**S**id Chib, Minchul Shin, and Anna Simoni (CREST) recently arXived a paper entitled “Bayesian Empirical Likelihood Estimation and Comparison of Moment Condition Models“. That Sid mentioned to me in Sardinia. The core notion is related to earlier Bayesian forays into empirical likelihood pseudo-models, like Lazar (2005) or our PNAS paper with Kerrie Mengersen and Pierre Pudlo. Namely to build a pseudo-likelihood using empirical likelihood principles and to derive the posterior associated with this pseudo-likelihood. Some novel aspects are the introduction of tolerance (nuisance) extra-parameters when some constraints do not hold, a maximum entropy (or exponentially tilted) representation of the empirical likelihood function, and a Chib-Jeliazkov representation of the marginal likelihood. The authors obtain a Bernstein-von Mises theorem under correct specification. Meaning convergence. And another one under misspecification.

While the above Bernstein-von Mises theory is somewhat expected (if worth deriving) in the light of frequentist consistency results, the paper also considers a novel and exciting aspect, namely to compare models (or rather moment restrictions) by Bayes factors derived from empirical likelihoods. A grand (encompassing) model is obtained by considering all moment restrictions at once, which first sounds like *more* restricted, except that the extra-parameters are there to monitor constraints that actually hold. It is unclear from my cursory read of the paper whether priors on those extra-parameters can be automatically derived from a single prior. And how much they impact the value of the Bayes factor. The consistency results found in the paper do not seem to depend on the form of priors adopted for each model (for all three cases of both correctly, one correctly and none correctly specified models). Except maybe for some local asymptotic normality (LAN). Interestingly (?), the authors consider the Poisson versus Negative Binomial test we used in our testing by mixture paper. This paper is thus bringing a better view of the theoretical properties of a pseudo-Bayesian approach based on moment conditions and empirical likelihood approximations. Without a clear vision of the implementation details, from the parameterisation of the constraints (which could be tested the same way) to the construction of the prior(s) to the handling of MCMC difficulties in realistic models.