Title: Conditional moment restriction models with missing data
Authors: Valentin Patilea - CREST-Ensai (France) [presenting]
Abstract: A general statistical model is considered which is defined by moment restrictions when a subvector of data are missing. The main incomplete data situations we have in mind are missing at random and endogenous selection. Using the inverse probability weighting, we show that such a model is equivalent to a model for the observed variables only, augmented by a moment condition defined by the missingness mechanism. In particular, our framework covers parametric and semiparametric mean regressions and quantile regressions. We allow for missing responses, missing covariates and any combination of them. We present a general equivalence result, obtained under minimal technical conditions, that sheds new light on various aspects of interest in the missing data literature. It also provides guidelines for building (efficient) estimators. Moreover, as an immediate consequence of our general theory, we derive the efficiency of the complete cases analysis in a general semiparametric regression model with responses missing at random.