Weighting schemes in global VAR modelling: a forecasting exercise

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.

• Publication year

  2017

• Venue

  Letters in Spatial and Resource Sciences

• Publication date

  2017-03-01

• Fields of study

  Economics

• Identifiers
  DOI 10.1007/S12076-016-0170-X
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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