DP15164 Estimating DSGE Models: Recent Advances and Future Challenges

Author(s): Jesús Fernández-Villaverde, Pablo A. Guerron-Quintana
Publication Date: August 2020
Keyword(s): Bayesian methods, DSGE models, estimation, MCMC, Variational Inference
JEL(s): C11, C13, E30
Programme Areas: Monetary Economics and Fluctuations
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=15164

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.