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

Ivan Canay

Journal of Econometrics, 2010, vol. 156, issue 2, 408-425

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.

Keywords: Empirical; likelihood; Partial; identification; Large; deviations; Empirical; likelihood; bootstrap; Asymptotic; optimality (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (161)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:156:y:2010:i:2:p:408-425

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