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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, but replace the commonly used Gaussian assumption with a Student´s 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 1) the Student´s t specification is strongly favored by the data, even when we allow for low-frequency variation in the volatility of the shocks, and 2) 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 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.

Suggested Citation

  • Vasco Cúrdia & Marco Del Negro & Daniel L. Greenwald, 2012. "Rare shocks, great recessions," Staff Reports 585, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:585
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    References listed on IDEAS

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    1. 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.
    2. Alejandro Justiniano & Giorgio E. Primiceri, 2008. "The Time-Varying Volatility of Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 98(3), pages 604-641, June.
    3. Charles S. Bos & Ronald J. Mahieu & Herman K. Van Dijk, 2000. "Daily exchange rate behaviour and hedging of currency risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 671-696.
    4. Siddhartha Chib & Srikanth Ramamurthy, 2014. "DSGE Models with Student- t Errors," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 152-171, June.
    5. Ascari, Guido & Fagiolo, Giorgio & Roventini, Andrea, 2015. "Fat-Tail Distributions And Business-Cycle Models," Macroeconomic Dynamics, Cambridge University Press, vol. 19(02), pages 465-476, March.
    6. 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, July.
    7. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
    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, September.
    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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    Keywords

    Equilibrium (Economics) ; Stochastic analysis ; Recessions ; Business cycles;

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