Estimating DSGE Models: Recent Advances and Future Challenges

Jesus Fernandez-Villaverde and Pablo Guerron

No 27715, NBER Working Papers from National Bureau of Economic Research, Inc

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.

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, nep-ecm and nep-ore
Note: EFG
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published as Jesús Fernández-Villaverde & Pablo A. Guerrón-Quintana, 2021. "Estimating DSGE Models: Recent Advances and Future Challenges," Annual Review of Economics, vol 13(1), pages 229-252.

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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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