计法(PQMLE),该方法能够同时进行结构识别和参数估计;证明了估计量具有Oracle渐近性质;蒙特卡洛模拟验证了该方法有效的样本性质;进一步应用该方法对我国股市进行Fama-French三因子模型的实证分析,验证了理论模型的应用效果。

Panel data sets are aggregated from time series data of different individuals. A significant amount of studies have shown that there exists grouped structure between individuals in the panel data set,and the model heteroscedasticity is common. Drawing on the study of group heterogeneity,the paper assumes that the variance of the error term has a group structure. Based on the specific assumptions in the model,this paper proposes a penalty pseudo maximum likelihood function estimation method,which can simultaneously perform parameter estimation and structure recognition,and proves the Oracle asymptotic properties of the estimator.Subsequently,Monte Carlo simulation verifies its effective sample properties. Finally,this method is applied to the parameter estimation of the Fama-French three-factor model in the Chinese stock market,which shows the proposed model performs well.