Weighting Schemes in Global VAR Modelling: A Forecasting Exercise

Florian Martin, Jesus Crespo Cuaresma

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

11 Downloads (Pure)

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.
OriginalspracheEnglisch
Seiten (von - bis)45 - 56
FachzeitschriftLetters in Spatial and Resource Sciences
Jahrgang10
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - 2017

Zitat