Estimating dynamic equilibrium models with stochastic volatility

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

No 13-19, Working Papers from Federal Reserve Bank of Philadelphia

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.

Keywords: Stochastic; analysis (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-dge, nep-ets and nep-ore
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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 (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 (2012)
Working Paper: Estimating Dynamic Equilibrium Models with Stochastic Volatility (2012)
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