A GMM Approach for Dealing with Missing Data on Regressors
提出一个广义矩估计（GMM）框架来处理线性回归中解释变量缺失的问题，该估计量在特定假设下有效，并允许对缺失假设进行检验，与常用方法比较发现虚拟变量法在随机缺失时也不一致。
Missing data are a common challenge facing empirical researchers. This paper presents a general GMM framework and estimator for dealing with missing values of an explanatory variable in linear regression analysis. The GMM estimator is efficient under assumptions needed for consistency of linear-imputation methods. The estimator, which also allows for a specification test of the missingness assumptions, is compared to existing linear imputation, complete data, and dummy variable methods commonly used in empirical research. The dummy variable method is generally inconsistent even when data are missing completely at random, and the dummy variable method, when consistent, can be less efficient than the complete data method.