Arbeitspapier

Inference in a class of optimization problems: Confidence regions and finite sample bounds on errors in coverage probabilities

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.

Language
Englisch

Bibliographic citation
Series: cemmap working paper ; No. CWP48/20

Classification
Wirtschaft
Subject
partial identification
normal approximation
finite-sample bounds

Event
Geistige Schöpfung
(who)
Horowitz, Joel
Lee, Sokbae
Event
Veröffentlichung
(who)
Centre for Microdata Methods and Practice (cemmap)
(where)
London
(when)
2020

DOI
doi:10.47004/wp.cem.2020.4820
Handle
Last update
20.09.2024, 8:24 AM CEST

Data provider

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Object type

  • Arbeitspapier

Associated

  • Horowitz, Joel
  • Lee, Sokbae
  • Centre for Microdata Methods and Practice (cemmap)

Time of origin

  • 2020

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