Working Paper: NBER ID: w18399
Authors: Jesus Fernandez-Villaverde; Pablo A. Guerrón-Quintana; Juan Rubio-Ramirez
Abstract: We propose a novel method to estimate dynamic equilibrium models with stochastic volatility. First, we characterize the properties of the solution to this class of models. Second, we take advantage of the results about the structure of the solution to build a sequential Monte Carlo algorithm to evaluate the likelihood function of the model. The approach, which exploits the profusion of shocks in stochastic volatility models, is versatile and computationally tractable even in large-scale models, such as those often employed by policy-making institutions. As an application, we use our algorithm and Bayesian methods to estimate a business cycle model of the U.S. economy with both stochastic volatility and parameter drifting in monetary policy. Our application shows the importance of stochastic volatility in accounting for the dynamics of the data.
Keywords: dynamic equilibrium models; stochastic volatility; Bayesian methods; business cycle model
JEL Codes: C1; E30
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| stochastic volatility (C58) | dynamics of the data (C69) |
| dynamic nature of monetary policy (E52) | successful modeling of economic outcomes (E17) |
| stochastic volatility and parameter drifting (C58) | successful model of the U.S. economy (P17) |