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

Author

Listed:
  • 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)

Abstract

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

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    Cited by:

    1. Pérez-Rodríguez, Jorge V. & Ledesma-Rodríguez, Francisco & Santana-Gallego, María, 2015. "Testing dependence between GDP and tourism's growth rates," Tourism Management, Elsevier, vol. 48(C), pages 268-282.
    2. Luca, Giovanni De & Guégan, Dominique & Rivieccio, Giorgia, 2019. "Assessing tail risk for nonlinear dependence of MSCI sector indices: A copula three-stage approach," Finance Research Letters, Elsevier, vol. 30(C), pages 327-333.
    3. Liu, Xiaoliang & Xu, Wei & Odening, Martin, 2011. "Can crop yield risk be globally diversified?," SFB 649 Discussion Papers 2011-018, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. 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.

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    More about this item

    Keywords

    Forecasting; Industrial Production; Copulas; VAR models.;
    All these keywords.

    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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