Frequentist Inference in Weakly Identified DSGE Models

Abstract

We show that in weakly identified models (1) the posterior mode will not be a consistent estimator of the true parameter vector, (2) the posterior distribution will not be Gaussian even asymptotically, and (3) Bayesian credible sets and frequentist confidence sets will not coincide asymptotically. This means that Bayesian DSGE estimation should not be interpreted merely as a convenient device for obtaining asymptotically valid point estimates and confidence sets from the posterior distribution. As an alternative, we develop new frequentist confidence sets for structural DSGE model parameters that remain asymptotically valid regardless of the strength of the identification.

Keywords: Bayes factor, Bayesian estimation, Confidence set, DSGE models, Identification, Inference, Likelihood ratio

JEL Classification: C32, C52, E30, E50