  In this article, we consider an imputation method to handle missing response
values based on semiparametric quantile regression estimation. In the proposed
method, the missing response values are generated using the estimated
conditional quantile regression function at given values of covariates. We
adopt the generalized method of moments for estimation of parameters defined
through a general estimation equation. We demonstrate that the proposed
estimator, which combines both semiparametric quantile regression imputation
and generalized method of moments, has competitive edge against some of the
most widely used parametric and non-parametric imputation estimators. The
consistency and the asymptotic normality of our estimator are established and
variance estimation is provided. Results from a limited simulation study and an
empirical study are presented to show the adequacy of the proposed method.
