Frequentist Inference in Weakly Identified DSGE Models

Guerrón-Quintana, Pablo; Inoue, Atsushi; Kilian, Lutz

Frequentist Inference in Weakly Identified DSGE Models

CEPR Discussion Paper No. DP7447

47 Pages Posted: 7 Oct 2009

See all articles by Pablo Guerrón-Quintana

Pablo Guerrón-Quintana

Federal Reserve Banks - Federal Reserve Bank of Philadelphia

Lutz Kilian

University of Michigan at Ann Arbor - Department of Economics; Centre for Economic Policy Research (CEPR)

There are 2 versions of this paper

Date Written: September 2009

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

Suggested Citation: Suggested Citation

Guerron-Quintana, Pablo and Inoue, Atsushi and Kilian, Lutz, Frequentist Inference in Weakly Identified DSGE Models (September 2009). CEPR Discussion Paper No. DP7447. Available at SSRN: https://ssrn.com/abstract=1484480