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Federal Reserve Bank of Philadelphia
Working Papers
Estimating dynamic equilibrium models with stochastic volatility
Jesús Fernández-Villaverde
Pablo Guerrón-Quintana
Juan F. Rubio-Ramírez
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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Jesús Fernández-Villaverde & Pablo Guerrón-Quintana & Juan F. Rubio-Ramírez, Estimating dynamic equilibrium models with stochastic volatility, Federal Reserve Bank of Philadelphia, Working Papers 13-19, 2013.
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Keywords: Stochastic analysis
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