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A Copula-VAR-X Approach for Industrial Production Modelling and Forecasting

  • Carluccio Bianchi

    ()

    (Department of Economics and Quantitative Methods, University of Pavia)

  • Alessandro Carta

    ()

    (Department of Economics and Quantitative Methods, University of Pavia)

  • Dean Fantazzini

    ()

    (Department of Economics and Quantitative Methods, University of Pavia)

  • Maria Elena De Giuli

    ()

    (Department of Economics and Quantitative Methods, University of Pavia)

  • Mario A. Maggi

    ()

    (Department of Economics and Quantitative Methods, University of Pavia)

World economies, and especially European ones, have become strongly interconnected in the last decades and a joint modelling is required. We propose here the use of Copulas to build flexible multivariate distributions, since they allow for a rich dependence structure and more flexible marginal distributions that better fit the features of empirical data, such as leptokurtosis. We use our approach to forecast industrial production series in the core EMU countries and we provide evidence that the copula-VAR model outperforms or at worst compares similarly to normal VAR models, keeping the same computational tractability of the latter approach.

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File URL: http://economia.unipv.it/docs/dipeco/quad/ps/RePEc/pav/wpaper/q105.pdf
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Paper provided by University of Pavia, Department of Economics and Quantitative Methods in its series Quaderni di Dipartimento with number 105.

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Length: 19 pages
Date of creation: Nov 2009
Date of revision:
Handle: RePEc:pav:wpaper:105
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  1. Giorgio Bodo & Roberto Golinelli & Giuseppe Parigi, 2000. "Forecasting Industrial Production in the Euro Area," Temi di discussione (Economic working papers) 370, Bank of Italy, Economic Research and International Relations Area.
  2. Thierry Roncalli & Gael Riboulet & Ashkan Nikeghbali & Vado Durrleman & Erick Bouy?, 2001. "Copulas: an Open Field for Risk Management," Working Papers wp01-01, Warwick Business School, Finance Group.
  3. Carlo Favero & Iryna Kaminska & Ulf Soderstrom, 2005. "The Predictive Power of the Yield Spread: Further Evidence and a Structural Interpretation," Working Papers 280, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  4. Joshua Rosenberg, 1996. "Pricing Multivariate Contingent Claims Using Estimated Risk-neutral Density Functions," New York University, Leonard N. Stern School Finance Department Working Paper Seires 96-36, New York University, Leonard N. Stern School of Business-.
  5. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  6. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
  7. Granger, Clive W.J. & Terasvirta, Timo & Patton, Andrew J., 2006. "Common factors in conditional distributions for bivariate time series," Journal of Econometrics, Elsevier, vol. 132(1), pages 43-57, May.
  8. Joshua V. Rosenberg, 2003. "Nonparametric pricing of multivariate contingent claims," Staff Reports 162, Federal Reserve Bank of New York.
  9. Bradley, Michael D. & Jansen, Dennis W., 2004. "Forecasting with a nonlinear dynamic model of stock returns and industrial production," International Journal of Forecasting, Elsevier, vol. 20(2), pages 321-342.
  10. Whitney K. Newey & Douglas G. Steigerwald, 1997. "Asymptotic Bias for Quasi-Maximum-Likelihood Estimators in Conditional Heteroskedasticity Models," Econometrica, Econometric Society, vol. 65(3), pages 587-600, May.
  11. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  12. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
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