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.
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I am indebted to Chuck Manski for his guidance and feedback and to two anonymous referees for extremely helpful suggestions. Of course, all errors are mine. Financial support through the Robert Eisner Memorial Fellowship, Department of Economics, Northwestern University, as well as a Dissertation Year Fellowship, The Graduate School, Northwestern University, is gratefully acknowledged.
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Stoye, J. Bounds on Generalized Linear Predictors with Incomplete Outcome Data. Reliable Comput 13, 293–302 (2007). https://doi.org/10.1007/s11155-006-9030-5
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DOI: https://doi.org/10.1007/s11155-006-9030-5