哈尔滨商业大学学报（自然科学版）2019，Vol.35Issue(1)：105-108,4.
面板数据复合分位数回归模型的渐进相对效率
Asymptotic relative efficiency of panel data composite quantile regression model
刘燕 1范永辉1
作者信息
- 1. 天津师范大学 数学科学学院,天津 300387
- 折叠
摘要
Abstract
In this paper,the asymptotic relative efficiency of regression coefficient estimation was studied for the composite quantile regression model of individual fixed effect in panel data.The ratio of trace of covariance matrix of composite quantile regression estimation and least squares estimation was calculated. The results showed that the asymptotic phase of composite quantile regression was relative to that of least squares method. The ratio of efficiency to efficiency was more than 70％. This paper also applied Zous idea of adaptive lasso proposed in 2008 to the composite quantile regression model of individual fixed effect in panel data,constructed the adaptive lasso penalty composite quantile regression estimation,and proved the asymptotic nature of the estimation under appropriate conditions.关键词
面板数据/复合分位数回归/渐进相对效率/适应性lasso/变量选择/渐进正态Key words
panel data/composite quantile regression/asymptotic relative efficiency/adaptive lasso/variable selection/asymptotic normality分类
数理科学引用本文复制引用
刘燕,范永辉..面板数据复合分位数回归模型的渐进相对效率[J].哈尔滨商业大学学报（自然科学版）,2019,35(1):105-108,4.
