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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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File URL: https://economicdynamics.org/meetpapers/2012/paper_654.pdf
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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
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Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA

Web page: http://www.EconomicDynamics.org/
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  1. 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.
  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. 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.
  4. 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.
  5. Zheng Liu & Daniel F. Waggoner & Tao Zha, 2010. "Sources of Macroeconomic Fluctuations: A Regime-switching DSGE Approach," Emory Economics 1002, Department of Economics, Emory University (Atlanta).
  6. 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.
  7. Alejandro Justiniano & Giorgio E. Primiceri, 2006. "The Time Varying Volatility of Macroeconomic Fluctuations," NBER Working Papers 12022, National Bureau of Economic Research, Inc.
  8. Bos, C.S. & Mahieu, R.J. & van Dijk, H.K., 2000. "Daily exchange rate behaviour and hedging of currency risk," Econometric Institute Research Papers EI 2000-25/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  9. Frank Smets & Raf Wouters, 2002. "An estimated dynamic stochastic general equilibrium model of the euro area," Working Paper Research 35, National Bank of Belgium.
  10. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
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