Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246801 
Year of Publication: 
2021
Series/Report no.: 
cemmap working paper No. CWP33/21
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This paper describes three methods for carrying out non-asymptotic inference on partially identified parameters that are solutions to a class of optimization problems. Applications in which the optimization problems arise include estimation under shape restrictions, estimation of models of discrete games, and estimation based on grouped data. The partially identified 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 finite-sample lower bounds on the coverage probabilities of the confidence intervals under three sets of assumptions of increasing strength. With the moderate sample sizes found in most economics applications, the bounds become tighter as the assumptions strengthen. We discuss estimation of population parameters that the bounds depend on and contrast our methods with alternative methods for obtaining confidence intervals for partially identified parameters. The results of Monte Carlo experiments and empirical examples illustrate the usefulness of our method.
Subjects: 
partial identification
normal approximation
sub-Gaussian distribution
finite-sample bounds
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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