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Rare shocks, Great Recessions

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  • Vasco Cúrdia
  • Marco Del Negro
  • Daniel L. Greenwald

Abstract

We estimate a DSGE model where rare large shocks can occur, by replacing the commonly used Gaussian assumption with a Student-t distribution. Results from the Smets and Wouters (2007) model estimated on the usual set of macroeconomic time series over the 1964-2011 period indicate that the Student-t specification is strongly favored by the data even when we allow for low-frequency variation in the volatility of the shocks, and that the estimated degrees of freedom are quite low for several shocks that drive U.S. business cycles, implying an important role for rare large shocks. This result holds even if we exclude the Great Recession period from the sample. We also show that inference about low-frequency changes in volatility and in particular, inference about the magnitude of the Great Moderation is different once we allow for fat tails.

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Paper provided by Federal Reserve Bank of San Francisco in its series Working Paper Series with number 2013-01.

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Date of creation: 2013
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Handle: RePEc:fip:fedfwp:2013-01

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  1. Guido Ascari & Giorgio Fagiolo & Andrea Roventini, 2012. "Fat-Tail Distributions and Business-Cycle Models," Working Papers 02/2012, University of Verona, Department of Economics.
  2. 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.
  3. Michele Boldrin & Lawrence J. Christiano & Jonas D. M. Fisher, 2000. "Habit persistence, asset returns and the business cycle," Staff Report 280, Federal Reserve Bank of Minneapolis.
  4. 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.
  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. 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.
  7. 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.
  8. 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.
  9. 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.
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As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Rare shocks, Great Recessions
    by Christian Zimmermann in NEP-DGE blog on 2013-03-03 00:49:08
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Cited by:
  1. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 1218, Federal Reserve Bank of Cleveland.
  2. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," EconomiX Working Papers 2012-17, University of Paris West - Nanterre la Défense, EconomiX.
  3. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2014. "Fat-tails in VAR Models," Working Papers 714, Queen Mary, University of London, School of Economics and Finance.
  4. Marco Del Negro & Giorgio Primiceri, 2013. "Time-varying structural vector autoregressions and monetary policy: a corrigendum," Staff Reports 619, Federal Reserve Bank of New York.

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