Bounds on Generalized Linear Predictors with Incomplete Outcome Data

Jörg Stoye

Abstract: This paper develops easily computed, tight bounds on Generalized Linear Predictors and instrumental variable estimators when outcome data are partially identified. A salient example is given by Best Linear Predictors under square loss, or Ordinary Least Squares regressions, with missing outcome data, in which case the setup specializes the more general but intractable problem examined by Horowitz et al. [9]. The result is illustrated by re-analyzing the data used in that paper.