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Estimation of DSGE Models under Diffuse Priors and Data-Driven Identification Constraints

Markku Lanne and Jani Luoto

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We propose a sequential Monte Carlo (SMC) method augmented with an importance sampling step for estimation of DSGE models. In addition to being theoretically well motivated, the new method facilitates the assessment of estimation accuracy. Furthermore, in order to alleviate the problem of multimodal posterior distributions due to poor identification of DSGE models when uninformative prior distributions are assumed, we recommend imposing data-driven identification constraints and devise a procedure for finding them. An empirical application to the Smets-Wouters (2007) model demonstrates the properties of the estimation method, and shows how the problem of multimodal posterior distributions caused by parameter redundancy is eliminated by identification constraints. Out-of-sample forecast comparisons as well as Bayes factors lend support to the constrained model.

Keywords: Particle filter; importance sampling; Bayesian identification (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 D58 (search for similar items in EconPapers)
Pages: 31
Date: 2015-08-18
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-for
References: View references in EconPapers View complete reference list from CitEc
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