A New Instrumental-Type Estimator for Quantile Regression Models

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