Generalized Method of Moments Estimator Based On Semiparametric Quantile Regression Imputation
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 re-sponse 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 de_ned through a general estimation equation. We demonstrate that the proposedestimator, which combines both semiparametric quantile regression imputation and generalized method of moments, has competitive edge against some of the most widely used parametric and nonparametric 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.
This preprint was published as Senniang Chen and Ciny Yu, "Generalized Method of Moments Estimator Based On Semiparametric Quantile Regression Imputation".