Estimation of DSGE models: Maximum Likelihood vs. Bayesian methods

Glenn Mickelsson ()
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Glenn Mickelsson: Department of Economics, Postal: Uppsala University, P.O. Box 513, SE-751 20 Uppsala, Sweden

No 2015:6, Working Paper Series from Uppsala University, Department of Economics

Abstract: DSGE models are typically estimated using Bayesian methods, but a researcher may want to estimate a DSGE model with full information maximum likelihood (FIML) so as to avoid the use of prior distributions. A very robust algorithm is needed to find the global maximum within the relevant parameter space. I suggest such an algorithm and show that it is possible to estimate the model of Smets and Wouters (2007) using FIML. Inference is carried out using stochastic bootstrapping techniques. Several FIML estimates turn out to be significantly diffrent from the Bayesian estimates and the reasons behind those differences are analyzed.

Keywords: Bayesian methods; Maximum likelihood; Business Cycles; Estimate DSGE models (search for similar items in EconPapers)
JEL-codes: C11 E32 E37 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2015-12-22
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:uunewp:2015_006

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