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Estimating Dynamic Equilibrium Models with Stochastic Volatility

Listed author(s):
  • 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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File URL: http://economics.sas.upenn.edu/system/files/13-036.pdf
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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
Handle: RePEc:pen:papers:13-036
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  1. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
  2. Jules H. van Binsbergen & Jesús Fernández-Villaverde & Ralph S.J. Koijen & Juan F. Rubio-Ramírez, 2010. "The Term Structure of Interest Rates in a DSGE Model with Recursive Preferences," PIER Working Paper Archive 10-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  3. Osnat Stramer & Matthew Bognar & Paul Schneider, 2010. "Bayesian Inference for Discretely Sampled Markov Processes with Closed-Form Likelihood Expansions," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(4), pages 450-480, Fall.
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  6. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, 05.
  7. Jesús Fernández-Villaverde & Juan F Rubio-Ramírez, 2007. "How Structural Are Structural Parameters?," Levine's Bibliography 843644000000000057, UCLA Department of Economics.
  8. Jonas D. M. Fisher, 2006. "The Dynamic Effects of Neutral and Investment-Specific Technology Shocks," Journal of Political Economy, University of Chicago Press, vol. 114(3), pages 413-451, June.
  9. S. Boragan Aruoba & Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2003. "Comparing solution methods for dynamic equilibrium economies," FRB Atlanta Working Paper 2003-27, Federal Reserve Bank of Atlanta.
  10. Peralta Alva, Adrián & Santos, Manuel S., 2003. "Accuracy of simulations for stochastic dynamic models," UC3M Working papers. Economics we034615, Universidad Carlos III de Madrid. Departamento de Economía.
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  13. Alejandro Justiniano & Northwestern University, 2006. "The Time Varying Volatility of Macroeconomic Fluctuations," Computing in Economics and Finance 2006 219, Society for Computational Economics.
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  20. Dario Caldara & Jesus Fernandez-Villaverde & Juan Rubio-Ramirez & Wen Yao, 2012. "Computing DSGE Models with Recursive Preferences and Stochastic Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 188-206, April.
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  24. Roger E. A. Farmer & Daniel F. Waggoner & Tao Zha, 2009. "Understanding Markov-switching rational expectations models," FRB Atlanta Working Paper 2009-05, Federal Reserve Bank of Atlanta.
  25. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
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  28. Ai[diaeresis]t-Sahalia, Yacine & Kimmel, Robert, 2007. "Maximum likelihood estimation of stochastic volatility models," Journal of Financial Economics, Elsevier, vol. 83(2), pages 413-452, February.
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