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Estimating Dynamic Equilibrium Models with Stochastic Volatility

  • Jesus Fernandez-Villaverde
  • Pablo A. Guerrón-Quintana
  • Juan Rubio-Ramírez

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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File URL: http://www.nber.org/papers/w18399.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 18399.

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Date of creation: Sep 2012
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Publication status: 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.
Handle: RePEc:nbr:nberwo:18399
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