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Rare Shocks, Great Recessions

  • Marco Del Negro

    (Federal Reserve Bank of New York)

  • Vasco Curdia

    (Federal Reserve Bank of New York)

We estimate a DSGE model where rare large shocks can occur, by replacing the commonly used Gaussian assumption with a Student-t distribution. We show that the latter is favored by the data in the context of a Smets and Wouters-type model estimated on macro variables, even if we allow for low frequency variation in the shocks' volatility. The evidence is even stronger when we introduce financial frictions as in Bernanke, Gertler and Gilchrist (1999), and correspondingly include a measure of interest rate spreads among the observables. We provide some evidence that introducing Student-t shocks reduces the importance of low-frequency time-variation in volatility. In particular, we show that the Great Recession of 2008-09 does not result in significant increases in estimated time-varying volatility (i.e., it is not a reversal of the Great Moderation) but is largely the outcome of large shocks.

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Paper provided by Society for Economic Dynamics in its series 2012 Meeting Papers with number 654.

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Date of creation: 2012
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Handle: RePEc:red:sed012:654
Contact details of provider: Postal: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
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  1. Charles S. Bos & Ronald J. Mahieu & Herman K. van Dijk, 2000. "Daily Exchange Rate Behaviour and Hedging of Currency Risk," Econometric Society World Congress 2000 Contributed Papers 0504, Econometric Society.
  2. Siddhartha Chib & Srikanth Ramamurthy, 2014. "DSGE Models with Student- t Errors," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 152-171, June.
  3. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S19-40, Suppl. De.
  4. Guido Ascari & Giorgio Fagiolo & Andrea Roventini, 2012. "Fat-Tail Distributions and Business-Cycle Models," Quaderni di Dipartimento 157, University of Pavia, Department of Economics and Quantitative Methods.
  5. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
  6. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
  7. Michele Boldrin & Lawrence J. Christiano & Jonas D.M. Fisher, 1999. "Habit persistence, asset returns and the business cycles," Working Paper Series WP-99-14, Federal Reserve Bank of Chicago.
  8. Rochelle M. Edge & Refet S. Gurkaynak, 2010. "How Useful Are Estimated DSGE Model Forecasts for Central Bankers?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 209-259.
  9. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, 09.
  10. Zheng Liu & Daniel F. Waggoner & Tao Zha, 2011. "Sources of macroeconomic fluctuations: A regime‐switching DSGE approach," Quantitative Economics, Econometric Society, vol. 2(2), pages 251-301, 07.
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