面板数据的自适应惩罚分位回归方法
发布时间：2019-04-09 14:30
【摘要】：传统的面板数据是从均值角度进行研究,但这会受经典假设条件的约束.而考虑面板数据的分位回归模型,可以更加全面地描述响应变量条件分布的全貌.文章引入自适应惩罚函数构造了自适应惩罚的分位回归面板数据方法,并证明所提出的估计量具有大样本性质.蒙特卡洛模拟结果显示该方法相对于均值回归更具优势,是处理面板数据的有效手段.文章最后对我国居民交通通讯消费进行案例分析,得到了有利于决策的参考信息.
[Abstract]:The traditional panel data is studied from the mean point of view, but it is constrained by the classical assumptions. The quantile regression model considering panel data can more fully describe the conditional distribution of response variables. In this paper, an adaptive penalty function is introduced to construct a quantile regression panel data method for adaptive penalty, and it is proved that the proposed estimator has a large sample property. Monte Carlo simulation results show that this method is superior to mean regression and is an effective method to process panel data. Finally, the paper analyzes the case of communication consumption of residents in our country, and obtains the reference information which is beneficial to the decision-making.
【作者单位】： 中国人民大学应用统计科学研究中心中国人民大学统计学院;兰州财经大学统计学院;新疆财经大学统计与信息学院;
【基金】：中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目成果(15XNL008)资助课题
【分类号】：O212.1
，
本文编号：2455263
[Abstract]:The traditional panel data is studied from the mean point of view, but it is constrained by the classical assumptions. The quantile regression model considering panel data can more fully describe the conditional distribution of response variables. In this paper, an adaptive penalty function is introduced to construct a quantile regression panel data method for adaptive penalty, and it is proved that the proposed estimator has a large sample property. Monte Carlo simulation results show that this method is superior to mean regression and is an effective method to process panel data. Finally, the paper analyzes the case of communication consumption of residents in our country, and obtains the reference information which is beneficial to the decision-making.
【作者单位】： 中国人民大学应用统计科学研究中心中国人民大学统计学院;兰州财经大学统计学院;新疆财经大学统计与信息学院;
【基金】：中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目成果(15XNL008)资助课题
【分类号】：O212.1
，
本文编号：2455263
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