Estimating Dynamic Equilibrium Models with Stochastic Volatility

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

No 2014-11, Working Papers from FEDEA

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 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.

Date: 2014-10
New Economics Papers: this item is included in nep-dge, nep-ets and nep-mac
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Estimating dynamic equilibrium models with stochastic volatility (2015)
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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