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Fat-tails in VAR Models

  • Ching-Wai (Jeremy) Chiu

    (Bank of England)

  • Haroon Mumtaz

    ()

    (Queen Mary University of London)

  • Gabor Pinter

    (Bank of England)

We confirm that standard time-series models for US output growth, inflation, interest rates and stock market returns feature non-Gaussian error structure. We build a 4-variable VAR model where the orthogonolised shocks have a Student t-distribution with a time-varying variance. We find that in terms of in-sample fit, the VAR model that features both stochastic volatility and Student-t disturbances outperforms restricted alternatives that feature either attributes. The VAR model with Student-t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity. This difference appears to be especially stark over the recent financial crisis.

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File URL: http://econ.qmul.ac.uk/research/workingpapers/2014/Items/docs/714.pdf
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Paper provided by Queen Mary University of London, School of Economics and Finance in its series Working Papers with number 714.

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Date of creation: Mar 2014
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Handle: RePEc:qmw:qmwecw:wp714
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  1. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
  2. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
  3. 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).
  4. Fernández-Villaverde, Jesús & Rubio-Ramírez, Juan Francisco, 2006. "Estimating Macroeconomic Models: A Likelihood Approach," CEPR Discussion Papers 5513, C.E.P.R. Discussion Papers.
  5. John Geweke, 1992. "Priors for macroeconomic time series and their application," Discussion Paper / Institute for Empirical Macroeconomics 64, Federal Reserve Bank of Minneapolis.
  6. Timothy Cogley & Thomas Sargent, . "Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US," Working Papers 2133503, Department of Economics, W. P. Carey School of Business, Arizona State University.
  7. Banbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
  8. Marco Del Negro & Vasco Curdia, 2012. "Rare Shocks, Great Recessions," 2012 Meeting Papers 654, Society for Economic Dynamics.
  9. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
  10. Guido Ascari & Giorgio Fagiolo & Andrea Roventini, 2012. "Fat-tail Distributions and Business-Cycle Models," Documents de Travail de l'OFCE 2012-01, Observatoire Francais des Conjonctures Economiques (OFCE).
  11. repec:thk:rnotes:5 is not listed on IDEAS
  12. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
  13. Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
  14. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-17, October.
  15. Siddhartha Chib & Srikanth Ramamurthy, 2014. "DSGE Models with Student- t Errors," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 152-171, June.
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