摘要
建模经济学领域中的面板数据,异方差性在所难免.两阶段估计方法是一种较好的研究异方差性的手段,在进行样本分组时,如果仅选定一个自变量作为依据,会导致信息量不完整.本文提出了用变量选择的方法筛选出用于分组的几个变量,之后用κ均值方法进行聚类,进而实现对样本的类别划分,从而可以得到异方差估计.实证显示:在异方差估计精度和拟合值方面,本文提出的方法在有效性和可行性方面优势明显.
The heteroscedasticity is inevitable for the panel data modeling in economics.The two-stage estimation method is a better means to study the heteroscedasticity,in which the basis is to select only one independent variable for samples grouping,it can cause the information used is incomplete.In this paper,we propose to select several variables for grouping using variable selection method,then k-mean algorithm is used to cluster,so the samples classi cation can be achieved and the heteroscedasticity estimation can be obtained.The results of real example analysis show that the method presented in this paper has obvious advantages in e ectiveness and feasibility.
作者
李顺勇
钱宇华
张晓琴
牛建永
LI ShunYong;QIAN YuHua;ZHANG XiaoQin;NIU JianYong(School of Mathematical Sciences,Shanxi University,Taiyuan,030006,China;School of Computer and Information Technology,Shanxi University,Taiyuan,030006,China;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan,030006,China)
出处
《应用概率统计》
CSCD
北大核心
2018年第2期191-200,共10页
Chinese Journal of Applied Probability and Statistics
基金
国家自然科学基金项目(批准号:61573229)
山西省高等学校教学改革创新项目(批准号:J2017002)
山西省回国留学人员科研资助项目(批准号:2017-020)
山西省基础研究计划项目(批准号:201701D121004)资助
关键词
异方差模型
变量选择
K均值
两阶段估计
heteroscedastic model
variable selection
k-mean algorithm
two-stage estimation
