A GMM approach for dealing with missing data on regressors and instruments

Missing data is one of the most common challenges facing empirical researchers. This paper presents a general GMM framework for dealing with missing data on explanatory variables or instrumental variables. For a linear-regression model with missing covariate data, an efficient GMM estimator under minimal assumptions on missingness is proposed. The estimator, which also allows for a specification test of the missingness assumptions, is compared to previous approaches in the literature (including imputation methods and a dummy-variable approach used in much recent empirical research). For an instrumentalvariables model with potential missingness of the instrument, the GMM framework suggests a rich set of instruments that can be used to improve efficiency. Simulations and empirical examples are provided to compare the GMM approach with existing approaches. JEL Classification: C13, C30