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Robust confidence regions for incomplete models

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  • 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))

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.

Suggested Citation

  • Larry G. Epstein & Hiroaki Kaido & Kyoungwon Seo, 2015. "Robust confidence regions for incomplete models," CeMMAP working papers CWP20/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:20/15
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    5. Safra, Zvi & Segal, Uzi, 2022. "A lot of ambiguity," Journal of Economic Theory, Elsevier, vol. 200(C).
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    7. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Chen, Zengjing & Epstein, Larry G. & Zhang, Guodong, 2023. "A central limit theorem, loss aversion and multi-armed bandits," Journal of Economic Theory, Elsevier, vol. 209(C).
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    10. Shuowen Chen & Hiroaki Kaido, 2022. "Robust Tests of Model Incompleteness in the Presence of Nuisance Parameters," Papers 2208.11281, arXiv.org, revised Sep 2023.
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