Frequentist inference in weakly identified DSGE models

Pablo Guerron, Atsushi Inoue and Lutz Kilian

No 09-13, Working Papers from Federal Reserve Bank of Philadelphia

Abstract: The authors 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, the authors develop a new class of frequentist confidence sets for structural DSGE model parameters that remains asymptotically valid regardless of the strength of the identification. The proposed set correctly reflects the uncertainty about the structural parameters even when the likelihood is flat, it protects the researcher from spurious inference, and it is asymptotically invariant to the prior in the case of weak identification.

Keywords: Stochastic analysis; Macroeconomics - Econometric models (search for similar items in EconPapers)
Date: 2009
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-ets and nep-mac
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Citations: View citations in EconPapers (4)

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Working Paper: Frequentist Inference in Weakly Identified DSGE Models (2009)
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