GMM with Multiple Missing Variables
研究了在非单调随机缺失条件下矩条件模型的有效估计，提出一种可直接用于构建高效半参数GMM估计量的闭式影响函数，蒙特卡洛实验和实证研究均显示其优于标准方法。
We consider efficient estimation in moment conditions models with non-monotonically missing-at-random (MAR) variables. A version of MAR point-identifies the parameters of interest and gives a closed-form efficient influence function that can be used directly to obtain efficient semi-parametric generalized method of moments (GMM) estimators under standard regularity conditions. A small-scale Monte Carlo experiment with MAR instrumental variables demonstrates that the asymptotic superiority of these estimators over the standard methods carries over to finite samples. An illustrative empirical study of the relationship between a child's years of schooling and number of siblings indicates that these GMM estimators can generate results with substantive differences from standard methods. Copyright © 2015 John Wiley & Sons, Ltd.