Estimating dynamic equilibrium models with stochastic volatility

Published

Journal Article

© 2014 Elsevier B.V. All rights reserved. This paper develops a particle filtering algorithm to estimate dynamic equilibrium models with stochastic volatility using a likelihood-based approach. The algorithm, which exploits the structure and profusion of shocks in stochastic volatility models, is versatile and computationally tractable even in large-scale models. As an application, we use our algorithm and Bayesian methods to estimate a business cycle model of the US 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.

Full Text

Cited Authors

  • Fernández-Villaverde, J; Guerrón-Quintana, P; Rubio-Ramírez, JF

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 185 / 1

Start / End Page

  • 216 - 229

Electronic International Standard Serial Number (EISSN)

  • 1872-6895

International Standard Serial Number (ISSN)

  • 0304-4076

Digital Object Identifier (DOI)

  • 10.1016/j.jeconom.2014.08.010

Citation Source

  • Scopus