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EL inference for partially identified models: Large deviations optimality and bootstrap validity
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DOI：10.1016/j.jeconom.2009.11.009.png)
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摘要
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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. (C) 2009 Elsevier B.V. All rights reserved.
Keyword:
Empirical likelihood
Partial identification
Large deviations
Empirical likelihood bootstrap
Asymptotic optimality
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