Frequentist Inference in Weakly Identified DSGE Models
Atsushi Inoue () and
Lutz Kilian ()
No 7447, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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 (search for similar items in EconPapers)
JEL-codes: C32 C52 E30 E50 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-ets
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Working Paper: Frequentist inference in weakly identified DSGE models (2009)
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