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Robust confidence regions for incomplete models

Listed author(s):
  • Larry G. Epstein

    ()

    (Institute for Fiscal Studies and Boston University)

  • Hiroaki Kaido

    ()

    (Institute for Fiscal Studies and Boston University)

  • Kyoungwon Seo

    (Institute for Fiscal Studies and Korea Advanced Institute of Science and Technology (KAIST))

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.

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File URL: http://www.ifs.org.uk/uploads/cemmap/wps/cwp651515.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP65/15.

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Date of creation: 08 Oct 2015
Handle: RePEc:ifs:cemmap:65/15
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