Robust confidence regions for incomplete models

Larry Epstein, Hiroaki Kaido and Kyoungwon Seo
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Kyoungwon Seo: Institute for Fiscal Studies and Korea Advanced Institute of Science and Technology (KAIST)

No CWP20/15, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: Call an economic model incomplete if it does not generate a probabilistic prediction even given knowledge of all parameter values. We propose a method of inference about unknown parameters for such models that is robust to heterogeneity and dependence of unknown form. The key is a Central Limit Theorem for belief functions; robust confidence regions are then constructed in a fashion paralleling the classical approach. Monte Carlo simulations support tractability of the method and demonstrate its enhanced robustness relative to existing methods.

Date: 2015-04-24
New Economics Papers: this item is included in nep-ecm
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Related works:
Working Paper: Robust confidence regions for incomplete models (2015)
Working Paper: Robust confidence regions for incomplete models (2015)
Working Paper: Robust Confidence Regions for Incomplete Models (2015)
Working Paper: Robust confidence regions for incomplete models (2015)
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