Estimating Dynamic Equilibrium Models with Stochastic Volatility
Jesus Fernandez-Villaverde,
Pablo Guerron and
Juan F Rubio-Ramirez
No 18399, NBER Working Papers from National Bureau of Economic Research, Inc
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.
JEL-codes: C1 E30 (search for similar items in EconPapers)
Date: 2012-09
New Economics Papers: this item is included in nep-cmp, nep-dge, nep-ecm, nep-ets and nep-ore
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Published as Fernández-Villaverde, Jesús & Guerrón-Quintana, Pablo & Rubio-Ramírez, Juan F., 2015. "Estimating dynamic equilibrium models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 185(1), pages 216-229.
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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 (2013)
Working Paper: Estimating Dynamic Equilibrium Models with Stochastic Volatility (2012)
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