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Estimating Dynamic Equilibrium Models with Stochastic Volatility

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Author Info

  • 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)

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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Bibliographic Info

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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Keywords: Dynamic equilibrium models; Stochastic volatility; Parameter drifting; Bayesian methods;

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  1. Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers, Massachusetts Institute of Technology (MIT), Department of Economics 464, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Margaret M. McConnell & Gabriel Perez Quiros, 1997. "Output fluctuations in the United States: what has changed since the early 1980s?," Research Paper, Federal Reserve Bank of New York 9735, Federal Reserve Bank of New York.
  3. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, Elsevier, vol. 74(1), pages 119-147, September.
  4. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, Econometric Society, vol. 57(5), pages 1027-57, September.
  5. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, American Economic Association, vol. 96(1), pages 54-81, March.
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Cited by:
  1. Kristoffer Nimark, 2013. "Man-Bites-Dog Business Cycle," Working Papers 700, Barcelona Graduate School of Economics.

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