A SIMPLE NONPARAMETRIC APPROACH FOR ESTIMATION AND INFERENCE OF CONDITIONAL QUANTILE FUNCTIONS

In this paper, we present a new nonparametric method for estimating conditional quantile function and develop its weak convergence theory. The proposed estimator is computationally easy to implement automatically ensures monotonicity by construction. For inference, propose use residual bootstrap method. Our Monte Carlo simulations show that compares well with the check-function-based in terms of estimation mean squared error. confidence bands yield adequate coverage probabilities. An empirical example uses dataset Canadian high school graduate earnings, illustrating usefulness applications.