Confidence intervals for partially identified parameters

Guido W. Imbens*, Charles F. Manski

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

342 Scopus citations


Recently a growing body of research has studied inference in settings where parameters of interest are partially identified. In many cases the parameter is real-valued and the identification region is an interval whose lower and upper bounds may be estimated from sample data. For this case confidence intervals (CIs) have been proposed that cover the entire identification region with fixed probability. Here, we introduce a conceptually different type of confidence interval. Rather than cover the entire identification region with fixed probability, we propose CIs that asymptotically cover the true value of the parameter with this probability. However, the exact coverage probabilities of the simplest version of our new CIs do not converge to their nominal values uniformly across different values for the width of the identification region. To avoid the problems associated with this, we modify the proposed CI to ensure that its exact coverage probabilities do converge uniformly to their nominal values. We motivate this modified CI through exact results for the Gaussian case.

Original languageEnglish (US)
Pages (from-to)1845-1857
Number of pages13
Issue number6
StatePublished - Nov 2004


  • Bounds
  • Confidence intervals
  • Identification regions
  • Uniform convergence

ASJC Scopus subject areas

  • Economics and Econometrics


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