A Robust Bayesian Exponentially Tilted Empirical Likelihood Method
Catherine Scipione Forbes, Monash University, Melbourne Australia
This talk will focus on a new Bayesian approach for analysing moment condition models in the situation where data that may be contaminated by outliers. The approach builds upon the foundations in Schennach (2005) who proposed the original Bayesian exponentially tilted empirical likelihood (BETEL) method, justified by the fact that an empirical likelihood can be interpreted as the nonparametric limit of a Bayesian procedure when the implied probabilities are obtained from maximizing entropy subject to the given moment constraints. Considering the impact that outliers are thought to have on the estimation of population moments, we develop a new robust BETEL (RBETEL) inferential methodology to deal with this potential problem. We show how the BETEL methods are linked to the recent work of Bissiri, Holmes and Walker (2016) who propose a general framework for update prior belief via a loss function. In addition to an empirical illustration, the results of a simulation experiment demonstrating that the proposed methodology produces reliable posterior inference will be reviewed.
Note: Seminars are free and open to the public.