Estimating dynamic equilibrium models with stochastic volatility

Jesus Fernandez-Villaverde, Pablo Guerron and Juan F Rubio-Ramirez

Journal of Econometrics, 2015, vol. 185, issue 1, 216-229

Abstract: 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.

Keywords: Dynamic equilibrium models; Stochastic volatility; Parameter drifting; Bayesian methods (search for similar items in EconPapers)
JEL-codes: C11 E10 E30 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (18)

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Related works:
Working Paper: Estimating Dynamic Equilibrium Models with Stochastic Volatility (2014)
Working Paper: Estimating Dynamic Equilibrium Models with Stochastic Volatility (2014)
Working Paper: Estimating Dynamic Equilibrium Models with Stochastic Volatility (2013)
Working Paper: Estimating dynamic equilibrium models with stochastic volatility (2013)
Working Paper: Estimating Dynamic Equilibrium Models with Stochastic Volatility (2013)
Working Paper: Estimating Dynamic Equilibrium Models with Stochastic Volatility (2012)
Working Paper: Estimating Dynamic Equilibrium Models with Stochastic Volatility (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:185:y:2015:i:1:p:216-229

DOI: 10.1016/j.jeconom.2014.08.010

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