Robust confidence regions for incomplete models

Larry Epstein, Hiroaki Kaido and Kyoungwon Seo

No 20/15, CeMMAP working papers from 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
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https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP2015.pdf (application/pdf)

Related works:
Journal Article: Robust Confidence Regions for Incomplete Models (2016)
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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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:20/15

DOI: 10.1920/wp.cem.2015.2015

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