Estimating DSGE Models: Recent Advances and Future Challenges

Fernández-Villaverde, Jesús and Pablo A. Guerron-Quintana
Authors registered in the RePEc Author Service: Jesus Fernandez-Villaverde

No 15164, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We review the current state of the estimation of DSGE models. After introducing a general framework for dealing with DSGE models, the state-space representation, we discuss how to evaluate moments or the likelihood function implied by such a structure. We discuss, in varying degrees of detail, recent advances in the field, such as the tempered particle filter, approximated Bayesian computation, the Hamiltonian Monte Carlo, variational inference, and machine learning, methods that show much promise, but that have not been fully explored yet by the DSGE community. We conclude by outlining three future challenges for this line of research.

Keywords: Dsge models; Estimation; Bayesian methods; Mcmc; Variational inference (search for similar items in EconPapers)
JEL-codes: C11 C13 E30 (search for similar items in EconPapers)
Date: 2020-08
New Economics Papers: this item is included in nep-cmp, nep-dge and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Estimating DSGE Models: Recent Advances and Future Challenges (2021)
Working Paper: Estimating DSGE Models: Recent Advances and Future Challenges (2020)
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