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A New Instrumental-Type Estimator for Quantile Regression Models

Author

Listed:
  • 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
    School of Statistics and Information, Xinjiang University of Finance and Economics, Urumqi 830026, China)

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.

Suggested Citation

  • Li Tao & Lingnan Tai & Manling Qian & Maozai Tian, 2023. "A New Instrumental-Type Estimator for Quantile Regression Models," Mathematics, MDPI, vol. 11(15), pages 1-26, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3412-:d:1210952
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