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Estimating Dynamic Equilibrium Models with Stochastic Volatility

  • Jesus Fernandez-Villaverde

    ()

    (Department of Economics, University of Pennsylvania, NBER, CEPR, and FEDEA)

  • Pablo Guerrón-Quintana

    ()

    (Federal Reserve Bank of Philadelphia)

  • Juan F. Rubio-Ramírez

    ()

    (Duke University, Federal Reserve Bank of Atlanta, and FEDEA)

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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Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 13-036.

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Length: 71 pages
Date of creation: 08 May 2013
Date of revision:
Handle: RePEc:pen:papers:13-036
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