Homogeneity Pursuit in the Functional-Coefficient Quantile Regression Model for Panel Data with Censored Data

Li Lu (), Xia Yue (), Ren Shuyi () and Yang Xiaorong ()
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Li Lu: College of Data Science, Jiaxing University, Jiaxing, China
Xia Yue: School of Statistics and Mathematics, Collaborative Innovation Centre of Statistical Data, Engineering Technology and Application, 12625 Zhejiang Gongshang University , Hangzhou, China
Ren Shuyi: School of Statistics and Mathematics, Collaborative Innovation Centre of Statistical Data, Engineering Technology and Application, 12625 Zhejiang Gongshang University , Hangzhou, China
Yang Xiaorong: School of Statistics and Mathematics, Collaborative Innovation Centre of Statistical Data, Engineering Technology and Application, 12625 Zhejiang Gongshang University , Hangzhou, China

Studies in Nonlinear Dynamics & Econometrics, 2025, vol. 29, issue 3, 323-348

Abstract: Homogeneity identification of panel data models has been popular in the literature in recent years. Most of the existing works only focus on the complete data case. This paper considers a functional-coefficient quantile regression model for panel data with homogeneity when its response variables are subject to censoring. In particular, we consider a more general censoring framework, i.e. different types of censoring are allowed to occur in the model simultaneously. For this, a “three-stage” method is proposed, which includes the preliminary estimation of subject-specific function coefficients based on data augmentation, the identification of group structure over subjects by clustering, and post-grouping estimation of function coefficients. Simulation studies considering the left-, right-, and double-censored data, are carried out to verify the finite-sample properties of the proposed method. Simulation results show that our method gives comparable performance to the complete data case. The application to the bank stock data further illustrates the practical advantages of this method.

Keywords: functional-coefficient model; panel data; censored data; homogeneity pursuit; data augmentation; quantile regression (search for similar items in EconPapers)
JEL-codes: C14 C15 C23 C24 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/snde-2023-0024

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