EL inference for partially identified models: Large deviations optimality and bootstrap validity

Ivan A. Canay*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

Abstract

This paper addresses the issue of optimal inference for parameters that are partially identified in models with moment inequalities. There currently exists a variety of inferential methods for use in this setting. However, the question of choosing optimally among contending procedures is unresolved. In this paper, I first consider a canonical large deviations criterion for optimality and show that inference based on the empirical likelihood ratio statistic is optimal. Second, I introduce a new empirical likelihood bootstrap that provides a valid resampling method for moment inequality models and overcomes the implementation challenges that arise as a result of non-pivotal limit distributions. Lastly, I analyze the finite sample properties of the proposed framework using Monte Carlo simulations. The simulation results are encouraging.

Original languageEnglish (US)
Pages (from-to)408-425
Number of pages18
JournalJournal of Econometrics
Volume156
Issue number2
DOIs
StatePublished - Jun 1 2010

Keywords

  • Asymptotic optimality
  • Empirical likelihood
  • Empirical likelihood bootstrap
  • Large deviations
  • Partial identification

ASJC Scopus subject areas

  • Economics and Econometrics

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