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Title: Estimating Dynamic Equilibrium Models with Stochastic Volatility

Author(s): Jesús Fernández-Villaverde, Pablo A. Guerron-Quintana and Juan Francisco Rubio-Ramírez

Publication Date: September 2012

Keyword(s): Bayesian methods, Dynamic equilibrium models, Parameter drifting and Stochastic volatility

Programme Area(s): International Macroeconomics

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.

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Bibliographic Reference

Fernández-Villaverde, J, Guerron-Quintana, P and Rubio-Ramírez, J. 2012. 'Estimating Dynamic Equilibrium Models with Stochastic Volatility'. London, Centre for Economic Policy Research.