Verifiable identification condition for nonignorable nonresponse data with categorical instrumental variables

Abstract: We consider a model identification problem in which an outcome variable contains nonignorable missing values. Statistical inference requires a guarantee of the model identifiability to obtain estimators enjoying theoretically reasonable properties such as consistency and asymptotic normality. Recently, instrumental or shadow variables, combined with the completeness condition in the outcome model, have been highlighted to make a model identifiable. However, the completeness condition may not hold even for simple models when the instrument is categorical. We propose a sufficient condition for model identifiability, which is applicable to cases where establishing the completeness condition is difficult. Using observed data, we demonstrate that the proposed conditions are easy to check for many practical models and outline their usefulness in numerical experiments and real data analysis.