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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.

Suggested Citation

  • Carluccio Bianchi & Alessandro Carta & Dean Fantazzini & Maria Elena De Giuli & Mario A. Maggi, 2009. "A Copula-VAR-X Approach for Industrial Production Modelling and Forecasting," Quaderni di Dipartimento 105, University of Pavia, Department of Economics and Quantitative Methods.
  • Handle: RePEc:pav:wpaper:105

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    References listed on IDEAS

    1. 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.
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    4. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    5. Favero, Carlo A. & Kaminska, Iryna & Söderström, Ulf, 2005. "The Predictive Power of the Yield Spread: Further Evidence and A Structural Interpretation," CEPR Discussion Papers 4910, C.E.P.R. Discussion Papers.
    6. 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.
    7. Giuseppe Parigi & Roberto Golinelli & Giorgio Bodo, 2000. "Forecasting industrial production in the Euro area," Empirical Economics, Springer, vol. 25(4), pages 541-561.
    8. 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.
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    11. Joshua V. Rosenberg, 2003. "Nonparametric pricing of multivariate contingent claims," Staff Reports 162, Federal Reserve Bank of New York.
    12. 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.
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    Cited by:

    1. repec:eee:touman:v:48:y:2015:i:c:p:268-282 is not listed on IDEAS
    2. Rivieccio, Giorgia & De Luca, Giovanni, 2016. "Copula function approaches for the analysis of serial and cross dependence in stock returns," Finance Research Letters, Elsevier, vol. 17(C), pages 55-61.

    More about this item


    Forecasting; Industrial Production; Copulas; VAR models.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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