Bayesian estimation of moment inequality models using empirical likelihood

2015 Fiscal Year Final Research Report

• Project/Area Number: 25780150
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Economic statistics
• Research Institution: Kobe University (2015); Kyoto University (2013-2014)
• Principal Investigator: Sueishi Naoya 神戸大学, 経済学研究科(研究院), 准教授 (40596251)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: 経験尤度 / モーメント不等式 / ベイズ推定 / 効率性の限界

Outline of Final Research Achievements

This study proposed a Bayesian estimation method that uses an empirical likelihood as a likelihood function, and applied the method to the estimation of moment inequality models. I showed asymptotic properties of the posterior density. Furthermore, I investigated finite sample validity of using an empirical likelihood as a likelihood of Bayes estimation.
Moreover, this study derived a least favorable submodel of conditional moment restriction models. The result suggests the efficiency bound and an asymptotically efficient estimator. The resulting estimator can be viewed as an empirical likelihood estimator for conditional moment restriction models.

Free Research Field

計量経済学