Frequentist Inference in Weakly Identified DSGE Models

47 Pages Posted: 7 Oct 2009

See all articles by Pablo Guerrón-Quintana

Pablo Guerrón-Quintana

Federal Reserve Banks - Federal Reserve Bank of Philadelphia

Atsushi Inoue

Southern Methodist University

Lutz Kilian

Federal Reserve Banks - Federal Reserve Bank of Dallas; Centre for Economic Policy Research (CEPR)

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

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

Pablo Guerron-Quintana

Federal Reserve Banks - Federal Reserve Bank of Philadelphia ( email )

Ten Independence Mall
Philadelphia, PA 19106-1574
United States

Atsushi Inoue

Southern Methodist University ( email )

Dallas, TX 75275
United States

Lutz Kilian (Contact Author)

Federal Reserve Banks - Federal Reserve Bank of Dallas ( email )

2200 North Pearl Street
PO Box 655906
Dallas, TX 75265-5906
United States

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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