Working Paper: CEPR ID: DP7157
Authors: Jess Fernández-Villaverde
Abstract: In this paper, I review the literature on the formulation and estimation of dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian methods. First, I discuss the evolution of DSGE models over the last couple of decades. Second, I explain why the profession has decided to estimate these models using Bayesian methods. Third, I briefly introduce some of the techniques required to compute and estimate these models. Fourth, I illustrate the techniques under consideration by estimating a benchmark DSGE model with real and nominal rigidities. I conclude by offering some pointers for future research.
Keywords: Bayesian methods; DSGE models; Likelihood estimation
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 |
|---|---|
| Bayesian methods (C11) | estimation process (C51) |
| Bayesian approach (C11) | combine prior beliefs with observed data (C11) |
| Bayesian methods (C11) | robust estimates (C51) |
| Bayesian econometrics (C11) | address model misspecification (C50) |
| prior distributions and likelihood functions (C46) | Bayesian framework (C11) |