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Weighting schemes in global VAR modelling: a forecasting exercise

Author

Listed:
  • Florian Martin

    (Vienna University of Economics and Business (WU))

  • Jesús Crespo Cuaresma

    (Vienna University of Economics and Business (WU)
    Wittgenstein Center for Demography and Global Human Capital (IIASA,VID/OEAW,WU)
    International Institute of Applied Systems Analysis (IIASA)
    Austrian Institute of Economic Research (WIFO))

Abstract

We provide a comprehensive analysis of the out-of-sample predictive accuracy of different global vector autoregressive (GVAR) specifications based on alternative weighting schemes to address global spillovers across countries. In addition to weights based on bilateral trade, we entertain schemes based on different financial variables and geodesic distance. Our results indicate that models based on trade weights, which are standard in the literature, are systematically outperformed in terms of predictive accuracy by other specifications. We find that, while information on financial linkages helps improve the forecasting accuracy of GVAR models, averaging predictions by means of simple predictive likelihood weighting does not appear to systematically lead to lower forecast errors.

Suggested Citation

  • Florian Martin & Jesús Crespo Cuaresma, 2017. "Weighting schemes in global VAR modelling: a forecasting exercise," Letters in Spatial and Resource Sciences, Springer, vol. 10(1), pages 45-56, March.
    • Handle: RePEc:spr:lsprsc:v:10:y:2017:i:1:d:10.1007_s12076-016-0170-x
    DOI: 10.1007/s12076-016-0170-x
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    References listed on IDEAS

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    1. Garratt, Anthony & Lee, Kevin & Pesaran, M. Hashem & Shin, Yongcheol, 2012. "Global and National Macroeconometric Modelling: A Long-Run Structural Approach," OUP Catalogue, Oxford University Press, number 9780199650460, Decembrie.
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    4. Florian Huber & Jesus Crespo-Cuaresma & Martin Feldkircher, 2014. "Forecasting with Bayesian Global Vector Autoregressions," ERSA conference papers ersa14p25, European Regional Science Association.
    5. Kapetanios, George & Labhard, Vincent & Price, Simon, 2006. "Forecasting using predictive likelihood model averaging," Economics Letters, Elsevier, vol. 91(3), pages 373-379, June.
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    8. Jesús Crespo Cuaresma & Martin Feldkircher, 2013. "Spatial Filtering, Model Uncertainty And The Speed Of Income Convergence In Europe," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 720-741, June.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
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    11. Jesús Crespo Cuaresma & Martin Feldkircher & Florian Huber, 2014. "Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors," Working Papers 189, Oesterreichische Nationalbank (Austrian Central Bank).
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    Cited by:

    1. Shigeki Ono, 2020. "Impacts of conventional and unconventional US monetary policies on global financial markets," International Economics and Economic Policy, Springer, vol. 17(1), pages 1-24, February.

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    More about this item

    Keywords

    Global VAR modelling; Forecasting; Global spillovers;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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