关键词: Elastic Net, 分位数回归, 贝叶斯估计, 面板数据

Abstract: This paper for the first time applies Elastic Net, a penalty method for highly correlated variables, to Bayesian quantile regression model for panel data. The posterior distribution of all parameters is deduced based on asymmetric Laplace prior distribution, and Gibbs sampling is constructed. To verify the validity of our model, this paper compares Bayesian Elastic Net Quantile Regression for panel data（BQR.EN）with Bayesian Adaptive Lasso Quantile Regression for panel data (BALQR), Bayes Lasso Quantile Regression for panel data (BLQR), Bayesian Quantile Regression for panel data (BQR) in all kinds of situations. The results show that the BQR.EN model is suitable for data with high correlation, high data dimension and peak and thick tail distribution characteristics. Furthermore, this paper conducts a simulation for BQR.EN model under different disturbance assumptions and different sample sizes, which verifies the robustness and small sample characteristic of the new method. Finally, this paper chooses the economic value added of Internet financial listed companies as an empirical research object to test the new method’s ability of parameter estimation and variable selection in practical problems, and the empirical results are in line with the expected objectives.

Key words: Elastic Net, Quantile Regression, Bayesian Parameter Estimation, Panel Data