Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/97406
Authors: 
Escanciano, Juan Carlos
Zhu, Lin
Year of Publication: 
2013
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP55/13
Abstract: 
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.
Subjects: 
Partial Identi…cation
Semiparametric models
Sensitivity analysis
JEL: 
C14
C21
C25
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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