Inference in a class of optimization problems: Confidence regions and finite sample bounds on errors in coverage probabilities - Joel Horowitz; Sokbae Lee (Centre for Microdata Methods and Practice (cemmap), 2020)
This paper describes a method for carrying out inference on partially identified parameters that are solutions to a class of optimization problems. The optimization problems arise in applications in which grouped data are used for estimation of a model's structural parameters. The parameters are characterized by restrictions that involve the unknown population means of observed random variables in addition to the structural parameters of interest. Inference consists of finding confidence intervals for the structural parameters. Our theory provides a finite-sample bound on the difference between the true and nominal probabilities with which a confidence interval contains the true but unknown value of a parameter. We contrast our method with an alternative inference method based on the median-of-means estimator of Minsker (2015). The results of Monte Carlo experiments and empirical examples illustrate the usefulness of our method.
Keywords: 330; partial identification; normal approximation; finite-sample bounds