A New Instrumental-Type Estimator for Quantile Regression Models

Li Tao, Lingnan Tai, Manling Qian () and Maozai Tian
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Li Tao: School of Information, Beijing Wuzi University, Beijing 101149, China
Lingnan Tai: School of Economics and Management, The Open University of China, Beijing 100039, China
Manling Qian: School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
Maozai Tian: Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China

Mathematics, 2023, vol. 11, issue 15, 1-26

Abstract: This paper proposes a new instrumental-type estimator of quantile regression models for panel data with fixed effects. The estimator is built upon the minimum distance, which is defined as the weighted average of the conventional individual instrumental variable quantile regression slope estimators. The weights assigned to each estimator are determined by the inverses of their corresponding individual variance–covariance matrices. The implementation of the estimation has many advantages in terms of computational efforts and simplifies the asymptotic distribution. Furthermore, the paper shows consistency and asymptotic normality for sequential and simultaneous asymptotics. Additionally, it presents an empirical application that investigates the income elasticity of health expenditures.

Keywords: quantile regression; instrumental variables; minimum distance estimator; panel data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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