摘要
本文针对面板数据模型的惩罚似然变量选择问题,比较研究了Lasso、Adaptive Lasso、Bridge和SCAD四种罚函数的渐近性质。模拟结果验证了在面板数据情况下,Adaptive Lasso、Bridge和SCAD的Oracle性质同样成立,且它们在变量选择准确性、参数估计精度和模型预测精度三方面的效果都优于Lasso。为了合理选取调整参数,本文考虑AIC、BIC、GCV、Cp四种准则,通过模拟显示BIC和GCV的表现通常要优于AIC和Cp。作为实证研究,本文在面板数据框架下应用惩罚似然方法对上市公司市盈率影响因素进行选择,以期对股市投资者做出理性投资决策有一定指导价值。
This paper focuses on the methods of penalized likelihood variable selection for the panel data model, and discusses and compares the asymptotic properties of Lasso, Adaptive Lasso, Bridge and SCAD. Through simulations, Adaptive Lasso, Bridge and SCAD are confirmed to have the oracle property and perform better than Lasso on variable selection accuracy, parameters estimation precision as well as model prediction precision. In addition, to properly select the tuning parameters, we consider the criteria AIC, BIC, GCV and Cp and indicate by simulations that tuning based on BIC or GCV in general do better than based on AIC or Cp. As an empirical study, we apply the penalized likelihood methods to selection of the influencing factors on price-earnings ratio of listed companies under the framework of panel data, in order to provide some references to stock investors in making rational investment decisions.
出处
《统计研究》
CSSCI
北大核心
2014年第3期83-89,共7页
Statistical Research
基金
国家自然科学基金青年项目“预测模型的结构化变量选择方法研究”(71301162)
国家社会科学基金“大数据的高维变量选择方法及其应用研究”(13CTJ001)
中国人民大学应用统计科学研究中心自主项目“高维异质性数据的特征选择方法研究”资助
关键词
面板数据
变量选择
惩罚似然
调整参数
Panel Data
Variable Selection
Penalized Likelihood
Lasso
Tuning Parameter
