Two-Stage Estimation of Partially Linear Varying Coefficient Quantile Regression Model with Missing Data

Shuanghua Luo (), Yuxin Yan and Cheng-yi Zhang ()
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Shuanghua Luo: School of Science, Xi’an Polytechnic University, Xi’an 710048, China
Yuxin Yan: School of Science, Xi’an Polytechnic University, Xi’an 710048, China
Cheng-yi Zhang: School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China

Mathematics, 2024, vol. 12, issue 4, 1-15

Abstract: In this paper, the statistical inference of the partially linear varying coefficient quantile regression model is studied under random missing responses. A two-stage estimation procedure is developed to estimate the parametric and nonparametric components involved in the model. Furthermore, the asymptotic properties of the estimators obtained are established under some mild regularity conditions. In addition, the empirical log-likelihood ratio statistic based on imputation is proposed, and it is proven that this statistic obeys the standard Chi-square distribution; thus, the empirical likelihood confidence interval of the parameter component of the model is constructed. Finally, simulation results show that the proposed estimation method is feasible and effective.

Keywords: composite quantile regression; partially linear varying coefficient model; empirical likelihood; confidence interval; missing responses (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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