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cwp201515.pdf
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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.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Hiroaki Kaido
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Larry G. Epstein
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Kyoungwon Seo
Working Paper details
- DOI
- 10.1920/wp.cem.2015.2015
- Publisher
- Institute for Fiscal Studies
Suggested citation
L, Epstein and H, Kaido and K, Seo. (2015). Robust confidence regions for incomplete models. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/robust-confidence-regions-incomplete-models (accessed: 1 July 2024).
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