Set inferences and sensitivity analysis in semiparametric conditionally identified models

This paper provides tools for partial identification inference and sensistivity analysis in a general class of semiparametric models. The main working assumption is that the finite-dimensional parameter of interest and the possibility infinite-dimensional nuisance parameter are identified conditionally on other nuisance parameters being known. This structure arises in numerous applications and leads to relatively simple inference procedures. The paper develops uniform convergence for a set of semiparametric two-step GMM estimators, and it uses the uniformity to establish set inferences, including confidence regions for the identified set and the true parameter. Sensitivity analysis considers a domain of variation for the unidentified parameter that can be well outside its identified set, which demands inference to be established under misspecification. The paper also introduces new measures of sensitivity. Inferences are implemented with new bootstrap methods. Several example applications illustrate the wide applicability of our results.