Title: A simple and efficient estimation method for models with nonignorable missing data
Authors: Chunrong Ai - University of Florida (United States)
Oliver Linton - University of Cambridge (United Kingdom)
Zheng Zhang - Renmin University of China (China) [presenting]
Abstract: A simple and efficient estimation procedure is proposed for the model with non-ignorable missing data recently studied. The previous semiparametrically efficient estimator requires explicit nonparametric estimation, and thus, it suffers from the curse of dimensionality and requires a bandwidth selection. We propose an estimation method based on the Generalized Method of Moments (hereafter GMM). Our method is consistent and asymptotically normal regardless of the number of moments chosen. Furthermore, if the number of moments increases appropriately our estimator can achieve the semiparametric efficiency bound derived previously, but under weaker regularity conditions. Moreover, our proposed estimator and its consistent covariance matrix are easily computed with the widely available GMM package. We propose two databased methods for selection of the number of moments. A small scale simulation study reveals that the proposed estimation indeed outperforms the existing alternatives in finite samples.