Working Paper: CEPR ID: DP15164
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; MCMC; variational inference
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 |
|---|---|
| Econometric tools (C51) | Estimation of DSGE models (C51) |
| Likelihood function (C51) | Parameter values (Y10) |
| Bayesian methods (C11) | Estimation process (C51) |
| MCMC (C38) | Incorporation of prior information (D83) |
| Kalman filter (C53) | Tracking the state of the economy (E32) |
| DSGE models (E13) | Understanding of dynamic interactions (C69) |
| Shocks (e.g., technology, monetary policy) (E39) | Effects on the economy (F69) |