Econometrica: Sep 2016, Volume 84, Issue 5

Robust Confidence Regions for Incomplete Models

https://doi.org/10.3982/ECTA13394
p. 1799-1838

Larry G. Epstein, Hiroaki Kaido, Kyoungwon Seo

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.

Log In To View Full Content

Supplemental Material

Supplement to "Robust Confidence Regions for Incomplete Models"

This supplement provides details on how to implement the inference method proposed in the main text.

Read More View PDF


Supplement to "Robust Confidence Regions for Incomplete Models"

This zip file contains replication files for the manuscript.

Read More View ZIP



Back