DP7447 Frequentist Inference in Weakly Identified DSGE Models

Author(s): Pablo A. Guerron-Quintana, Atsushi Inoue, Lutz Kilian
Publication Date: September 2009
Keyword(s): Bayes factor, Bayesian estimation, Confidence set, DSGE models, Identification, Inference, Likelihood ratio
JEL(s): C32, C52, E30, E50
Programme Areas: International Macroeconomics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=7447

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.