关键词: 面板数据, 变量选择, 惩罚似然, Lasso, 调整参数

Abstract: 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.

Key words: Panel Data, Variable Selection, Penalized Likelihood, Lasso, Tuning Parameter