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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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  6. S. Boragan Aruoba & Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2003. "Comparing Solution Methods for Dynamic Equilibrium Economies," PIER Working Paper Archive 04-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
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  10. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2008. "How Structural Are Structural Parameters?," NBER Chapters, in: NBER Macroeconomics Annual 2007, Volume 22, pages 83-137 National Bureau of Economic Research, Inc.
  11. 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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  17. 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.
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  21. Neil Shephard & Torben Andersen, 2008. "Stochastic Volatility: Origins and Overview," Economics Papers 2008-W04, Economics Group, Nuffield College, University of Oxford.
  22. Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
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  26. Dario Caldara & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Yao Wen, 2012. "Computing DSGE models with recursive preferences and stochastic volatility," Finance and Economics Discussion Series 2012-04, Board of Governors of the Federal Reserve System (U.S.).
  27. Stacey L. Schreft, 1990. "Credit controls: 1980," Economic Review, Federal Reserve Bank of Richmond, issue Nov, pages 25-55.
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