Working Paper: NBER ID: w27715
Authors: Jess Fernández-Villaverde; Pablo A. Guerrón-Quintana
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; Machine Learning
JEL Codes: C11; C13; E30
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Likelihood function (C51) | Parameter estimation (C51) |
| Likelihood function (C51) | Reliable parameter values (C51) |
| Bayesian methods (C11) | Posterior distributions (C46) |
| Likelihood function (C51) | Bayesian inference (C11) |
| Econometric tools (C51) | Understanding of economic dynamics (E32) |
| Machine learning methods (C45) | DSGE model estimation (C51) |
| Machine learning methods (C45) | Handling of high-dimensional data (C55) |