惩罚加权复合分位数回归方法在固定效应面板数据中的应用研究

doi:10.16088/j.issn.1001-6600.2024111001

Abstract

Abstract: Panel data can explore and explain the dynamic changes and heterogeneity differences behind the data, making it a research hotspot in many fields.This article addresses the issue of variable selection in panel data with individual fixed effects. Firstly, a filter matrix is introduced to eliminate the fixed effects of panel data, and then estimates the regression coefficients and conducts variable selection through the adaptive LASSO-penalized weighted composite quantile regression method. Secondly, the weighted composite quantile regression applies data-driven weights to each different quantile regression. Thirdly, the results of numerical simulations indicate that this method outperforms the adaptive LASSO-penalized composite quantile regression method and the least squares method in both estimation accuracy and variable selection accuracy. Finally, the proposed method is applied to analyze international economic panel data, and the results show that the goodness of fit of the proposed method is higher than that of the other two methods.

Key words: fixed effect, panel data, weighted composite quantile regression, variable selection

CLC Number: O212.4

JIANG Yunlu, LU Huijie, HUANG Xiaowen. Application Research of Penalized Weighted Composite Quantile Regression Method in Fixed Effects Panel Data[J].Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(6): 120-127.

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Application Research of Penalized Weighted Composite Quantile Regression Method in Fixed Effects Panel Data

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