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

Listed author(s):
  • Canay, Ivan A.

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.

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File URL: http://www.sciencedirect.com/science/article/pii/S0304-4076(09)00298-X
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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 156 (2010)
Issue (Month): 2 (June)
Pages: 408-425

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Handle: RePEc:eee:econom:v:156:y:2010:i:2:p:408-425
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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