Discrete Choice Nonresponse

Abstract

Missing values are endemic in the data sets available to econometricians. This paper suggests a semiparametrically efficient likelihood-based approach to deal with general nonignorable missing data problems for discrete choice models. Our concern is when the dependent variable and/or covariates are unobserved for some sampling units. A supplementary random sample of observations on all covariates may be available. The key insight of this paper is the recognition of nonresponse as a modification of choice-based samples. Semiparametrically efficient GMM estimation appropriate for choice based samples is then adapted for the nonresponse framework considered in this paper. Simulation results for various GMM estimators proposed here are very encouraging.